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Systems Biology and the Quest forGeneral PrinciplesA philosophical analysis of methodological strategiesin systems biology

Sara Marie Ehrenreich Green

Project Group of Philosophy of Contemporary Science in Practice

Centre for Science Studies,Department of Physics and AstronomyAarhus University, Denmark.Ny Munkegade 120, Building 15208000 Aarhus C DK

Email: sarag@ivs.au.dk

Systems Biology and the Quest for General Principles

A philosophical analysis of methodological strategies in systems biology

Sara Marie Ehrenreich Green

PhD dissertation presented to theFaculty of Science and Technology of Aarhus University

January 2014

Supervisor: Hanne Andersen, Centre for Science Studies, Department of Physics andAstronomy, Aarhus University

Co supervisor: Sabina Leonelli, Egenis, the Centre for the Study of Life Sciences,Department of Sociology, Philosophy and Anthropology, University of Exeter.

Cite as: Green, S. M. E. (2014). Systems Biology and the Quest for General Principles. Aphilosophical analysis of methodological strategies in systems biology. PhD dissertation.Centre for Science Studies, Department of Physics and Astronomy, Aarhus University.

Preface

I came to this project having studied both philosophy and biology – but as two separate subjects –at the University of Southern Denmark (SDU), Odense. This background reflects my dual interest in understanding the living world as well as how this is studied; an interest I decided to pursue in this current body of work.

The study of philosophy alerted me to the implications of the metaphysical assumptions that underlie different worldviews, including scientific analysis. I became interested in understanding the conditions that focus our attention on some aspects while systematically ignoring others. This interest can, in part, be attributed to my philosophical training at SDU, under the inspirational tutelage of associate professor Jørgen Hass, who instilled in me a love of German and French philosophy. Hass encouraged me to read the works of Foucault, and I became increasingly interested in understanding the ‘trancendental conditions’ that underlie the particular ways we talk about, reason about and interact with the world. In this project I have chosen a quite different framework than a Foucauldian discourse analysis, but my interest in understanding the effects of the methodological and theoretical assumptions that underlie specific practices remains. Foucault focused on ‘transcendental conditions’ that set the historical possibilities for what can constitute knowledge in a given time period. My analysis is much more modest in investigating the implications of different methodological and explanatory strategies in contemporary research practice. But similar to Foucault’s archaeology, my interest lies in the question of how some conditions (in this context research heuristics) can at the same time constitute and limit the possibilities of knowledge generation.

Despite my early philosophical training, when I subsequently decided to follow a biological sciences program my focus stayed on the factual content of the courses. In retrospect this seems naïve, but I was – like most science students – busy absorbing the course contents and carrying out the assignments as laid down in the semester plan. This is probably how we live our life most of the time; our attention is on the world and the problems we have to solve, not the conceptual schemes we view the world through. From a philosophical position this could be considered remiss. Foucault noted, for example, that through the habit of ‘ordering’ things with our concepts and measurements, alternative possibilities often become ‘unthinkable’ for the coded mind. So we are often not aware of the assumptions underlying our (scientific) world view until these are met with resistance. This is analogous to the way that people wearing spectacles in their focus on the world forget wearing eyewear until a speck of dust or condensation interrupts their vision. A philosophical deconstruction is as a way to break habits and free the way for thinking what otherwise appear unthinkable. But this is easier said than done. Basic assumptions are so self-evident to us that our perspective on the order of the world seems to be the only one conceivable. This rather blinkered view means that it is often forgotten that there are many ways to describe or explain phenomena – just like there are many ways a picture may be cut to make a jigsaw. Looking at the same biological system, systems biologists with backgrounds in engineering and molecular biology, respectively, may approach the system from different perspectives and describe the systems in very different ways. It is these differences that

interest me – the historical or conceptual moves that make the same research object appear different and that, through a new way of thinking about a topic, often lead to new insights.

In Hanne Andersen’s research group – Philosophy of Contemporary Science in Practice - I found an incubator in which this interest at the interface of biology and philosophy could be nurtured. Hanne Andersen’s group has a special focus on interdisciplinary collaboration, which gave me a unique chance to study the implications of different methodological frameworks in biology. The PCSP group is also open to different ways of doing philosophy of science. Hanne Andersen guided me towards a practice-oriented research tradition that I have come to greatly appreciate. In this project, I was given the freedom to survey different new research disciplines and pick out those cases and questions that interested me the most. Although I had studied biological sciences, I knew nothing about systems biology when I began this project, but this field quickly caught my interest. Uri Alon’s (2007) textbook “An Introduction to Systems Biology. Design Principles of Biological Circuits’ was my first encounter with systems biology. This is where I discovered my ‘eyewear’, the assumptions that implicitly had guided my view on biological systems and left other aspects blurred. I was struck by the simplicity and generality of the network motifs Alon’s group had discovered and the lack of molecular details in the illustrations. It occurred to me that this framework was a very different way of thinking about biological systems than I had previously learned. This feeling only increased as I met the systems biologist Olaf Wolkenhauer who used an even more abstract approach to understand biological systems. These encounters raised several questions that have puzzled me since. How can a rigid mathematical framework be useful in biological analysis when everything in biology is so complex, contingent and context-dependent? How do the assumptions underlying the abstract approaches relate to those of molecular biology and physiology? What is the heuristic and explanatory virtues of these so-called design principles, and to what extent do these reflect a different perspective on living systems?

Throughout this project, many of the assumptions underlying my own views have been challenged. As a biology student I did not question the text-book description of biological causation as linear pathway sequences, or the metaphor of the genome as a program. Except for a general skepticism of evolutionary psychology, in particular the Bell Curve perspective that became the subject of my bachelor project, my view of organisms was clearly adaptationist. Over the duration of this project my view on many of these things has changed as I have learned more about not only the new branches of the life sciences but also about older systems-theoretical and neo-Rationalistic streams that I had not encountered previously. This project has revealed some oversight with respect to both biology and philosophy and to the ways in which different heuristics have specific affordances at the expense of others. Through perseverance I have addressed some of these blind-spots and extended my interest in constraints on reason to an interest in constraints on biological causation. Even as my project draws to an end I am still fascinated by how these constraints constitute the scope of what is possible. I hope that this thesis illuminates why I find these aspects so fascinating, and that it helps open other scholars’ eyes to alternative views on the philosophy of science and on the study of living systems.

AcknowledgementsKnowledge is in the end based on acknowledgement

-Ludwig Wittgenstein

With Wittgenstein’s quote in mind, I would first and foremost like to thank Hanne Andersen, my primary supervisor, principle investigator of the project group Philosophy of Contemporary Science in Practice (PCSP) and head of the Centre for Science Studies (CSS) at Aarhus University. Hanne made this project possible and gave me the freedom to shape the project after my own interests. After working with Hanne for 3½ years, I do not just see her as an amazing supervisor and a skilled researcher but also as a good friend. Her mentoring has guided me to see the bigger picture, regardless of whether I felt confident in or disappointed by my progress. The importance of this kind of support is incalculable, and it has helped me remain relatively (!) sane. Although I have done my very best to annoy Hanne with many silly questions, I have yet to experience her in a bad mood. Even with the heavy workload that goes with being Centre head, Hanne always has time to care about everyone’s wellbeing. Hanne goes out of her way to encourage good social relations in the Centre, and I shall always remember the dinner parties that Hanne and her husband Carsten held at their home - at this point I should say a special thank you to Carsten for letting me partake in the consumption of some fine specimens from his wine cellar. Finally, I would like to thank Hanne for her professional integrity. My choice of focus on systems biology turned out to be one of very few things that felt outside Hanne’s impressively broad range of expertise. Instead of seeing this as a problem, Hanne encouraged me to seek co-supervision from other researchers. Through Hanne therefore I got to meet many of the leading authorities in my area who contributed greatly to my developing knowledge.

The second half of my project benefitted tremendously from the co-supervision of Sabina Leonelli from Egenis, the Centre for the Study of the Life Sciences, at the University of Exeter. In the winter of 2013, I spent three months as a visiting scholar at Egenis. No one that knows Sabina can avoid being affected by her energetic passion for research and devotion to her projects. She has been a major source of inspiration, philosophically as well as on the personal level, and I am very grateful for the time she has spent guiding my research. Her comments are always spot on, with concrete suggestions for improvement, and evidence of her broad and deep level of expertise. I would also like to thank John Dupré, director of Egenis, for letting me take part in the productive and dynamic research environment at Burne House. The influence of the practice-oriented works of both John and Sabina is apparent in many ways in this project. I would also like to thank the PhD students at Egenis for nice discussions, lunches and pub visits. In particular, I thank Jo Donaghy and Nick Binney for all the mud-trail walks and good fun. The time spent with you made this stay even better.

Likewise, I acknowledge the Department of Philosophy at the University of Sydney, and Maureen O’Malley in particular, for hosting me as a visiting scholar for two months in the spring of 2012. Maureen has helped me improve my work through her reflective and honest critiques balanced with caring support. Maureen sets an outstanding example of a spirited researcher that I admire for her profound expertise, tireless working mentality and for her way of taking scientific practice seriously. Maureen’s sense of humor and interest in facilitating social activities made my stay rich in experiences. In addition to Maureen, I would also like to thank David Braddon Mitchell, Kristie Miller, Paul Griffiths and Karola Stotz for nice barbecues and adventurous ‘bushwalks’ that make me smile when I think of Sydney. Rasmus Grønfeldt Winther, who is now employed at University of California, Santa Cruz, co-supervised part A of my project which was when I did

my course work. I would like to thank Rasmus for his guidance during the initial period of confusion, and for encouraging a pluralistic approach to understanding biological practice that inspires my work to this day. I am also grateful to Claus Emmeche, University of Copenhagen, for useful comments and guidelines in connection to my Part A exam in January 2011.

Especially the last part of my PhD project has been far from a lonesome journey due to interactions with many other researchers, some of who have collaborated with me on papers. I have greatly benefitted from interactions with Olaf Wolkenhauer who is professor in systems biology at the University of Rostock. Olaf’s curiosity and genuine interest in pushing science further through self-critical reflection is something I greatly appreciate and feel inspired by. I would like to thank Olaf for inviting me to visit the research group in Rostock, for stimulating conversations on various topics, and not least for the optimistic and patient attempt to teach me kite surfing even when there was no wind… Another outstanding researcher I feel privileged to have worked with is William Bechtel, UC San Diego. William’s influence on discussions in philosophy of biology needs no explanation, and his views on biological research stand out in many pages of this dissertation. William has supported my work in several ways, and he generously shares his ideas and takes time to discuss philosophical work in an open and constructive way, which is a great benefit for junior scholars. While visiting University of Sydney, I met an outstanding visiting scholar, Arnon Levy, from the Van Leer Institute in Jerusalem. Arnon took the initiative for a joint paper together with Bechtel, and I would like to thank Arnon for the interest he has shown in my work and for all I have learned from him. Melinda Fagan of Rice University, and JohannesJaeger of the Centre for Genomic Regulation (CRG) in Barcelona are the co-authors of the third and final paperthat makes up this dissertation. During the writing process they introduced me to new philosophical topics and I much appreciate their guidance during the writing of this paper. Many other researchers have inspired my work, provided feedback to my ideas, and made participation at various conferences something to look forward to. Among these are in particular Fridolin Gross, Tarja Knuuttila, Giora Hon, Robert Richardson, IngoBrigandt, Annamaria Carusi, Nancy Nersessian, Marta Bertolaso, Miles MacLeod, Brett Calcott, Sandra Mitchel, Sune Holm, Manfred Drack, Henrik Thorén, Veli-Pekka Parkinen, and Pierre-Alain Braillard.

I owe particular thanks (and beers!) to the PSCP group in Aarhus: Mads Goddiksen, Brian Hepburn and Susann Wagenknecht. I have learned a lot from our group meetings on various topics and their feedback on my work has been greatly appreciated. I have enjoyed being part of such a great team. I would also like to thank the participants at the CSS junior researchers’ meetings organized by the PhD coordinator Matthias Heymann for their helpful comments and suggestions, and Samuel Schindler, associate professor at CSS, who kindly offered feedback on my work. My overall PhD experience was greatly facilitated and enhanced by the current and former members of the CSS administrative team - Trine Binderup, Minna Elo, and our librarian Susanne Nørskov. They made me feel welcome from day one and ensured the smooth running of daily activities, as well as organizing many nice social events. Thank you also to Claire Neesham for linguistic assistance and for all the fun we had in Exmouth and London.

My deepest acknowledgements go to my long-suffering partner, Kim Lundgreen, who has been extremely supportive in not only joining me on several work-related trips but also in patiently listening to all kinds of philosophy-stuff. Last, but definitely not least, I gratefully acknowledge the Danish Research Council for Independent Research, Humanities, and Aarhus University, for financing this project, and the Niels Bohr Fondet, AUFF and the ISHPSSB foundation for providing financial support for trips to international conferences and study visits abroad. These trips have contributed greatly to my knowledge and I feel lucky to have had sufficient funding to make the most of so many opportunities and to meet so many exceptional people.

List of content

Abstract (English) Resumé (summary in Danish) Overview of the thesis

Introduction1. Reflections on philosophical methodology 1

1.1. From theoretical to practice-oriented philosophy of science 2 1.1.2. Towards a philosophy of biology 4

1.1.2. Philosophy of science in practice and the ‘messiness’ of science 6 1.2. Using case studies in philosophical analysis 7 1.3. Philosophy as a complementary perspective to science 10 1.4. On the relation between science and philosophy 12

2. Heuristic strategies in systems biology 17

2.1. Analyzing research strategies in systems biology 18 2.2. What is systems biology? 22 2.3. Systems biology – on term, many meanings 25 2.4. Network fever and reverse engineering 28

3. For and against generality in biology 33

3.1. Against generality in philosophy of biology 34 3.2. On the roles of general laws and principles 36 3.3. Natural selection – the true general principle of biology? 39 3.4. The pitfalls of the design approach and the emergence of ESB 41 3.5. Overview of research papers 47

Paper section

4. When one model is not enough: Combining epistemic tools in systems biology 53Green, S. (2013), Studies of History and Philosophy of the Biological and Biomedical Sciences 44:170-180.

5. Tracing Organizing Principles: Learning from the History of Systems Biology 6Green S. & Wolkenhauer, O. (2013), History and Philosophy of the Life Sciences 35: 555-578

6. A philosophical evaluation of adaptationism as a heuristic strategy 9Green, S. Resubmitted to Acta Biotheoretica

7. Design sans Adaptation 11Green, S., Levy, A. & Bechtel, W. In review in European Journal of Philosophy of Science

8. Integration challenges in Evolutionary Systems Biology 139 Green, S. Fagan, M. & Jaeger, J. Draft of a paper to appear in a special issue of Biological Theory

9. Conclusions and new perspectives 167 9.1. Concluding comments on the research papers 167 9.2. Revisiting generality and the role of formalizations 171 9.3. On the regulative role(s) of general principles 179.4. Aiming for completeness: whole-cell models 182 9.5. Systems biology – the future of medicine? 184 9.6. Revisiting the implications of ‘systems-heuristics’ in biology 191 9.7. What do engineering and physics have to do with biology 196 9.7.1. Towards a systems view of evolution? 199

General Bibliography 207

AppendixesA Statement from our PCSP group on philosophy of science in practice, SPSP newsletter 2012 247 B Compressed representation of the E. coli transcriptome decomposed into network motifs 251 C EMBO workshop report on Integration in Science (Green & Wolkenhauer 2012) 253 D FEBS minireview on the challenges for systems medicine (Wolkenhauer & Green 2013) 257

AbstractThis thesis examines the philosophical implications of the intensified quest for so-called designprinciples or organizing principles in systems biology. These signify general relational aspects of system behavior and are often conceptualized in mathematical or engineering terms. Examples are negative feedback control, allometric scaling relations and network motifs. I address the question of whether and how the quest for general principles can be a feasible research strategy in the life sciences, given the complexity and contingency of biological systems. Drawing on a set of case studies in contemporary systems biology and on insights from precursors of systems biology, I argue that the conceptualization of general principles can serve important pragmatic aims. Among these are the categorization of biological functions, articulation of possible relations given a set of general constraints, and transfer of resources across disciplinary boundaries. The thesis throws light on how mathematical abstractions, in combination with other epistemic tools, can help to identify biological mechanisms. Thus, I highlight the role of mathematical models and general principles in the production, rather than reduction, of biological explanations.

In analyzing the role of abstract research strategies, or heuristics, I highlight the enabling and disabling constraints of different epistemic means. Heuristics define as well as limit the problem space of scientific analysis. For instance, an engineering approach to biological systems can focus attention on a set of functional designs that are common in engineered and living systems thereby guide the search for how-possible explanations. But this approach has also been criticized for oversimplifying the organization of living systems and for drawing a misleading parallel between intentional design and selective origin of traits. The search for design principles in biology therefore raises a question of the relation between design thinking and adaptationism. I argue that the problematic aspects of adaptationism go beyond the issue of testability and that the criticism, therefore, has not lost its relevance in modern biology. Meanwhile, I demonstrate how design thinking and adaptationism in some contexts can be dissociated. I conclude by categorizing different roles of general principles in biological research and by clarifying how mathematical abstractions support the identification of biological mechanisms. Even without accurately representing causal relations, general principles in biology can increase the understanding of living systems by highlighting common organizational patterns in different systems that are shaped by general constraints on function and form. This way the different explanatory frames may be mutually supportive rather than conflicting.

The thesis consists of three introductory chapters, five research papers, a concluding chapter and a set of appendixes. The first paper (“When one model is not enough: Combining epistemic tools in systems biology”) analyzes how knowledge is generated in systems biology through the combination of models with different properties. Rather than the traditional focus on the representational relationship between models and target objects, I argue for the importance of understanding the relations between the models combined in practice. I draw on Rheinberger’s historical epistemology to examine a case study where mathematical models guide the identification of so-called network motifs. Network motifs constitute examples of design principles, and the paper concludes with

reflections on the transfer of resources from engineering to biology. The second paper (“Tracing Organizing Principles: Learning from the History of Systems Biology”, with Olaf Wolkenhauer) reflects on the search for design principles in a broader historical context. We draw on insights from proponents of earlier systems-theoretical fields to clarify the motivation behind the search for general principles in contemporary systems biology and to highlight characteristics of different levels of biological analysis.

Design thinking has often been criticized for having adaptationist leanings when applied to the biological domain. The third paper (“A philosophical evaluation of adaptationism as a heuristic strategy”) analyzes the implications of methodological adaptationism. The debate on adaptationismhas so far m been discussed in the context of evolutionary biology and has often centered on empirical and explanatory issues rather than on implications of adaptationism as a heuristic. This paper reexamines Gould and Lewontin’s seminal criticism, in their Spandrels-paper from 1979, in the light of an of how methodological adaptationism is used in functional andevolutionary analysis in zoophysiology and systems biology. I argue that the criticism of adaptationism is still highly relevant but also that the implications of adaptationism differ in functional and evolutionary analys s. Paper four (“Design sans Adaptation”, with Arnon Levy and William Bechtel) examines more closely the relation between adaptationism and design thinking. We argue that design thinking, including the use of optimality assumptions, also has non-adaptationist applications. We define a thin notion of design that is termed in relation to a causallyspecified capacity that does not involve commitments to the origin of this design.

Having analyzed the explanatory implications of general principles in systems biology and theproblematic as well as productive aspects of adaptationism, paper number five (“Integrationchallenges in Evolutionary Systems Biology”, with Melinda Fagan and Johannes Jaeger) examines the attempt to integrate different explanatory frames in the new branch called evolutionarysystems biology. This field aims to facilitate an integration of experimental as well as theoretical approaches to development and evolution though the application of systems biology methods. To examine the challenges associated with the attempt to extend the evolutionarysynthesis, we clarify the basis for disagreements regarding explanatory standards thatcharacterize previous debates between neo-Darwinists and neo-Rationalists. Furthermore we show how similar difference cause tensions between experimental biologists and dynamical systems theorists in modern stem cell research. Drawing on a case study from evolutionary systems biology we argue that these different strategies can be productively combinedand the synergistic effects of this integration. The final chapter of the thesis (Section 9) summarizes and extends the main points of the thesis and reflects on new questionsthat are raised as systems biology expands into systems medicine and evolutionary biology. The implications of abstract approaches, inspired by physics and engineering, are reexamined against this background.

ResuméNærværende afhandling er en filosofisk analyse af den intensiverede søgen efter såkaldte designprincipper eller organiseringsprincipper i systembiologien. Ved disse forstås principper som i matematisk eller design terminologi udtrykker generelle relationer mellem et systems organisering og dets funktioner eller dynamik. Eksempler er negativ feedback kontrol, allometrisk skalering og network motifs. Afhandlingen rejser spørgsmålet om hvorvidt og hvordan en søgen efter generelle principper kan være en produktiv strategi, givet den kompleksitet og kontingens der kendetegner levende systemer. Gennem en analyse af case studier og historiske forløbere for systembiologien argumenterer jeg for at generelle principper kan tjene vigtige pragmatiske formål. Blandt disse er kategorisering af biologiske funktioner, artikulering af mulige biologiske relationer og processer givet generelle biologiske og fysiske rammer, og fremmelse af resourceoverførsel mellem forskellige videnskabelige discipliner. Afhandlingen belyser hvordanmatematiske modeller, i kombination med andre epistemiske værktøjer, kan spille en vigtig rolle i identificeringen af biologiske mekanismer. Altså fremhæver jeg vigtigheden af matematiske modeller og generelle principper i produktionen af, snarere end i reduktionen af, biologiske forklaringer.

I analysen af abstrakte forskningsstrategier i biologien, såkaldte heuristikker, fokuserer jeg på de mulighedsskabende og mulighedsbegrænsende rammer disse sætter. Heuristikker definerer såvel som begrænser rammen for videnskabelig problemløsning ved at skærpe blikket for nogle aspekter og tilsløre andre. Eksempelvis kan en design-inspireret forskningstilgang i biologien guide dannelsen afmulige biologiske forklaringer ved at trække på analogier mellem funktionelle designs i designedeog biologiske systemer. Men designtilgangen er også blevet kritiseret for at drage misvisende paralleler mellem formålsrettet design og naturlig selektion af biologiske træk. Systembiologernes søgen efter designprincipper rejser derfor spørgsmålet om forholdet mellem designtænkning og adaptationisme. Jeg argumenterer for at de problematiske aspekter ved adaptationisme ikke blot handler om hvorvidt evolutionære hypoteser er testbare og at kritikken af adaptationisme stadig er relevant i moderne biologi. Samtidig demonstrerer min analyse hvordan design tænkning og adaptationisme i nogle tilfælde kan adskilles. I afhandlingens konklusion kategoriserer jeg forskellige roller som generelle principper spiller i biologisk forskning og redegør for hvordan abstra te matematiske tilgange kan støtte identificeringen af biologiske mekanismer. Selvom generelle principper ikke muliggør en detaljeret repræsentation af kausale forhold, kan disse øge forståelsen for levende systemer ved at fremhæve organisatoriske mønstre i forskellige systemer som er formet af generelle rammer for form og funktion. På denne måde kan de forskellige forklaringsmæssige rammer stå i et synergistisk forhold.

Afhandlingen består af en introduktion, fem forskningsartikler, en konklusion og fire appendix. Indholdet af artiklerner er som følger. Den første artikel (”When one model is not enough: Combining epistemic tools in systems biology”) analyserer hvordan viden dannes i systembiologien gennem kombinationen af modeller med forskellige egenskaber. Snarere end det traditionelle fokus på forholdet mellem modeller og det, de repræsenterer, fremhæver jeg vigtigheden af at forstå relationerne mellem modellerne selv. Inspireret af Rheinbergers historiske erkendelsesteori

analyserer jeg et case studie fra systembiologi hvor brugene af matematiske modeller er vigtig for opdagelsen af generelle mønstre i biologiske netværk, såkaldte ’network motifs’. Disse udgør eksempler på designprincipper og artiklens konklusion reflekterer over implikationerne ved at overføre re sourcer fra teknologi til biologi. Den anden artikel (”Tracing Organizing Principles: Learning from the History of Systems Biology”, med Olaf Wolkenhauer) sætter dennutidige søgen efter designprincipper i en bredere historisk kontekst. In sigter fra tidligere system-teoretiske tilgange at tydeliggøre motivationen bag den søgen efter generelle principper, som vi ser i moderne systembiologi, og til at karakteristiske egenskaber ved forskellige niveauer af biologisk analyse.

Designtænkning i biologien er ofte blevet kritiseret for at være forbundet med adaptationisme. Den tredje artikel (”A philosophical evaluation of adaptationism as a heuristic strategy”) analyserer implikationerne af metodisk adaptationisme. Debatten om adaptationisme har primært fokuseret på evolutionsbiologien og på empiriske og forklaringsmæssige problemstillinger snarere end metodiske strategier. Denne artikel sammenstiller Gould og Lewontins kritik, i deres berømte Spandrels-artikelfra 1979, med en analyse af brugen af metodisk adaptationisme i zoofysiologi og systembiologi. Jeg hævder at kritikken stadig er relevant men også at implikationerne er forskellige i hhv. funktionel og evolutionær analyse. Artikel fire (”Design sans Adaptation”, med Arnon Levy og William Bechtel) fokuserer yderligere på forholdet mellem adaptationisme og designtænkning. Vi argumenterer for at designtænkning, inklusiv antagelser om optimalt design, også har ikke-adaptationistiske anvendelsesmuligheder. Vi definerer et ’tyndt’ designbegreb som står i relation til en kausal kapacitet der ikke involverer antagelser om et designs historiske baggrund.

Efter at have undersøgt de forklaringsmæssige implikationer af generelle principper i system-biologien, og de problematiske såvel som produktive aspekter af adaptationisme, analyserer artikel fem (”Integration challenges in Evolutionary Systems Biology”, med Melinda Fagan og Johannes Jaeger) forsøget på at integrere forskellige forklaringsmæssige rammer i det nye forskningsfelt kaldet evolutionary systems biology (ESB). Målet for ESB er at facilitere en integration af eksperimentelle såvel som teoretiske tilgange til evolution og udviklingslære gennem brugen af metoder fra systembiologien. For at belyse de udfordringer der er forbundet med målet om en udvidet evolutionær syntese, klarlægger vi årsagen til historiske spændinger mellem neo-Darwinister og neo-Rationalister angående forskellige forklaringsmæssige idealer. Ydermere viser vi hvordan lignende spændinger karakteriserer forholdet mellem eksperimentelle biologer og systemteoretikere i moderne stamcelle-forskning. Gennem en analyse af et case studie fra ESB argumenter vi for at de forskellige strategier kan kombineres på produktiv vis og reflekterer over den forklaringsmæssige synergi som en integrering af disse muliggør. Afhandlingens sidste kapitel (kapitel 9) opsummerer og udvider de centrale pointer i afhandlingen og reflekterer over nye spørgsmål der rejser sig i takt med at systembiologien udvikler sig i nye retninger såsom systemmedicin og ESB. Implikationerne af abstrakte forskningstilgange, inspireret af fysik og teknologi, evalueres på denne baggrund.

Th thesis consists of three introductory chapters (Section 1-3), five research papers (Section 4-8), a concluding chapter (Section 9) and four appendi es. To ease the reading of the thesis I provide a brief overview of the structure below.

Introduction (synopsis)

Section 1 introduces the type of philosophy this thesis exemplifies; philosophy of science in practice. I spell out what this approach is a reaction to, and discuss the implications of the practice-oriented approach. Section 2 outlines the topic of the thesis and clarifies what systems biology is. Section 3 reflect on the relationship between the quest of general principles in systems biology and the widely accepted perception in philosophy of biology that biological systems are too complex and contingent to allow for law-like generalizations.

Paper section

Section 4 is a paper on the interlocking use of a combination of different models in systems biology towards the identification and analysis of an example of so-called design principles, namely Alon’s network motifs. Section 5 traces the current quest for general principles in systems biology back to earlier approaches such asGeneral Systems Theory and Cybernetics and reflects on what can be learned from this historical view aboutthe motivation for this heuristic strategy in contemporary science.Section 6 analyses an often debated research heuristic in evolutionary biology, adaptationism, but focuses on the use of this strategy in the context of systems biology and physiology.Section 7 examines more closely the relation between adaptationism and design thinking as it is used in the context of reverse engineering. The paper examines the question of whether design thinking in general, and optimality modelling in particular, necessarily impl adaptationist leanings.Section 8 examines central integration challenges in evolutionary systems biology towards the ultimate aim of an extended synthesis that integrates the quest for general evolutionary principles, mechanistic explanations and of development and evolution.

Conclusions and new perspectives

Section 9 summarizes and extends the main points of the thesis and reflects on new questions that is raised as systems biology expands into systems medicine and evolutionary biology. The implications of abstract approaches, inspired by physics and engineering, are reexamined on this background.

Appendixes

A Statement from our PCSP group on philosophy of science in practice, SPSP newsletter 2012 B Compressed representation of the E. coli transcriptome decomposed into network motifs C EMBO workshop report on Integration in Science (Green & Wolkenhauer 2012) D FEBS minireview on the challenges for systems medicine (Wolkenhauer & Green 2013)

1. Reflections on philosophical methodology In order to understand how and why systems biologists draw on different research strategies to investigate living systems, it has been necessary for me to investigate how this research is conducted in practice. Accordingly, this project is situated within philosophy of science in practice. Philosophy of science in practice is a relatively new field of interest in philosophy of science. The aim of scholars with an interest in practice is to bring philosophical analyses closer to scientific practice in order to study science as a product of epistemic and social activities that involve experimental, historical, cognitive and theoretical aspects. In the following I shall reflect on questions that arise from studying philosophy in relation to the practice of science and defend the value of this approach.

To understand what philosophy of science in practice is, it is fruitful to understand what it is a reaction to, namely the aims and methods of traditional Anglophone analytical philosophy of science. Whereas the latter type of philosophy aimed to establish formal criteria for relations between scientific theories and for a unified scientific method, philosophy of science in practice aims to critically analyze how science is actually practiced. An important difference between the traditional and practice-oriented philosophy of science is therefore whether, or not, discrepancies between philosophical theories and scientific practice pose a problem for the philosophical accounts.1 While philosophy of science still is engaged in reflections on what counts as scientific and non-scientific practice, the idea of one unifying ‘method of science’ has in the practice-oriented philosophy of science been replaced with an acknowledgement of a plurality of methods of science. Accordingly, the methods of philosophers have also changed from being a purely rationalistic enterprise to heavily draw on detailed case studies and a variety of empirical methods. It is therefore relevant to re-think the role of philosophy of science in relation to science proper and reflect on the methodological assumptions that underpin this new type of philosophy. Is a consequence of the increasing use of case studies that philosophy of science in practice can only be a philosophy of the particular? What is the role of the philosopher, and what types of insights can a philosophical project afford? How can we make sense of the use of case studies in philosophy? And how does the scientific underpinning influence the philosophical analysis through the philosopher’s parallel and often engaged understanding of the same object as the scientists?

The following section introduces what philosophy of science in practice means, from what type of philosophy it differs, and the motivations for developing this kind of approach (Section 1.1-1.2). I then examine the implications of this method, including the role of case studies in philosophy of science (1.3), and the relation between science and philosophy of science (1.4). Drawing on this

1 Although I here write ‘traditional’ it does not mean that this type of philosophy is not practiced today or that highly theoretical philosophical analyses cannot play an important role in contemporary philosophy of science (Schindler 2013). However, if a project in philosophy of science aims to understand how science is practiced, it needs to take this practice seriously and to modify the philosophical accounts according to empirical insights drawn from this practice (Fagan 2011).

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framework I clarify how this specific project is situated between philosophy and science, and what has motivated my use and choice of case studies.

1.1. From theoretical to practice-oriented philosophy of science In the early twentieth century and up to the 1970s and 1980s, analytical philosophy of science was to a large extent a study of logical relationships between scientific theories, understood as bodies of propositions. Philosophy of science concerned the establishment of rational schemes for scientific explanations and their verification (e.g. Carnap 1938/1949, Hempel and Oppenheim 1948, Popper 1959, Hempel 1966,). For many years, philosophers of science accepted a sharp distinction between what Reichenbach called a context of discovery and one of justification, where only the latter was considered a subject of philosophical, i.e. logical, analysis (Reichenbach 1951). Whereas conditions for the empirical evaluation of scientific hypotheses could be rationally analyzed and generalized, the context of discovery was seen as a subjective and contingent psychological matter. In this perspective, scientific discovery was therefore viewed as a preliminary stage of research whereas the justification stage was considered the defining aspect of science (Nickles 1990). As a result, non-propositional dimensions of science were left unanalyzed, including the question of how knowledge was gained through the activities of modeling and experimentation.

In the 1970s and 1980, a few philosophers began to show interest in discovery processes. Some viewed the discovery process as an extension and sophistication of everyday problem-solving activities, following some general ‘rules of thumb’ or heuristics that could be rationally analyzed (Polyá 1948, Simon 1966, 1977). But in contrast to the logical and foundational approach of the traditional philosophers of science, Simon used his famous concept of bounded rationality to emphasize the importance of understanding the limitations of human analytical power and the context of knowledge generation. In this framework human reasoning in general, and scientific reasoning in particular, is a selective search through a problem space (to be further clarified in Section 2). Simon believed these strategies to be rather general and sought inspiration and support in research on artificial intelligence (Langley et al. 1987). While many scholars came to share the view of scientific discovery as problem solving, others were skeptical of the generality of such heuristics. Like the positivists, Nickles contended that there could be no global and topic-neutral deductive logic of discovery (Nickles 1988, 1996). But, following Simon, he argued that a rational analysis of strategies of problem-solving could be conducted if it was accepted that such philosophical accounts had to include an analysis of historical and scientific contexts (Nickles 1990). In the same period, scholars outside the philosophical framework had paid closer attention to the characteristics of scientific theories as a product of human activities and historical and social contexts (e.g. Fleck et al.1935/1979, Kuhn 1962/1996, Foucault 1973). Many philosophers found this literature to overstate the influence of social and historical factors on science, but the positivist ideal became harder and harder to defend as a closer examination of science revealed a much more complex and diverse enterprise (Callebaut 2005). Accordingly, the philosophers’ aims of a formal justification theory and axiomatic schemes of

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theory reduction were attacked for being not only irrelevant to science but also misleading for anyone with the wish to understand how science works or could work.

The turn towards the examination of historical or contemporary case studies to understand, ratherthan rationally define, science is apparent in a series of important publications around 1980. Whereas some developed a ‘postpositivist skepticism’ regarding the possibility of establishing fruitful normative claims about science (Feyerabend 1975), others claimed that philosophy of science could bring about fruitful insights on theory choice by comparing different scientific domains (e.g. Thagard 1988, Giere 1979). As a result of the turn towards practice, the aim and scope of philosophical analysis were re-defined. The narrow focus on the logic of scientific theory and methods was balanced with an acknowledgement of rather diverse scientific practices in an ongoing development of new fields and ‘interfield theories’ that spanned the traditional disciplinary domains (Darden and Maull 1977). Specifically, the context-distinction - demarcating what could be subject of philosophical analysis - has been criticized for being an artificial philosophical construct, shutting off the interesting aspects of science (Giere 1979, 1988, Nickles 1980 ed., 1988, 1990, Mehaus and Nickles 1999, Arabatzis 2009).2 Among topics of increasing philosophical interest were the central role of experiments in knowledge generation (Hacking 1983), the practical constraints and scope of models and explanations in science (Cartwright 1983, 1989, 1999, Bechtel and Richardson 1993), historical and sociological conditions including experimental and instrumental resources (Knorr-Cetina 1981, Rheinberger 1997), and the heterogeneity of scientific practice (Dupré 1993).

In recent years, the dichotomies between historical, social and rational contexts of science have been further challenged as scholars have realized that all of these features are necessary for understanding a given tendency or trajectory of scientific practice (Leonelli 2010). The upshot of this changed perspective is that aspects that do not follow logical-analytical schemes can be included as important parts in a philosophical analysis. In early analytical philosophy we find little interest in the tools and processes of discovery.3 Today, the value of a deeper understanding of the conceptual and cognitive constraints of heuristics employed in scientific research is increasingly appreciated (Bechtel and Richardson 1993/2010, Waters 2004, Nersessian 2008, 2009, Pigliucci and Boudry 2010).4

Furthermore, whereas previously philosophy of science sought to delineate the reference and meaning of scientific terms, modern philosophy of science aims to make sense of how central scientific concepts change through history (Burian et al. 1996, Stotz et al. 2004, Griffiths and Stotz 2013).

2 For recent discussions on the context distinction, see (Schickore and Steinle 2009, eds.). 3 An important exception was Mary Hesse (1963/1966) who analyzed the role of analogies in science. The cognitive linguists Lakoff and Johnson (1980) demonstrated how human language is pervaded by metaphors but these analyses did not until recently receive much attention from philosophers of science (Nersessian 1995, 2008, Keller 2002, Knuuttila forthcoming). Given the importance of heuristics, including analogies and metaphors, in scientific reasoning it is striking that so few textbooks in philosophy of science give any space to this topic. 4 See also Routledge Companion to Philosophy of Science (Psillos and Curd, ed. 2008)

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1.1.1. Towards a philosophy of biology

The philosophical focus on a narrower field or sub-field of science, that also characterizes this project, is another trend of modern philosophy of science that deserves a few comments. Previously, physics had been the prototypical scientific discipline, exemplifying the ideal of ‘hard science’. With the practical turn in philosophy there was a growing interest for disciplines other than physics. This led to heated discussions on the status of biology that differed from physics in many respects. It was, and to some extent still is, debated whether biology could be considered a less mature discipline where biological theories – with time – could be reduced to the principles of physics and chemistry (Schaffner 1993, Dupré 2007a, Weber 2008, Winther 2009). With the more practice-oriented philosophy the status of physics as an ‘ideal’ scientific discipline was shaken by criticism of the received view of laws in physics as well as biology. Cartwright (1983, 1999) demonstrated how the laws of physics are only applicable under idealizing conditions, and Mitchell (2005) rejected the normative requirement of laws to hold with since this would constitute a problemfor many physical laws but also neglect important pragmatic aspects of the formulation of laws (see also Section 9). Furthermore, proponents of the growing group of philosophers of biology forcefully argued that biology did not and should not aim to live up to the same scientific criteria as physics. Rather, philosophers should appreciate the differences between biology and physics and aim to describe how the explanatory strategies differ (Dupré 1981, 2004, Bechtel and Richardson 1993/2010, Burian et al. 1996). Against the picture of physics as a ‘model science’, some scholars pointed out that many sciences do not come to resemble physics as they mature. Instead they may diversify into specialized fields and sub-fields (Dupré 1993, Nickles 1996).5

Many discussions have centered on what a scientific explanation is or should be. The two most influential accounts of scientific explanation were for several years the causal-mechanical account (Salmon 1998) and the unificationist subsumption model of explanation (Friedman 1974, Kitcher 1989).6 Analyses of scientific practice in different fields came to the conclusion that these did not account for the variety of explanatory frames in scientific practices. Instead, new accounts of explanation were defined. The most influential has been the mechanistic account, but the scope of explanatory strategies in biology also includes what have been called topological explanations, dynamic explanations, mathematical explanations, and design explanations (cf. Berger 1998, Machamer et al. 2000, Glennan 2002, 2010, Bechtel 2007, Craver 2007, Braillard 2010, Huneman

5 This thesis is concerned with the reasoning behind the expectation that this diversification is now being counterbalanced by the attempts at unification in systems biology - but importantly for different reasons than reduction of theories or scientific fields. 6 These accounts can be seen as successors of Hempel’s D-N-model and Scriven’s causal account (de Regt 2006). Unificationists keep the argument structures as the focus of scientific explanations, whereas causal accounts emphasize the relation to the explanation of how effects are caused by events. Salmon (1998) hoped to resolve the Hempel-Scriven’s debate by suggesting that these and more developed accounts could be seen as complementary. But whereas Salmon saw these two as sufficient to capture scientific explanation and scientific understanding, contemporary philosophers have expanded the spectrum of strategies to explain and understand phenomena (de Regt et al. 2009).

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2010, Brigandt 2014b in press).7 These different accounts reflect a recognition that scientific explanations in biology not only differ from explanations in physics, but that also explanations withinbiology can vary between the different biological fields (Burian et al. 1996; Winther 2003, 2011). In addition to the realization that different explanatory strategies existed, philosophers of science also recognized that the reduction of scientific understanding to criteria for scientific explanations is a shortsighted view (de Regt 2006, de Regt et al. 2009, Leonelli 2007a). Scientific understanding also covers other aspects of science such as non-propositional and tacit knowledge, analogical reasoning, visualization etc. To cover this multitude of different strategies and tools to understand the world scientifically one must therefore take cognitive, social and historical factors into account in addition to philosophical epistemology. The acknowledgement of these relations between fields has been supported through institutional developments that bridge the gap between history, philosophy and sociology of science.

One of the important steps towards an integrated study of science was the founding of the International Society of History, Philosophy, and Social Studies of Biology (ISHPSSB) in the late 1980s. A ‘new philosophy of biology’ was initiated and supported by a large group of scholars interested in understanding what science is rather than constructing philosophical systems of what science should be. The proponents of this stream shared the aim of biologists to understand the target phenomena, the living world, and the implications of the methods to achieve this aim. The developments within philosophy of biology have resulted in a greater openness towards a fruitful interaction across the boundaries of science and philosophy, but also towards the use of a greater range of empirical methods through the integration of historical and sociological analyses. This closer collaboration between fields is reflected in the institutional label of integrated history and philosophy of science (HPS), methodological integration of ‘history, philosophy and social studies of science and technology’ (HPSSST), and history, philosophy and social studies of biology (HPSSB). The institutions reflect the need for a combination of these perspectives to fully account for scientific developments, e.g. for understanding how ‘big science’ is influenced by both historical, sociological as well as epistemological factors (Leonelli 2007a, 2008, 2010). Thus, the demarcation lines between philosophy of science and other fields associated with the study of science have become less rigid, and the collaboration between these has the potential to provide a nuanced view on what philosophy can and cannot ‘do’ for science. While the attempt to develop a foundational framework of science based on a general ‘scientific method’ has been abandoned, new possibilities have emerged such as intensified collaborations between philosophers and scientists (Section 1.4) and enforced relations to neighboring disciplines such as science education (Goddiksen 2013).

7 I do not provide any analysis of the relation between these types of explanations or the merits of these. However, I stay open to the view that there exist different types of explanations in biology (see Section 3 and 9).

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1.1.2. Philosophy of science in practice and the ‘messiness’ of science

Philosophy of science in practice is a further step in the direction of analyzing science “as it is” rather than “as it should be”. An institutionally important development to strengthen these efforts was the foundation of the Society of Philosophy of Science in Practice (SPSP), an initiative to support the network of philosophers of science engaged with scientific practice and the practical use of scientific knowledge, including “the functioning of science in practical realms of life” (Ankeny et al. 2011). The first conference was held at Twente University (the Netherlands) in 2007, and since then SPSP has grown in size with each biennial conference.8 What philosophy of science in practice is of course changes with the developments in the various scientific and philosophical fields, but there have been a few more general reflections on what constitutes the ‘practice-part’. John Dupré9

recently stressed that there are distinct ways to study science in practice within this framework:

i) Philosophy-of-science in practice, andii) Philosophy of science-in-practice

The former is directly engaged with scientific research through interactions with scientists such as participation in laboratories, interviews, participant observation, and through collaboration with scientists on philosophical papers etc. The latter interpretation, philosophy of science-in-practice, is an analysis of how science-in-practice works. This task covers issues associated with the scientific communities and their political and scientific aims, methods and epistemic strategies employed within these communities, and the social structures of institutions. This notion may also be widened to capture philosophical analyses of the way science is taught and (mal)practiced, where philosophical insights potentially can be helpful for decision-making in situations related to research policy issues and to education of future scientists. The philosophical analysis may not only address other philosophers of science or scientists, but can the equally important aim to increase the understanding of the complex relations between science and society for policy makers and citizens. In summary, the duality in the notion of philosophy of science in practice reflects a broadened scope of ways to do philosophy of science, i.e. the use of a wider methodological toolkit, but also a broader scope of the aspects of science that are relevant for philosophical analysis. Different analyses may emphasize these aspects differently but many projects seem to imply both at the same time; a philosophy-of-science-in-practice. In summary, philosophers of science in practice are not only engaged with well-established scientific theories but also ongoing science investigated on an empirical basis. This implies the difficulty of understanding developments that are still ‘in the making’. But what can be gained in return is the possibility of hands-on experiences through observation or interactions with scientists.

8 The initiative for SPSP was taken by Rachel Ankeny, Mieke Boon, Marcel Boumans, Hasok Chang and Henk de Regt among others. See http://www.philosophy-science-practice.org/ for more information, accesses 10-01-2014. 9 Plenary talk at the SPSP 2011 in Exeter. See comments from our group in the SPSP Newsletter Autumn 2012 (Appendix A).

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In the present PhD project I have examined how research in systems biology works through case studies and interactions with scientists. The use of case studies in philosophical analysis raises a number of important issues. Many historians, sociologists, and philosophers of science come to the conclusion that science is more ‘messy’ than textbooks and popular science material give us reasons to believe. This ‘messiness’ provides important constraints on abstract generalizations.10 An anecdote may serve to clarify a characteristic of philosophy of science in practice. At the EPSA meeting in Athens in 2011, Dr. Raphael Scholl cited the ancient Greek poet Archilocus for saying: “The fox knows many things but the hedgehog knows one big thing”. The citation has been used by Isaiah Berlin in the book The Fox and the Hedgehog to illustrate two different categories of thinkers; hedgehogs, who view the world through the lens of a single defining idea (e.g. Plato), and foxes who draw on a variety of experiences and focus on the heterogeneity of the world (e.g. Aristotle). Following this analogy, Scholl claimed that the epistemology of philosophy of science in the twentieth century has been a playground for top-down hedgehogs attempting to define science as one thing with one method. But developments in both science and philosophy of science have forced philosophers to give up the idea of one ‘big’ and all-compassing philosophy of science for a broader perspective and awareness of contextual differences. As a consequence, philosophers have become less likely to generalize beyond their case studies. This raises a question of the scope and foundation of philosophical accounts. In the following section I shall argue that the practical turn does not imply that philosophy of science in practice can only be a philosophy of the particular. But it does imply that empirical findings can lead to a revision of philosophical ideas.

1.2. Using case studies in philosophical analysis

A general and important question for philosophy of science in practice, which so far has been discussed mainly in relation to the context of integrated history and philosophy of science (HPS), is the role case studies play in philosophical conclusions about scientific practice. Can such philosophical analysis address questions that exceed the limits of the case studies? And if so, (how) can the choice of case studies be justified? I here mention two contemporary accounts that have inspired my own view on the use of case studies in philosophical analysis. There are others, of course, but they played a lesser part in the choice of methodology for this thesis.

In the HPS community, two issues regarding the use of case studies have gained special attention, namely generalizations from case studies and the issue of descriptive and normative ideals. Pitt (2001) described a dilemma for philosophers who want to use a single historical case study as the starting point for drawing general (normative) philosophical insights. For Pitt, case studies had limitations if used to test general philosophical claims about scientific practice because the choice and interpretation of the cases will always be biased by the philosopher’s aim. Drawing inductive

10 The description, ‘messiness of science’, was used by Lisa Osbeck in a reflection on the empirical aspect of philosophy of science at a workshop on “Empirical Philosophy of Science – Qualitative Methods”, organized by Hanne Andersen and Susann Wagenknecht, March 21-23, 2012 at Sandbjerg, Denmark.

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inferences from case studies, even if based on a broad range of case studies, always runs the risk of generalizing from a biased sample of cases that do not represent the domain that the general philosophical claim addresses. Is there a way to avoid this problem? Burian (2001) contends that Pitt sets up a false dilemma that only arises because the use of case studies is viewed through a Popperian scheme where philosophical claims should be tested against case studies, and inferences to the best theory made, based on the applicability of the general theory (see e.g. Donovan et al. 1988 eds.). Burian admits that since the very choice of samples is often shaped by the epistemological claim to be tested, the case studies are indeed untrustworthy as tests of the epistemological claim. Burian thus concedes that successful application of philosophical ideas to a few concrete cases tells us nothing about the generality of these abstractions. However, he argues that to lower the philosophical ambitions regarding the scope of the accounts should not be seen as an embarrassment for philosophy of science but as a consequence of the diversity, complexity and continued development of science. Instead, we must accept and acknowledge that “methodologically and epistemologically useful case studies need not be philosophically innocent and need not proceed to grand conclusions by induction from absurdly small samples” (Burian 2001, 388). The shift of perspective involves an appreciation of the particular; of the value of context-dependent findings. Burian’s solution is thus to give up the idea that inferences from case studies yield universal epistemologies. Instead we should settle with the task of improving our knowledge in the context of “real” situations, and for that purpose case studies are seen as the best source (see also Nersessian 1995). As an example of what can be gained from case studies, Burian mentions the research mode “exploratory experimentation” that was not a category found in standard top-down philosophy of science, despite the popularity of this approach in scientific practice (Burian 2001, 2007, O'Malley 2007, Waters 2007). ne could

draw an analogy between this approach and studying scientific practice in a more ‘exploratory fashion’, from a more open theoretical framework.

Burian’s appreciation of context-dependent findings has received support in the practice-oriented philosophical community. Nevertheless, his argument that philosophy of science is best done ‘bottom-up’ can be problematized. Chang (2012) has argued that we need to revise the dimension of inferences drawn from case studies if we want to understand the relation between abstract and contextual analyses. Chang’s starting point is to dismiss the inductive view of the use of case studies in history and philosophy of science. The next step is to reject the distinction between philosophical and historical analysis that is based on a description of philosophy of science as a normative and general analysis, in contrast to history of science that is conceived as a descriptive analysis of the particular. Instead, Chang views it as more instructive to think of the relation between history and philosophy of science as a difference in degree between the concrete and the abstract. Furthermore, he claims that it is not possible to analyse the concrete without abstract ideas with normative content.

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In other words, the purely descriptive ideal is a misnomer for history as well as for philosophy of science, although normativity can of course come in degrees.11

To illustrate the change of perspective that includes a graded difference between the concrete and the abstract, Chang suggests that case studies can viewed as being analogous to episodes in a TV series. The episode is seen as a “concrete instantiation of the general concepts (the characters, the setting, the type of events to be expected, etc.), and each episode also contributes to the articulation of the general concepts” (Chang 2012, 111). Chang thereby replaces the linear perspective on inferences drawn from case studies with a corrective iterative spiral. By focusing, as Chang suggests, on the interactions of the concrete and the abstract, instead of that between induction and deduction, the process of using case studies in HPS research becomes clearer. Chang uses the notion of epistemiciterativity to denote the self-correcting evolution of theories from the integration of independent measurements. This process can be imagined as a helix rather than a circle, where successive states of knowledge are created, revised and refined, in science as well as in the context of HPS (Chang 2004, 2012). The notion of iterativity has affinities with what Thagard and Nersessian calls reflective equilibrium, a term borrowed from Rawl’s who used it in the context of ethics and applied it a consideration of the relation between inferential principles and practice in philosophy of science (Thagard 1988, Nersessian 1995). What is important is that inference is not a one-way direction but a reflective and circular movement: “The goal is to bring historical and cognitive interpretations into a state of reflective equilibrium, so as to make the circularity inherent in the approach virtuous rather than vicious” (Nersessian 1995, 196). Nersessian states that although certain key assumptions must guide every analysis, these assumptions will often be subject to critical scrutiny if discrepancies in or with the empirical material arise.

In summary, the idea of a corrective circularity can be used here to understand how philosophical accounts are strengthened by the integration of abstract and concrete analyses.12 Empirical inputs are both an opportunity to learn about science in the making, and to get a productive “dialectical resistance” from the source material, which can serve to revise, sharpen and improve the abstract philosophical accounts (Burian 2001). But it requires effort to listen carefully to these sources and discover the resistance. In Kuhn’s words; “The historian’s problem is not simply that the facts do not speak for themselves but that, unlike the scientist’s data, they speak exceedingly softly” (Kuhn 1980, 183, quoted in Chang 2012, 110). If this is true for history, it is no less true for a philosophy, which aims to articulate the blind spots of the scientific practice and to go beyond a description that just mirrors the scientists’ own reflections. This brings us to a discussion on the relation between philosophy and science.

11 This seems to resonate with Burian’s account that views the descriptive ideal of modern philosophy of science as an “overreaction” to the criticism of normativity (Buran et al. 1996). Chang also admits several connections between Burian’s and his own account (Chang 2012, 111), and I see no major differences between these accounts. 12 And more generally to integrate historical and philosophical accounts (Chang 2012, 122). This is however a discussion I cannot include here.

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1.3. Philosophy as a complementary perspective to science

The ‘fox-like’ philosophy of science in practice is a philosophy drawing on many means to gain an increased understanding of scientific practice. In philosophical projects concerned with knowledge generation there is a need to understand science in part from the “inside”. In other words, the philosopher needs to understand the methodological strategies in the context of the current state of knowledge and level of experimental techniques in the particular field. As a result, philosophical accounts of this type are often based in part on written scientific publications, the work of other philosophers of science, and though the use of empirical methods. An increasingly common way to make the philosophical analysis empirically informed is to interact directly with scientists. These interactions are often facilitated by workshops where both scientists and philosophers participate, and in the writing of joint publications.13,14 The advantage is that philosophy of science has become more scientifically informed. But it has also become more pressing to discuss methodological issues and the role(s) of philosophy in relation to science. In the following I focus on these interactions and on the question of what role philosophy of science could play for scientists.

Generally speaking I do not believe that the value of philosophy of science necessarily should be judged on its usefulness for scientists. An equally legitimate aim is to increase the understanding of scientific practice for readers outside the realm of science, whether these are other philosophers, lay people or policy makers. But since philosophy of science currently receives attention from practicing systems biologists, and since parts of this thesis is written in collaboration with scientists, it raises the question of what role(s) philosophy might play in such collaborations. What is the relation between philosophy of science and science proper? (How) can philosophical analyses be useful for scientists? My view on the relation between science and philosophy of science is inspired by Chang’s suggestion that history and philosophy of science should be seen as complementary science (Chang 2004). Chang’s view is that HPS can generate knowledge outside the usual scientific domains. HPS has a role in the inherent tension of scientific practice between what Kuhn described as the necessity of some fundamental norms and assumptions of normal science, and Popper’s warning of the resulting closed-mindedness of science. Thus, on one hand scientists cannot afford to be completely open; they need guiding assumptions to constrain the problem space of scientific analysis. On the other hand,

13 Examples of such interdisciplinary workshops that I have participated in are: i) “Philosophy of Systems Biology”, Aarhus University, July 2012, ii) “Integration in Science. What it is, what it means and what philosophers and scientists have to say about it”, Sydney University, May 2012; iii) “Understanding Evolvability and Robustness”, University of Exeter, February 2013; iv) “Systems Biology Skills X change”, University of Exeter, March 2013; v) “Philosophers meet biologists. Experimental Studies of Population Phenomena”, Konstanz Universität, May 2013, vi) “Expert Workshop on Evolutionary Systems Biology”, Konrad Lorenz Institute, Vienna, September 2013. Moreover, scientists in the life sciences increasingly give talks in sessions at conferences on philosophy of science, participate in philosophical meetings and invite philosophers to take part in their activities.

14 As examples the following works ould be mentioned: O’Malley and Soyer (2012), Orzack and Sober (1994a, 1994b, 2001), Knuuttila and Loettgers (2011, 2013), Boogerd et al. (2007, eds.), Boogerd et al. (2013), and Carusi et al. (2012). Section 6, 8 and Appendix C and D of this dissertation are written in collaboration with systems biologists (Olaf Wolkenhauer and Johannes Jaeger).

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the scientists risk becoming prisoners of their own assumptions as these shut off other possibilities - thereby impeding progress in science (Levins and Lewontin 1985). HPS can serve as a complementary approach by recovering neglected questions, previous insights, and by reflecting on these assumptions. In doing so, HPS can deepen the pool of knowledge by conceptualizing current challenges and future directions of scientific fields, as well as by challenging the authoritative claims of science with respect to issues of societal influences (Chang 2004). The latter is an important contribution because HPS has more freedom than the specific scientific fields to pursue an open inquiry for the benefit of general society, which may result in a critical reflection on scientific practice and its practical implications for society.

Thus, HPS is complementary to science in the sense that it deals with questions that are not normally addressed as a part of science, either because these have been forgotten, because they cannot be addressed within the narrow scope of specialized sciences, or because their answer is presupposed in the way the specific fields operate and therefore only can be addressed from a perspective outside this field. History and philosophy of science bring about an openness of what can be regarded as relevant for science and must therefore remain partly independent of science. But in what sense is HPS complementary science? Chang’s account may be seen as controversial since philosophy since Newton has been distinguished from science as a reflective rather than constructive practice, and since philosophical questions often are defined as ‘unsolvable questions’. If science is defined as the norms and activities that constitute science as we know it, then it appears to be misleading to call historical and philosophical analyses complementary science. The constructive skepticism inherent in the question of what makes something a scientific statement is in a sense a question situated outside the realm of science. Thus, in this sense it is the non-scientific nature of philosophy that enables the critical aspect of philosophical analysis. But Chang’s notion should be understood exactly as this – as a reflective HPS counterpart to science that, qua this virtue, can be constructive. Chang’s intention is not to make HPS a part of science, but to acknowledge the productivity of reflection. He proposes that a constructive skepticism can enhance the quality, if not the quantity, of knowledge (Chang 2004, 243). He therefore opposes the previous philosophical neglect of practice as well as the ideal of descriptive neutrality. Thus, HPS, or philosophy of science in practice for that matter, is a normative enterprise, but the ideals of normativity are based on the science in practice, not a priory philosophical constructs. What remains a challenge for both history and philosophy of science is to keep a balance between the interest in science and the critical reflection of science; i.e. between an acceptance of scientific norms for the purpose of understanding scientific practice and the tendency to rehearse current scientific orthodoxy. By accepting the complementarity of the approaches they become at the same time interrelated and independent. On one hand, the philosophical analysis must be open for revision if empirical findings suggest this is necessary, but on the other hand, this does not mean that the ultimate aim is to bring the analysis completely into line with the scientists’ own descriptions (Waters 2004). Philosophical research may be valuable for other purposes even when it is not necessary or even fruitful for scientific achievements. In the following section I provide further

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reflections on the relation between science and philosophy, starting with my own reasons for engaging with scientists.

1.4. On the relation between science and philosophy

In this project, my aim has been to study how some of the common research strategies work in practice. The starting point was to read various research papers, review articles, research statements etc. of different groups of systems biologists to get a feel for the field, and then to focus on a few case studies (for the choice of cases see below). An initial examination of my chosen cases led me to realize that some of my questions could not be answered by reading the research papers alone. Among these were questions about the different stages of analysis, in what order different models were combined, and in particular questions about the technical parts of mathematical and computational modeling that are hard to access without mathematical training.

Early on in my PhD I was lucky to meet systems biologists that were interested in philosophical issues. Why scientists should be interested in philosophy is however not self-evident. Historically, scientists have often viewed philosophical work as being of little or no practical use for improving their current projects (O’Malley and Dupré 2005). Yet, O’Malley and Dupré argue that this conception is changing as philosophers become more interested in understanding science and as scientists realize the need for new methodological frameworks to meet todays’ ‘grand challenges’. As Callebaut (2005) notes, the interest for philosophy might be greater in areas such as systems biology than, say, genomics and molecular biology. This resonates with my experiences. The reason for this difference is probably that systems (theoretical) biology to some extent is also a philosophical project: a theoretical reorientation in biology involving conceptual and methodological shifts (to be clarified in Section 2). Olaf Wolkenhauer (2012, 3)15 expresses the value of collaborations between philosophers and systems biologists in the following way: “[B]ecause of the inevitable uncertainty of knowledge (interpret data, construct models etc.), every ontological question carries with it an epistemological one. In other words, not only should we ask how biological systems function but we must also consider the process by which we generate knowledge. This is one reason why interactions between the life sciences and the philosophy of science are valuable”. Wolkenhauer’s statement is a defense of the relevance of philosophy of science for scientists but at the same time the quote also exemplifies how many philosophical questions are dealt with by scientists themselves. Many important philosophical contributions are written by biologists or by scholars with a scientific background, on topics such as engineering approaches to biology, modeling and adaptationism (e.g. Jacob 1977, Gould 1996, Gould & Lewontin 1979, Levins and Lewontin 1985, Lewontin 1979, Sterelny 2007). Given that scientists know their practice better than philosophers, this may – and perhaps should – lead us to reflect on not only whether philosophy of science has anything to offer scientists but also whether the practice-turn has made scientific training more important than

15 Professor in systems biology at University of Rostock and the co-author of Section 6 and Appendix C and D.

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philosophical training for doing philosophy of science. In other words, what role does philosophical training play in philosophy of science?

I think there is no particular role in philosophy of science that only a trained philosopher can occupy. But this view does not imply that there are no tasks in which philosophers may excel. In fact the philosopher’s lack of specialization in the discipline, together with the experience of dealing with abstract epistemological and conceptual issues may be of particular benefit to scientific enquiry. This apparent contradiction should be understood from the ambiguous aim of philosophy of science to provide a detailed understanding of scientific practice but also a critical and distancedreflection. As phrased by philosophers in the continental tradition, inspired by Heidegger’s terminology, a de-construction of belief systems can reveal the instabilities that knowledge is built on and articulate the boundaries of possibilities given the conceptual or methodological framework the analyses are embedded in (Canguilhem 1983/2005, Rheinberger 2005). If and when philosophy of science is useful for science it is thus in a very different sense than envisioned by traditional philosophers of science. It is not a matter of building a foundation for science or of telling scientists which scientific method they should employ. Rather, the role of the philosopher is often to facilitate reflections by questioning what can seem self-evident in the scientific practice because it is tightly embedded in the very methodology of that field. Many issues can only be questioned by stepping outside the epistemic framework a concrete methodological approach requires. This is not to say that scientists cannot do the same, but it might be easier for a philosopher situated at a greater distance from this field. The reflective distance may also make thephilosopher particularly well situated for communicating scientific issues to both lay people, scientists from other disciplines, and policy makers. Furthermore, it is important to emphasize that many philosophy of science projects go beyond the scope of any scientific field by considering the relation between science and society. Since philosophy of science in practice neither is a purely scientific or purely philosophical enterprise, there is no reason why scientists or philosophers should have a privileged position for writing or teaching philosophy of science. To know what the important issues are, the philosopher of science (whether scientist or philosopher) often needs a foot in both camps (Leonelli 2007a).

The perspective of a philosopher of science in practice is therefore one of shuttling back and forth between the bird’s eye perspective and the researcher’s perspective. Although scientists may do the same, philosophers often have greater freedom to do this, in terms of funding, time to read scientific literature of a broader scope and not least a social and epistemic freedom to provide a critical and comparative analysis of different scientific approaches. With time, this can give the engaged philosopher of science a profound insight into a broader scientific area. The value of scientific and philosophical review papers is often underestimated. Philosophers of science in practice, ideally in collaboration with practicing scientists, may have an advantage in writing such papers because trends in science are often visible only from a broad perspective. Furthermore, philosophical texts that

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include a review perspective often do much more than describe a research practice, e.g. they may discuss the sociological implications of different methodologies in the context of the broader society (Barnes and Dupré 2008, Richardson 2007, O'Malley 2012). On the more local basis, a philosophical perspective can help clarify philosophically influenced disagreements in science, such as those regarding standards for scientific inquiry, metaphysical assumptions, or views on other scientific groups. These are all issues that can cause tensions in interdisciplinary collaborations (Eigenbrode et al. 2007, O’Rourke and Crowley 2012). In the latter context, the philosophers may take a role akin to a consultant that facilitates the conceptualization of different standpoints.This view does not imply that science needs philosophy but rather that interactions amongscientists and philosophers can be of mutual benefit. Whereas philosophers can contribute with a theoretical and conceptual framework to discussions, and question what may seem self-evident in scientific practice, contributions from scientists in such collaborations are of key value for a deeper understanding of scientific practice and of the highly specialized techniques used.

So far I have talked about philosophy of science in practice in general terms. In this last sub-section I reflect on the choice of case studies in the research papers of this dissertation. My aim has been to conceptualize and analyze different research strategies and explanatory standards in systems biology. For this purpose I have chosen to focus on a few case studies that exemplify different heuristics. The aim has not been to provide a description that covers most of the research in systems biology but to understand the implication of the search for general principles through engineering and systems theoretical approaches, respectively, and to discuss the implications of adaptationist and non-adaptationist strategies. I have given special priority to Alon’s research on network motifs because this research has gained much attention in systems biology and because the case nicely illustrates the shift away from context-sensitive molecular details and towards general design principles. The case is not taken to represent systems biology as such, but exemplifies a research strategy focused on formal analysis of biological networks (to be clarified in Section 2). At the same time, this case will be used to illustrate the combination of models with different constraints (Section 4) and the problematic relations between design thinking and adaptationism (Section 6). As a case for comparison, Section 6 also draw on an example from zoophysiological research on sperm whales. This case exemplify MA used on a quite different level than research on molecular networks but is, like the other case, situated outside the framework of evolutionary biology.16

A discussion with Arnon Levy and Willliam Bechtel on the relation between design thinking and adaptationism in this paper led to the writing of a joint paper on the more general relation between these two heuristics (Section 7). In the philosophical literature design thinking, and reverse engineering in particular, are often defined as an adaptationist reasoning strategy. Discussing this

16 The reason for choosing this specific case was that I had a unique chance in the summer 2011 to follow the field work of whale researchers and to talk to the proponents of different theories about the function of the spermaceti organ (see Section 6 for clarification).

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relation we however came to question this tight connection, and we accordingly examined a set of cases for comparison where the connection to adaptationism seemed to differ. We deliberately chose two closely connected cases regarding mathematical modeling of features of the same system, the intestinal crypt, that both draw on design thinking. Against these similarities we demonstrate how the cases differ in their relation to adaptationism. Importantly, the cases are not used to back up a claim about a general dissociation of design thinking and adaptationism, but rather to demonstrate that these in some cases can be disentangled. Furthermore, we draw on a third case that illustrate how a mathematical conception of biological robustness, like assumptions about optimality, can serve as a constraint on the problem space for possible mechanisms to investigate experimentally.

The design approach however only exemplify one approach to general principles. Together with Olaf Wolkenhauer, I explore the historical trajectories of quest for general principles within the more general systems-theoretical stream dating back to early neo-Rationalists and General Systems Theory (Section 5). This paper is not based on in-depth study of a few cases but rather seeks to spell out tendencies in several examples regarding the motivation behind this research strategy. I have chosen to include this paper as Section 5 (before I discuss the relation between design thinking and adaptationism) because it situates the design approach in a broader historical context. The last paper (Section 8) combines the issues of general and specific explanations and the issue of adaptationism.The paper is the result of a collaboration with Johannes Jaeger and Melinda Fagan and is based on resonant themes in our presentation at a workshop on evolutionary systems biology at the KLI, Vienna. Fagan’s research on stem cell biology had revealed deep tensions between DS theorists and experimental biologists due to differences in explanatory standards, whereas I had argued for the possibility of reconciling the quest for general principles and detailed mechanistic explanations. Since Jaeger’s group attempts to combine these strategies in order to integrate insights on development and evolution, I suggested that we should join forces and examine the explanatory strategies in further detail. Because Jaeger’s work aims to reconcile the study of evolution and development and different levels of explanation, the last paper relates to the issue of general principles (Section 4 and 5) and to the discussion on adaptationism in (Section 6 and 7).

Writing this thesis has been a matter of shuffling between the philosophical literature and case material. Although the case material has been constitutive for the formulation of philosophical accounts, I see the status of the cases as examples of the more abstract points. The conclusions I drawreflect a philosophical view regarding the affordances and limitations of the strategies studied. I have attempted to stay true to the cases studied while also approaching these from a critical perspective. With the current project I hope to provide insights that are fruitful for understanding the research practice of systems biology, whether the readers are philosophers, scientists or readers who have a different interest in the research practice of systems biology. The following two sections provide some background knowledge on the topic of this thesis and clarify the motivations behindthe different subtopics of the research papers. Section 2 clarifies what I mean by heuristic strategies,

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introduces the essentials of systems biology and illustrates the search for so-called design principles. Section 3 puts the quest for general principles in systems biology in a larger context of recent practice-oriented philosophical literature that does not seem to leave much space for generality in the life sciences. One of the main questions in this thesis is therefore to clarify the relation between the search for general principles and the characteristic context-dependency associated with explanations in biology.

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2. Heuristic strategies in systems biology General principles and laws have, as explained in the previous section, not occupied much space in modern philosophy of biology. The covering law model of explanations turned out to be ill-suited for capturing the ways in which biologists identify and explain biological systems. But recent years have witnessed an increased embedding of mathematics in certain disciplines within the life sciences, which has led to a growing interest in the definition of general principles (e.g. Alon 2007, Westerhoff et al. 2009, Huang et al. 2011a, 2011b, Wolkenhauer et al. 2012). This thesis focuses on the quest for so-called organizing principles or design principles in systems biology. Such principles hint at general features of the functional organization of biological systems, such as scale-free topologies of biological networks, hierarchical modularity or network motifs (all examples to be clarified below). I examine what motivates this search for general principles, how this research is conducted in practice and reflect on the implications of this strategy in relation to the aim of formulating detailed causal explanations.

The quest for general principles in systems biology calls for a reexamination of heuristics and explanatory strategies and in biological research. Does the quest for general principles in systems biology mean a return to the deductive explanatory strategies that were abandoned by philosophers of biology? Or is it a misguided strategy that can be expected to be short-lived due to the complexity of biological systems? To address these questions I analyze a set of case studies in systems biology. I focus in particular on abstract strategies that aim to identify design principles. I argue that the quest for these principles can be interpreted as a response to the increasing fragmentation and specialization of the life sciences, where general and quantitatively articulated functions have traditionally been sparse. The pluralism of different specializations in different fields and sub-fields in the life sciences is an inevitable response to biological complexity, but at the same time this plurality of research strategies has in itself become a source of complexity. To make biological and biomedical research applicable for solving grand challenges in society, such as multilevel diseases and environmental problems, there is an increasing need to integrate efforts towards shared goals.1 Systems biology aims to reintroduce the systems perspective in biology but with an understanding of the dynamics of molecular components. The integrative effort is not only a matter of putting together the existing pieces of the puzzle. It is also a matter of exploring new directions in biology through the application of methodological toolkits from engineering, physics, computer science and mathematics, in combination with biological experimentation in a bid to interpret the vast amount of biological information that has become available recently.

Integration is the name of the game in systems biology, but integration is pursued by different strategies, and there are therefore different conceptions of what constitutes a ‘systems view’ of biological phenomena. One strategy is to build large-scale models that not only encompass a large system of interaction but also a high resolution of these interactions. This strategy, at the time of writing, is being pursued in connection to a move from current systems biology and biomedical

1 See also Green and Wolkenhauer 2012, attached as Appendix C.

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research towards an integrated field called personalized medicine where the goal is to develop digital models of individual patients. The results of the first whole-cell model have already been published, and human organs, the heart and liver, are currently simulated in advanced computer models. But building human systeome models is a challenge just as overwhelming as it is exciting. Among the concerns with this approach are the increasing complexity of the models themselves and the heterogeneity of the units that must be integrated in these modes. Instead of relying on detailed models, other systems biologists therefore seek integration through abstraction. This approach is motivated by the robustness or resilience of biological functions to changes at the level of molecular mechanisms. The high degree of change-invariance is seen by many systems biologists as an indication that higher level dynamics, and the conditions under which these change, could be understood without first knowing all the molecular details involved in their production. Instead of focusing on relations of change in specific molecular processes, the aim is for this approach to understand the general principles that characterize systems dynamics such as switching between stable states or sustained oscillations. In engineering, such functions are often supported by designs principles that are independent of specific contexts of implementation. Similarly, many systems biologists hope that biological counterparts to design principles can be identified and generalized across different biological systems. This thesis focuses on such attempts to articulate abstract biological principles that are largely context-independent. I analyze the heuristic value of the associated cognitive and representational/computational strategies that often have their origin in non-biological disciplines. In summary, I examine whether and how such abstractions can guide the generation of biological knowledge, given that biological systems are complex and highly diverse due to the historical contingency of organismal evolution.

The structure of this chapter is as follows. I first clarify the motivation for analyzing heuristic strategies in systems biology (Section 2.1). Section 2.2 introduces what systems biology is and briefly outlines the historical factors that contributed to the emergence of systems biology that is not one unified field but a set of different research strategies. Section 2.3 clarifies two different streams in systems biology, a pragmatic stream and a systems-theoretic (top-down) stream. I shall concentrate on the latter as this best highlights the quest for general principles as a research strategy. To exemplify the quest for such principles, Section 2.4 introduces the search for design principles using the graph-theoretical framework of network modelling. Section 3 will reflect on these strategies in a philosophical context, leading up to the more specific discussions of the research papers.

2.1. Analyzing research strategies in systems biology One of the fascinating features of science is that new knowledge is generated despite ignorance of many relevant aspects of target systems and despite the lack of accessibility to many aspects of the subject of study. Simon famously coined the term bounded rationality to denote how decision making situations – in science as well as everyday life – are constrained by limitations in the information available, cognitive and technical skills, and time (Simon 1966, Simon et al. 1981, 2008 eds.). From this perspective, scientific discovery is perceived as a problem-solving activity. The task of scientific problem solving should not be evaluated in relation to objective standards but to criteria for satisfaction given the information and epistemic tools available. Although knowledge generation is

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not a strictly logical enterprise where derivation from foundational principles can be conducted, there exist common strategies that can be rationally analyzed. Following this framework, Wimsatt (2007b) distinguishes between perfect and pragmatic rationality. The former refers to the explanatory ideals of logical positivists, whereas pragmatic rationality is always relative to specific states of knowledge and specific research aims. This thesis is concerned with pragmatic rationality, i.e. with strategies for problem solving when the research subject is too complex to be addressed by a random trial-and-error strategy and when there is no obvious and objective way to approach the system.

Scientific reasoning, according to Simon, can be understood as constrained search through a problemspace that is characterized not only by the research object studied but also by available knowledge, accessible data, and choices regarding cognitive, computational, and representational strategies. If the search space is vast, researchers can draw on strategies of discovery to narrow down the set of possible solutions and relevant paths to explore, and thereby make scientific investigations manageable and more efficient (Simon 1966, Simon et al. 1992, Wimsatt 2007b). Such strategies that constrain the problem space are often referred to as heuristics or ‘rules of thumb’ (Polyá 1948, Langley et al. 1987). Whereas a few strategies might be general, most strategies are domain and problem-specific (Nickles 1990). For instance, a general strategy for human problem solving is to approach a complex problem by solving a similar simpler problem, often facilitated by the use of analogies and metaphors (Hesse 1963/1966, Ricoeur 1973, Lakoff and Johnsson 1980). But to what the system of study can be productively compared to is dependent on the research context.

The use of heuristic strategies is indispensable in science, because the complexity of the systems studied typically exceeds our limited cognitive and technical abilities (Wimsatt 2007b). In biology the problem space is often vast, poorly defined and often changes throughout the analysis as newknowledge about the system is gained. Processes in biological systems are difficult to access for analysis, and biological researchers must typically explore and combine a variety of modeling strategies and experimental techniques to make traces of such processes visual in the lab. In this process it is often far from obvious what is fact and what is artifact (Rheinberger 1997). Biologists must constantly pay attention to the effects of the operational constraints of the experimental system and of the representational constraints of the model framework chosen (Bechtel and Richardson 1993/2010). Furthermore, because all processes in living organisms cannot be measured simultaneously, difficult choices on the aspects to include must be taken. What makes biological systems so complex is not only the number of interacting components, but also the variety of cross-level non-linear interactions that operate simultaneously and in orchestrated cycles through various regulative processes (Hooker 2011, ed.). Biological systems are different from many physical or chemical systems in that they, insofar as they are living, are far-from-equilibrium and self-maintaining through regulation of internal processes and interaction with the environment (Kaplan & Bechtel 2011). The property of self-maintenance through organized complexity and change across hierarchical levels make causal analysis of biological systems, or parts of these, extremely difficult (Weaver 1948, Wolkenhauer and Muir 2011). Due to practical limitations, researchers often have to isolate sub-components or assume that a specific system is closed in order to experiment on it, although one of the most important features of biological systems is that they are never closed. In

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summary, a large part of scientific research therefore regards evaluation of the implications of methodological choices on what aspects to study and what aspects to ignore. Inspired by the French philosophy of science tradition, Rheinberger (1997) has conducted a historical analysis of the affordances of experimental strategies in molecular biology. This analysis has greatly inspired my work, but I have emphasized that there are many important techniques in science that are not material. In the current research practice of systems biology, knowledge generation is to a high extent guided by the transfer of mathematical techniques, conceptual schemes from other disciplines and through analogical reasoning. I argue that the role of these techniques can also be understood through their methodological constraints imposed on an abstract problem space.

Despite the complexity-challenge, life scientists do manage to generate robust explanations through the combination of cognitive, representational, computational, and experimental strategies. The underlying puzzles that motivate the current project is how this is possible, how this is done in practice, and – more specifically – what role the quest for general principles play in the attempt of understanding living systems. I have aimed to clarify how knowledge generation is possible becauseof as well as despite of the constraints imposed by methodological strategies. I refer to the dual ability to productively constrain a problem space and to limit the vision of scientific views as the enablingand disabling constraints of epistemic tools (see also Knuuttila 2011). The conceptual framework of constraints draws on the concrete counterpart of causal constraints on biological or physical processes (to be discussed in Section 8 and 9). For instance, the vertebrate skeleton provides an enabling constraint for movement on land but a disabling constraint for the flexibility of the body (Hooker 2011a, 28). Similarly, the choice of heuristic strategies or specific epistemic tools make some aspects open for analysis while other aspects are left out of the analysis. To get an idea of the relevance of this conceptual framework for understanding the use of heuristics in biological research, I shall briefly outline some often-discussed implications of the design approach to organisms.

Comparison of organisms to machines or computers is a highly powerful heuristic for focusing attention on specific causal capacities of the systems that are of benefit to the organisms. Analogies between designed systems and organisms have a strong visual impact, and biologists often benefit from the ready-made design language to conceptualize biological functions. If more is known about the design space for specific functional capacities in engineering than about the mechanism underlying a functional capacity in biology, analogical reasoning can enable a transfer of information from the well-known engineering domain to produce productive guesses about the workings of a biological system. As a more general, but closely related, research heuristic in biology, Bechtel and Richardson (1993/2010) highlight the strategies of decomposition and localization in the identification of biological mechanisms. These strategies imply the provisional assumption that biological systems can be subdivided into localizable operations of interrelated parts in modules from which the workings of larger (sub-) systems can be recomposed, similar to the workings of a machine. The result of a successful use of this heuristic, a mechanistic explanation, shows how interacting and hierarchically organized parts causally produce phenomena. Thus, rather than explaining a system by derivation of a law, this type of explanation refers to the organization and operation of parts. Many biological systems have been successfully explained using this strategy, that also affords a transfer of

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decomposition criteria from well-known engineered systems to the largely unknown biological systems. But the productivity of the strategy comes at the expense of an inherent risk to neglect aspects of systems that cannot be localized to independent operations. The disabling constraints describe the side-effects of the systematic bias that generates blind spots in investigations. Decomposition strategies are particularly productive if biological systems are hierarchically structured into functional modules, but may face limitations if living systems are not decomposable or even near-decomposable (Simon 1962, 1977, Marom 2010).2

Importantly, analyzing the merits of heuristic strategies is not just a matter of analyzing the merits of their guiding assumptions. As several scholars have pointed out, epistemic tools can lead to true theories even if they are false (Levins 1966, Wimsatt 1987, 2007a, 2007b, Wolkenhauer and Ullah 2007). In contrast to scientific hypotheses, heuristic strategies are not empirical claims about the world (that can be judged to be true or false), but guidelines on how a complex problem is best approached. Heuristics are tentative discovery strategies that do not imply any substantial commitment to the guiding assumptions of the heuristic framework. For instance, it can be fruitful to approach organisms as if they were designed, even if a given biological system defies a complete decomposition. When engineering-inspired hypotheses about biological mechanisms are met with resistance, this strategy may lead to important insights into the differences between organisms and artifacts (Knuuttila and Loettgers 2013). Thus, researchers can learn from the relation between positive and negative analogies, i.e. the dialectic relation between similarity and difference (Hesse 1963/1966, Ricoeur 1973, Knuuttila forthcoming).

In summary, heuristics can be thought of as productive, but tentative and fallible, guidelines for what features are relevant in scientific inquiry. But if heuristics cannot be judged upon their truth value, (how) can we then evaluate the implications of heuristics? Heuristics are difficult to analyze because they operate at the border of the known and the yet unknown, but different types of heuristics can be analyzed according to their systematic biases towards simpler problem spaces (Wimsatt 2007b). Heuristics tend to bias the analysis in characteristic directions and thereby to leave some aspects of the problem space unexplored. Identifying the ‘blind spots’ of commonly used heuristics can help to increase the awareness of the productivity and limitations of the tools. A better understanding of the implications of different heuristics is therefore not only of philosophical but also of scientific interest. Heuristics are often only indirectly apparent in scientific practice because they, as self-evident and implicit guidelines, are the very basis on which reflections on results are made. It may require philosophical reflection or criticism from other scientific approaches to make heuristics visible as methodological frames that could have been different. This realization is a philosophically interesting aspect of many interdisciplinary collaborations where researchers are confronted with different and sometimes conflicting heuristics and explanatory standards. This is one reason why systems biology, with its perhaps unprecedented level of interdisciplinarity, is well suited for studying different research strategies in the life sciences. In the following sections I introduce systems biology as a

2 While some design approaches draw on a machine analogy where the heuristic rel on strategies odecomposition, other design approaches may instead rely on an analogy to software engineering (see Section 9 and Calcott 2014 forthcoming). The relation between design thinking and adaptationism will be analyzed in Section 6 and 7.

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highly diverse interdisciplinary approach. I focus in particular on the heuristic strategies associated with an abstract engineering approach that raises the question of the role of general principles in biological research.

2.2. What is systems biology?

This is a question with no simple or short answer. Systems biology has been described in terms as different as a ‘holistic worldview’, a ‘paradigm shift’ or even a ‘revolution’ in biology to being merely seen as a ‘buzzword’ used for funding purposes. I believe all these descriptions are incorrect in the sense that systems biology neither radically shifts the perspective of biological analysis, nor is systems biology merely a buzzword. The emergence of systems biology marks a new situation in biological research where integration and interpretation of unprecedented amounts of information is becoming a reality through new experimental techniques advanced mathematical and computational modeling. This affords new perspectives on biological systems or rather; it affordsthe exploration of earlier ideas that could not be empirically explored until now. Systems biologists draw on various tools from other disciplines such as physics, engineering, computerscience, and mathematics, but also draw on the insights and methodologies of earlier approaches. Westerhoff and Hofmeyr (2005), two practitioners of systems biology, therefore reach the conclusion that systems biology is old and new at the same time. While many of the visions can betraced back to earlier theoretical approaches and to classical physiology, the increasing mathematicaland computational embedding of biology on a big scale is without comparison in previous biological research.Whereas the systems view from the perspective of physiology is nothing new, what is new is the merging of a systems perspective with insights and methods from molecular biology and omics disciplines. As a general characterization one can say that systems biology combines ‘old’ modeling tools and new computational methods to interpret an unprecedented amount of experimental data from high-throughput technologies.3 The emergence of systems biology shows that technology is not just a tool in science but can also be constitutive for the development of new fields and for definition of a new set of research questions (O’Malley and Soyer 2012). But the shift towards an emphasis on systems properties also has a more theoretical component, namely the criticism of methodologies centered on molecular properties of isolated components (Moreno 2007, Moreno et al. 2011). Tracing the historical trajectories of systems biology in detail is beyond the scope of this thesis but some background knowledge on the most recent developments can help to better understand the attention that systems biology currently receives.

The term systems biology was used already in 1968 by Mesarovi to denote the use of systems theory to understand biological systems (Mesarovi 1968). Attempts to develop a systems view in biology data much further back, e.g. to Claude Bernard’s classical physiology in the nineteenth century the, and to Bertalanffy and Weiss’ work in the 30s and 50s respectively. But when the term is used in the

3 The ‘older’ modeling tools stem from systems theory, systems engineering, non-equilibrium thermodynamics, biochemical systems theory (BST), Mathematical General Systems Theory, and metabolic control analysis (MSA). For an overview of these methods and for information on the production and interpretation of high-throughput data in array and hybridization experiments, see (Klipp et al. 2009, Alberghina and Westerhoff, 2005 eds.).

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context of contemporary science it typically refers to a much more recent approach. The ‘new’ systems biology was initiated in the late 1990s as a response to new experimental techniques and fast computers that made rapid sequencing of DNA and automated measurements of regulatory interactions possible. This development afforded new exciting possibilities in the life sciences, but also unforeseen challenges. The rapid production of data from new high-throughput technologies is often described via dramatic metaphors such as a ‘flood of data’, ‘data deluge’, or a ‘tsunami of data’ (Gross 2013). These metaphors illustrate the inability of modeling tools in biology to keep up with the production of new data. Systems biology has become the name of the effort to meet this challenge by application of mathematical and computational tools better suited for handling the vast amount of quantitative data. Through these modelling efforts, systems biologists hope to extend the scope and practice of biology by emphasizing the importance of a quantitative understanding of system-level dynamics.

The year 2000 is often described as a ‘decisive year’ for the development of systems biology (Mendoza 2009). The goal of the The Human Genome Project, to sequence the whole human genome, was about to be reached4, and the first university departments of systems biology were established in Seattle and Tokyo (led by Leroy Hood and Hiroaki Kitano, respectively). The same year the first international conference took place in Tokyo, and a project to create a common language for programming was initiated by John Doyle from Caltech (called Systems Biology Mark-up Language).5 Since then, systems biology has undergone a fast development with a rapid increase in systems biology institutions, conferences, and journals focused on systems biology research. The fast development of systems biology is however only partly explainable by the emergence of new technological possibilities. Equally important is the growing need for a methodological and theoretical framework for understanding dynamic and organizational aspects of biological systems. The Human Genome Project was a great achievement in terms of technological innovation, and the project was completed much sooner than expected. But the reason for the rapid completion was not only due to technological advances. Instead of the expected estimate of 100,000 genes, the human genome was found to consist of only approximately 20,000-25,000. In comparison, baker’s yeast (Saccharomyces cerevisiae) has about 6,000 genes, a roundworm (Caenohabditis elegans) has around 19,000 coding sequences, and mice (Mus musculus) and the weed Aradopsis thaliana have about the same number of genes as humans.6 Thus, DNA sequences provide a considerably small part of the required information to understand the processes leading to the observed variety in phenotypes. Phrased differently, this means that regulation of genes and protein products plays a much larger role than expected.

4 The project was initiated in 1990 and an almost complete sequencing was done around 2001 (the project was finalized in 2004). 5 The purpose of SBML is to serve as a machine-readable lingua franca that enables communication and translation between software programs. 6 Estimates obtained from http://www.nature.com/scitable/knowledge/library/comparative-genomics-13239404,accessed 08-12-2013.

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Unlike genomics that focuses on static genetic information in terms of sequences of base pairs, functional genomics includes dynamic aspects of gene regulation and protein-protein interactions.7

As the first omics technology, genomics was a key step towards rapid data-collection and quantitative measures of whole systems. But data do not speak for themselves, and traditional modes in molecular biology and biochemistry were not suited for handling dynamic aspects of large-scale regulatory interactions. As new types of datasets became available, the need for new initiatives to store, integrate and interpret these through databases and mathematical models has increased (Wanner et al. 2005, Leonelli 2008, 2010, 2012a, 2013a). One of the hallmarks of systems biology is therefore what Bentele and Eils (2005) call a ‘quantitative turn’, i.e. the increased application of quantitative methods to investigate dynamic patterns from a large amount of data that can be directly downloaded from databases.8 The idea of a quantitative turn does not imply that other biological fields are not concerned with quantitative measures. What is referred to here is a shift of focus from qualitative aspects of mechanisms, e.g. protein-protein interactions in specific molecular pathways or the effects of a sub-set of genes, to large-scale modeling that involves genome-wide datasets on transcriptional regulation, gene expression levels and data on protein-protein interactions. This shift of perspective means that different aspects of biological systems take center stage in research. Whereas research in molecular biology typically aims to provide data for a more detailed understanding of concrete biological systems, many systems biologists (with scientific backgrounds in engineering, mathematics, physics etc.) are focused on the identification of features that, in general, make a system able to conduct specific capacities. For instance, systems biologists have been particularly interested in robustness of biological networks because this feature is an instantiation of a solution to a common design problem in engineering (Gross 2013). This interest has inspired a search for the more abstract features of biological organization that realize these functions, independent from the molecular context. This characterization of the difference between molecular biology and systems biology is however a truth with modifications. As we shall see below, the characterization of molecular biology from systems biologists is not unproblematic. Furthermore, systems biologists have different views on the relation between systems biology and molecular biology, signifying that different groups of systems biologists pursue different epistemic aims. The relation to molecular biology can therefore be used to clarify how distinct groups of systems biology pursue different heuristic strategies.

Proponents of systems biology often characterize the emergence of their field as a maturation of molecular biology. Systems biologists commonly describe molecular biology as a strategy that can be used to decompose biological systems into their component parts and to study these in isolation, whereas systems biology attempts to put the pieces together using advanced models in order to study their interactions (Van Regenmortel 2004, Alberghina and Westerhoff 2005, Noble 2006). Systems biologists also often contrast their field to molecular biology by highlighting the ‘holistic’ aspects of

7 Functional genomics includes proteomics and metabolomics. With the emerging shift from systems biology to systems medicine there has been a further expansion of these into personalized genomics, personalized metabolomics, and endocrinomics towards clinical phenotyping.8 Examples of such databases are RegulonDB (http://www.ccg.unam.mx/en/projects/collado/regulondb),http://www.geneontology.org/, www.bioontology.org, http://www.arabidopsis.org/. www.yeastgenome.org,www.flybase.org and www.wormbase.org. Some databases are dedicated to a single model organism whereas others aim for identification of cross-species generalizations.

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systems biology. This description is partly justified by the increasing focus on modeling tools that can handle a larger number of interchangeable variables (Kummer and Olsen 2005, Sauer 2005). Yet, one should be cautious not to overstate the difference between systems biology and molecular biology or to uncritically accept systems biologists’ description of state of the art in molecular biology. First, studying parts in great detail, even in isolation, has often lead to important insights. The criticism of molecular biology by systems biologists sometimes underplay the importance of results from molecular biology because not all biological questions require quantification (Gross 2003). Even for those that do, quantitative measures of complexity cannot stand alone (Emmeche 1997). Focusing on dynamical systems may be anti-reductionist in its focus on the quantitative complexity of wholesystems. But quantification is on the other hand a reduction of the richness and diversity of biological entities and properties, and a full understanding often requires both perspectives (Kaplan and Bechtel 2011). To this point it should also be mentioned that the increased size of the systems analyzed so farhas often come at the expense of a lower resolution of data (Krohs 2010, 2012). Second, molecular biologists are often well aware of the limitations of their approaches but see their contributions as important pieces to a larger puzzle that also requires other approaches (De Backer et al. 2010). Third, rather than overcoming reductionist approaches, many projects in systems biology have continued or even extended the focus on molecular pathways or networks (Mesarovi et al. 2005, Wolkenhauer and Green 2013). The promotion of systems biology as a holistic alternative is therefore often lacking self-critical reflection. Importantly, this criticism of the presentation of systems biology as a holistic alternative is not only held by molecular biologists and philosophers but also by systems biologists who are still waiting for the promised shift of perspective. These systems biologists criticize molecular biology but do not argue that systems biology (yet) represents a solution to these limitations. Thus, there are different views on how well systems biology has met its ambitions during the last decade (Calvert and Fujimura 2011). In the following section I clarify how some of the disagreements on how to characterize systems biology stem from the existence of different streams in systems biology.

2.3. Systems biology – one term, many meanings

Proponents of systems biology characterize their field in considerably different ways, and an examination of research practice shows a variety of methodological and explanatory strategies, from detailed large-scale modeling of molecular pathways to highly abstract mathematical analysis. Rather than a unified research approach, systems biology can be characterized as an umbrella term that covers the interpretation of biological data with tools from various fields such as computer science, physics, engineering and mathematics. The different ways of combining and integrating tools and theoretical frameworks from these fields makes a philosophical analysis of systems biology a challenging project. But the diversity of approaches within systems biology is also a source for important philosophical insights, because the application of methodologies from various disciplines represent different epistemic strategies to how living systems may be approached. These differences sometimes result in tensions in systems biology. It is therefore of scientific as well as philosophical interest to understand more specifically the prospects and challenges of different research strategies.

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Whereas some view systems biology as a successor of genomics (Ideker et al. 2001, Aderem 2005), others have emphasized the relation to systems theory or classical physiology (Wolkenhauer and Mesarovi 2005, Noble 2008). The latter proponents see the emergence of systems biology as a revival of systems theoretical ideas that for half a century were neglected by the experimentally oriented biological research community and have only recently been revisited as the need for advanced mathematical modeling arose. For systems-theoretically oriented researchers, the important prospects of systems biology are not only focused on increased computational power to interpret big datasets but foremost on conceptual and theoretical developments that may change the way biologists think about living systems (Drack and Wolkenhauer 2011, Wolkenhauer et al. 2012). The different views on systems biology are also associated with disagreements on the most promising research modes in systems biology.

To make sense of these conflicting views on systems biology, some philosophical distinctions are fruitful. O’Malley and Dupré (2005) distinguish between a pragmatic and a systems-theoreticapproach. These have different historical roots and put different emphasis on the notion of ‘system’. The former refers to an extension and further development of genomics and molecular biology, and is in the scientific community typically referred to as molecular systems biology (de Backer et al. 2010). In contrast, the latter builds on a systems-theoretical framework that abstracts from many molecular details. Krohs and Callebaut (2007) expand this perspective to identify the following three roots of systems biology; 1) biological cybernetics and systems theory, 2) classical molecular biology, and 3) omics-disciplines. They argue that omic-disciplines should be seen as a third root due to the richness of the data that is not found in any of the two other roots, and because the production of data is central for understanding the emergence of systems biology. Krohs and Callebaut describe systems biology as a merger of modeling strategies from data-poor fields with data from fields that are data-rich, but largely deficient in explanatory modeling. Whereas biological cybernetics and systems theory have been poor in terms of structural data, the opposite is true for omics disciplines. But the omics disciplines in turn lack a theoretical framework to make sense of the data. Systems biology can therefore be characterized as strategies to bridge these gaps. The combination of data- and modeling sources can be divided into bottom-up or top-down strategies (Figure 2A). These distinctions not only capture the direction of analysis - from structural data to functional hypotheses or top-down decomposition - but also different views on the level of details needed for biological explanations and the importance of general descriptions.

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Figure 2A. Krohs and Callebaut’s (2007) representation of the structure of systems biology. Horizontal arrows indicate links between the historical roots of systems biology to bottom-up and top-down approaches in contemporary systems biology. Vertical arrows indicate interactions between the roots and between bottom-up and top-down strategies.

The thickness of arrows in the figure indicate the strength of links between the root disciplines and bottom-up and top-down systems biology. The dotted lines indicate that bottom-up and top-down strategies are often combined and intertwined in middle-out approaches (Noble 2006), and thereby that the distinction between these is not a sharp one. Nonetheless, the simplified distinction is useful for clarifying different strategies for dealing with complexity. The goal of bottom-up approaches is to develop a detailed understanding of biological systems through simulations that integrate as many details as possible for as big a system as possible, whereas top-down approaches aim to identify appropriate abstract principles that distill and clarify general patterns in the datasets (Kitano 2002b). In the following I focus mainly on top-down approaches but from a different perspective than Krohs and Callebaut. In describing top-down systems biology, Krohs and Callebaut (2007) focus mainly on data-intensive top-down strategies that aim for ‘unbiased decomposition’ of biological networks,relying on software tools for pattern detection. In contrast, they describe functional (cybernetic) decomposition as a “less favored strategy” in systems biology (Krohs and Callebaut 2007, 199). In recent years a design approach, explicitly focused on functional comparisons between living and artificial systems, has however become widespread. I therefore find that there is a need tophilosophically analyze the implications of design-inspired reverse engineering projects, as well as to examine systems theoretical approaches that are not combined with data-rich fields (Section 3 and 5). Below I introduce the network view associated with current reverse engineering methodologies.

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2.4. Network fever and reverse engineering

Top-down approaches can be viewed as strategies of abstraction designed to provide ‘short-cuts’ to biological generalizations without the need for a full understanding of all molecular properties from the bottom up. Reverse engineering methodologies can in the context of systems biology be generally understood as the use of mathematical and computational strategies to uncover the design principlesbehind specific topological and functional features of biological systems.9 This strategy is not only a matter of simplifying the task of identifying mechanisms but also a complementary search for higher-level patterns of organization that are not visible at the level of molecular details. Such patterns are for instance the topological features of networks that influence the robustness of the network againstchanges in its component parts. The network view in systems biology draws on the representationaland computational framework of graph theory, a field in mathematics, where patterns of connectivity are represented as interactions (edges) among parts (nodes) in a network. Such networks typically lack detailed information on the biochemical and molecular interactions depicted. Instead, graph-theoretical representations afford identification of the overall connectivity of a large set of interactions by visualizing a large set of connections and by easing the computational tractability.

Figure 2B. The difference between an exponential Erdös-Rényi network and a scale-free network. Whereas the former is largely homogeneous, the latter has a power law distribution where some nodes have many links (see text for details). The nodes with the higher number of genes are marked with red, and their neighboring nodes with green. Source: Albert et al. (2000).

Interestingly, many real-world networks show a non-random connectivity distribution called small-world organization with a high clustering of neighboring nodes and a few long-distance connections, making the average shortest path length between two nodes small (Watts and Strogatz 1998). Furthermore, the number of connections is not distributed equally among nodes in most real-world

9 This definition differs from the use of the concept in the literature on adaptationism. To connect with the literature on adaptationism as well as the use of the term in research practice, the notion of reverse engineering will be used in slightly different ways in Section 6 and 7. The difference will be clarified in the two papers.

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networks as in random graph models (or exponential models, Fig. 2B above). Instead, the connectivity pattern exhibits a power law distribution with a long tail, indicating that most nodes have only few links whereas some nodes – called hubs – have a large number of connections (Barabási and Albert 1999, Barabási 2003). Power law distributions are scale invariant, i.e. the connectivity distribution does not change with the size of the network.10

Examples of scale-free networks are the World-Wide Web and social networks. It was recently discovered that biological networks, such as protein-protein interactions and metabolic networks, also share these topological features (Jeong et al. 2000, Lee et al. 2002, Wang and Chen 2003). These similarities suggest that common dynamic principles may underlie the functioning of biological networks. A scale-free distribution does in itself not provide much information on the capacities of concrete networks, but scale-free networks have characteristic features that may provide insight intosome network properties. Scale-free networks have been associated with a high error-tolerance against random failures in nodes and edges. On the other hand, scale-free networks are more sensitive than exponential networks to breakdown of central hubs. Scale-free networks in biological systems are described as ultra-small, and the path length between two pairs of metabolites in metabolic networks is only three to four reactions – a feature shared among regulatory networks in simple multicellular organisms (Barabási and Oltvai 2004). This means that local perturbations can reach nodes in large networks very quickly. Furthermore, scale-free topology and functional modularity of many biological networks seem to be integrated in a hierarchical topology with highly clustered subgroups of nodes.

The network approach is based on the idea that some insights can be inferred from a rather abstract analysis of network topology, regardless of the details of these interactions. How much information such analyses generate is currently a hotly contested issue (Arita 2004, Prill et al. 2010). For instance, it has been questioned to what extent biological networks are scale-free because sub-sets of the networks often do not have power law distributions, and the gene regulatory networks of some organisms seem to display a mixture of exponential and scale-free characteristics (Barabási and Oltvai 2004, Keller 2005). On a more fundamental level, some researchers have questioned the biological relevance of studies of connection architectures of gene networks due to the plasticity of biological networks, and dependence on specific parameter values for regulatory interactions (Ingram et al. 2006, Knabe et al. 2008). However, it is clear that biological networks have a connectivity distribution that is far from random, and that many biological networks are largely robust due to a (functionally) modular and hierarchical structure (Steinacher and Soyer 2012). Many systems biologists therefore explore the characteristics of the non-random features and hope that more specific analyses of the network properties, drawing on data with a higher resolution of the intensity and temporal aspects of the links, can reveal the central mechanisms behind the functioning of biological systems.

10 Expressed formally, this means that scaling the network with a constant will affect the proportionate scaling for the whole function, so that the degree distribution (of k links) approximates P(k)~ k-r where r is the degree exponent. Thus, the network is scale invariant if you zoom in or out.

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Figure 2C. Network representing the transcriptional regulatory network of E. coli. Nodes represent genes and edges regulatory connections between these. Note the existence of a small number of hubs with many connections while most nodes have only a few connections. Source: Salgado et al. (2006).

Pattern identification in real-world networks has been a major research topic in many fields because network methods, being highly insensitive to particular implementations, can be applied to the study of systems as different as social groupings and ecosystems (Loscalzo and Barabási 2011). But due to the access to large data-sets of metabolic and regulatory interactions in systems biology, the network view is particularly enforced in systems biology (Margolin et al. 2006, Knight and Pinney 2009, Palsson 2011). Even for simple organisms, regulatory networks can be overwhelmingly complex, as the network in Figure 2C illustrates. Organizing the data as graph-theoretical networks allows biologists to use mathematical models as search tools for the identification of non-random patterns. That is, mathematical models can guide biological reasoning when no intuitive functional or structural decomposition is available.

Mathematical models that guide the decomposition of biological networks are of course not assumption free. They may for instance draw on assumptions about similarities between engineered and biological networks. A brief description of one of the main case studies in this thesis can serve to illustrate this point, namely Uri Alon and co-workers’ topological decomposition of transcriptional regulatory networks into overabundant structures called network motifs (Alon 2007a). The starting point for the group was a large dataset on transcriptional regulatory interactions in E. coli, i.e. data on the position and targets of transcription factors that regulate genes. Because of the high number of interactions, the search for patterns of organization in this dataset can be a challenging task, and a

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functional decomposition is often not possible. A productive ‘first bet’ can be to assume that the biological systems are organized in the simplest possible way, akin to how an engineer would design an information network.

In electronic networks, sub-circuit designs that perform characteristic functions are often reused throughout the network. Alon drew on this structural criterion to explore whether similar recurring patterns with biologically meaningful functions could be identified in complex biological networks (Alon, personal communication). As a starting point, the data set was modeled as a directed graph representing the regulatory connections among different genes (like in Figure 2C) to investigate whether similar small regulatory patterns could be found in biological networks. Using mathematical algorithms they scanned the biological network for recurring sub-units and compared this result to randomized networks.11 They found a strikingly small set of abundant patterns, called network motifs.Further analysis showed that different motifs had characteristic functions that could be interpreted and analyzed in different biological contexts (Milo et al. 2002, Shen-Orr et al. 2002). Examples ofmotifs are pictured in Figure 2D below.

Figure 2D. Examples of network motifs (modified from Shen-Orr et al. 2002). For further details see Section 4.

This research has received much attention in systems biology, not least because the results provided optimism for the possibility of functionally decomposing complex networks to simple ‘building blocks’, i.e. network motifs with easily interpretable functions that could be dissociated from the rest of the network (Alon 2003, 2007b, 2007c). Moreover, strikingly few of the logically possible network motifs were found to be overabundant. For instance, the coherent feed-forward loop (examined in detail in Section 4) was the only statistically significant motif among 13 possible variants. For four-node motifs, only two of 199 possibilities are found in biological networks (Alon 2007a). This result therefore gives reason to believe that biological systems are not as complex as once imagined. Because of the overabundance of a small set of network motifs, the researchers could decompose a large regulatory network into a very compact representation of network motif distributions. This representation is attached as Appendix B and can be compared to Figure 2C to get an idea of the difference between the complex and the decomposed network based on network motifs.

To summarize, a characteristic aim of research in systems biology is to uncover simple organizational principles behind apparently complex biological structures and functions, and to use mathematical models to ‘reverse engineer’ generalizable biological mechanisms where an intuitive way to identify

11 The status of these randomized networks has been the topic of a heated debate in systems biology ( Section 6).

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these is lacking (Winther 2012, Gross 2013). Alon (2007a) describes his approach as ‘reverse engineering of an unknown network’, and argues that engineering approaches can be efficient research tools for uncovering the simple dynamic principles behind seemingly complex biological systems (Alon 2003, 2007c). This structural view provides a relatively new way to explore biological problem-spaces. Reverse engineering signifies the systematic search for design principles that relate to different types of function but are independent of systems-specific contexts. To represent design principles, engineers use different types of diagrams with symbols indicating specific sub-functions of the systems such as gates, switches and amplifiers. These representational tools indicate that reverse engineering draws on abstract formalizations rather than a bottom-up analysis of all the component parts. Similarly, reverse engineering projects in systems biology aim for generalizable insights into the organization of biological systems (Savageau 2001, Tegner et al. 2003, Tomlin and Axelrod 2005, Velazquez 2009, Salvado 2011, Poyatos 2012).

Some of the principles in engineering and biology are strikingly similar and the same cybernetic model can be used to facilitate reasoning in both contexts. For instance, negative feedback control is a central principle in mechanical and electronic engineering but also occurs in various biological systems where the same mathematical formalization can be applied to represent these control principles (Wiener 1948). Negative feedback inhibits a reaction as a result of accumulation of the product of the same reaction, and can thereby serve to maintain stable concentrations, minimize fluctuations and create oscillatory behaviors. Whereas a causal model including the mechanistic details for the two contexts differs, the cybernetic model may be the same and serve as a starting point for identifying the processes that lead to these effects in biology (Boogerd et al. 2013). The cybernetic model can therefore denote a design principle that holds for a larger class of systems. Thus, design principles signify general features of biological organization and functioning and are typically conceptualized in an abstract mathematical or engineering-inspired framework. In the light of recent developments in systems biology it therefore becomes relevant to investigate how this strategy relates to the aim of formulating detailed causal explanations and to the modern view of biology, described in Section 1, that biology is a discipline without much room for generality. Section 3 returns to the modern view of biology and highlights the questions that systems biology raises with respect to heuristic and explanatory frames.

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3. For and against generality in biology

In traditional philosophical accounts of scientific theories, explanations, demarcation of science and non-science, and reductionism, generality was a key explanatory virtue (Carnap 1938/1949, Hempel and Oppenheim 1948, Friedman 1972). The positivist account viewed theories as axiomatic argument structures and explanations as deductive relations between theories, and theory reduction as a matter of deducing one theory from another (Nagel 1961, Hempel 1966). The idea of theory reduction has been widely debated in biology, in particular in relation to Schaffner’s (1993) model of theory reduction.1 Accounts of theory reduction have recently received limited support in philosophy of biology (see Section 3.1), since the move toward practice has led to a replacement of the deductive-nomological model of scientific explanations with an appreciation of characteristics that are specific for biological explanations. Several philosophers of biology have argued that a search for mechanistic explanations is a better model for what explaining biological phenomena means (e.g. Bechtel and Richardson 1993/2010, Machamer et al. 2000). Yet, the quest for general principles in systems biology appears to highlight a different explanatory ideal.

Winther argues that the use of abstract formalizations, e.g. in the context of mathematical analysis of gene regulatory networks in systems biology, has affinities with the aim of providing reducing theories. He argues that this ideal should not be dismissed because there are important regulative functions involved in this research practice. Winther stresses that: “mathematical reduction functions can powerfully and justifiably: (1) map the same particular variables and parameters (i.e., term-like formalisms), and basic functions (“laws of the model”) across mathematical theories and models of different mereological levels, or (2) directly correlate variables parameters, or basic functions of T1 models to different variables, parameters, or basic functions of T2 or T*” (Winther 2009, 138). Winther stresses that these explanatory functions are important aspects of theoretical biology and suggests that a mathematical model of a gene regulatory network (GRN) can be seen as a mathematical reducing theory whereby qualitative phenotypic characteristic (e.g. tissue differentiation) are embedded in, and ultimately derived from and reduced to GRN models.2 But Winther (2009) acknowledges that it still is unclear whether mathematical derivations will have atruly unifying function in biology or merely serve as important ‘regulative ideals’, since there are reasons to expect that some aspects of biological sciences are better captured by other explanatory frameworks, and that the world is too “dappled” (cf. Cartwright 1999) to allow for a global mathematical structure of the life sciences. In the following section I clarify why the biological world

1 For an overview of Schaffner’s model and the external criticism in the context of classical and modern genetics, see (Winther 2009). Winther further add to the criticism by pointing out internal inconsistencies in the model but he also develops an elaborate and reconstructed version Schaffner’s account to demonstrate the relevance of derivational relations in theoretical biology. 2 See Section 8 for a clarification of the concrete modelling approach that Winther uses as an example and for a different perspective on the epistemic virtues of this approach.

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is perceived as being dappled and complex and reflect on the relation between this perception and the current quest for general principles in systems biology. While the quest for general principles seems to imply a different biological ‘worldview’ I shall emphasize that there are different dimensions to generality and that general principles can serve regulative or heuristic roles in biological research. Ishall elaborate more on the regulative role of general principles in Section 3.2. As background for this discussion, let us first examine the reasons for reasons why philosophers of biology have been skeptical of the explanatory role of general laws and principles in the life sciences.

3.1. Against generality in philosophy of biology

The contingency and variation of biological systems have led to the view that biology is nomically inhibited, i.e. that no candidate for a biological law is universally applicable to the biological domain (Smart 1963, Beatty 1995, Mayr 2005). Examining the most common explanations in biology, they are indeed different from physics. Burian, Richardson and Van der Steen (1996) observe that in biology we do not find fundamental laws or theories but instead loosely interconnected theories and concepts acquiring meaning in local historical and scientific contexts. To demonstrate the inadequacy of general philosophical theories of meaning, reference, and scientific explanation in this context, they outline how these cannot account for the development of the gene concept in the history of genetics. Depending on the context, genes are functionally or structurally defined, and there is therefore no all-encompassing answer to what constitutes a gene (see also Stotz et al. 2004, Griffiths and Stotz 2013). The lack of an ‘essence’ of central entities such as genes has also been used as an argument against theory reduction and explanatory reduction, i.e. the idea that phenotypic features can be understood through logic derivations from laws relating to physicochemical principles or bystudying the molecular characteristics of the genome (Noble 2008, 2012). Since this is central to the emergence of systems biology, I shall elaborate a little further on this point.

In the early days of genetics, the discovery of DNA as a universal ‘code of life’ provided optimism for a reduction of the causal analysis of phenotypic characteristics to a study of the properties of the molecular and genetic building blocks (Powell & Dupré 2009). Genes have often been described as causal agents or “blueprints” that “code for” a specific trait by containing information that determine the sequence of amino acids that constitute proteins. To describe the relation between genes and protein products, information metaphors have been highly productive, but have also led to misunderstandings and simplifications of the relation between genes and phenotypes (Sterelny and Griffiths 1999).3 The gene centered view has now largely been replaced with an acceptance of the complexity of heredity, and the influences of higher level factors such as epigenetic influences and developmental processes on expression of genes. As mentioned in Section 2, an insight resulting from the Human Genome Project is the richness and complexity of the regulation of gene expression and protein products. The last decades of biological research have revealed a complex many-to-many relationship between genotypes and phenotypes; One genotype can lead to different phenotypic

3 August Weismann proposed in late 19th century that genes are the determinants in evolution, and more recently the focus on genes has been advanced in Dawkins’ idea of ‘selfish genes’ (Dawkins 1976). For further information on this debate see (Sterelny and Griffiths 1999, Sterelny 2007, Noble 2006, 2012)

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expressions under different environmental conditions (a phenomenon called phenotypic plasticity),while set of distinct phenotypes often display a high degree of robustness to changes at the genetic level. Thus, the relations between genotypes and phenotypes are highly nonlinear. As discovered in gene knock-down experiments, many changes at the genetic level do not affect functions at the phenotypic level, whereas other small changes can have major impact on the system (Jaeger and Sharpe, in press). This means that the idea of genome as a deterministic program has to be revised and a set of complex organizational schemes and causal influences have to be included. An understanding of the genotype-phenotype map requires an analysis that goes beyond genome sequencing, and that must encompass the nonlinear and often intuitively unpredictable effects of the concerted behavior of biological processes that are regulated across levels. At the time of writing, there are still profound gaps in the understanding of the relationship between genotypes and phenotypes (see also Section 8).

Moreover, the output of the sequencing efforts, the rather imprecise estimate of 20,000-25,000 , can be used to illustrate the problem of causal boundaries in biology. Genes are not

precisely locatable material DNA-segments. DNA strands consist of regions called introns and exons. When a sequence is transcribed to mRNA, it is the exon regions that are translated into protein (whereas introns conduct important organizational roles). However, there exist many alternative splicing possibilities of exons which mean that many proteins can be based on the same gene sequence. Furthermore, genes are non-contiguous entities that can overlap and even be read in two directions (sense and anti-sense) where ‘sense’ leads to templates for protein production and short anti-sense sequences can modify gene expression through interactions with complementarystrands (Dupré 2007a, 2008). Together these factors explain why different experimentalapproaches to DNA sequencing yield different numbers of genes on the same DNA-sequence. In general, many biological processes operate not as mechanisms with fixed entities. Disappearance and generation of entities, together with importance shifts in concentrations of biomolecular species, characterize many biological processes (Brigandt 2014a, 2014b in press). The blurring of boundaries between entities in biology is a philosophically interesting problem on several levels that also pertains to difficulties in distinguishing sharply between organisms and arguably also between living and non-living entities (Dupré and O’Malley 2007, 2009, Dupré 2008).4

In biology, processes span several levels and time-scales, and parts of systems can change in quantity, subcellular location, and organization even over short timescales of experimental operations (Alberghina et al. 2005, Marom 2010). The need to describe biological systems on many different ontological levels and from different perspectives has led to the view that biological research is best described through a pluralistic framework (Dupré 1993, Mitchell 2002, 2009). To account for this complexity and diversity, there seems to be little space for unifying biological laws. Rather than deductive strategies, explanations in biology have been more adequately described as mechanistic explanations (e.g. Glennan 1996, Machamer et al. 2000, Bechtel and Richardson 1993/2010, Boogerd et al. 2013). These are explicitly cross-level and irreducible to the principles of physics and

4 The intuitively sharp distinction between organisms can be questioned by the vast amount of inter- and intra-species symbiotic relationships (e.g. between humans and gut bacteria), and by some forms of asexual reproduction that also problematize the attempt to formulate a unified species concept and a demarcation of living and non-living entities.

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chemistry. To explain a phenomenon in this framework means to demonstrate how the behavior of a system can be produced from the interactions among the component parts. Biological mechanisms are thus composed of interdependent parts and interactions where entities are only causes by virtue of the processes and vice versa.5 Mechanistic explanations can be general due to the regularity of some biological mechanisms, and some accounts do emphasize regularity in their definition of mechanisms: “Mechanisms are entities and activities organized such that they are productive of regular changes from start of set-up to finish or termination conditions” (Machamer et al. 2000, 3,my emphasis). But generality is not considered the key requirement for explanation as in the covering law model (Bogen 2005).6 Machamer, Darden and Craver (2000) argue that you rarely find laws in biology, and even when generalizations are stated, the intelligibility of mechanisms is not directly reducible to their regularity but requires grounding in inter-level explanatory models. To explain a phenomenon is to explain how the actual behavior of a system is constituted due to interactions among its component parts, and this explanation may be valuable regardless of its explanatory scope outside the context of the system examined.

3.2. On the roles of general laws and principles

To investigate and articulate biological mechanisms, biologists draw heavily on diagrammatic representations of molecular interactions. Any biology student will be familiar with the use of exploded diagrams in biology textbooks where lock-and-key illustrations are popular pedagogic tools to clarify spatial molecular interactions. In systems biology publications and textbooks, exploded diagrams are often replaced with more abstract network models and a high number of equations, signifying the focus on quantitative modeling of dynamic aspects. How can we make sense of the epistemic roles of these rather abstract strategies for increasing our understanding of concretebiological systems? And is the explanatory status of general principles in biology?

As a starting point for exploring the relation between general principles and concrete real-world systems, one may draw on Cartwright’s (1983) distinction between virtues of theoretical and phenomenological models, or between general laws and concrete representational means. When discussing fundamental laws and phenomenological models, respectively, Cartwright uses the notion of explanation in two different ways.7 If the model or law expresses causal relations, she describes a trade-off between generality and truth. Because the world is complex and dappled, increasing generality is associated with increasing reliance on idealizing assumptions, or ceteris paribus clauses, which makes the general law or model ill-suited for accurate explanation of any real-world target

5 Mechanistic accounts have been criticized for focusing too much on properties of entities. For a response to this criticism see (Machamer 2004, Darden 2008, Kaplan and Bechtel 2012). 6 Glennan’s early account might be an exception since he defined a mechanism as ”a complex system which produced that behavior by virtue of the interaction of a number of parts according to direct causal laws” (Glennan 1996). In recent papers the concept of law is replaced with ‘invariant, change-relating generalizations’ (Glennan 2002, 2010). 7 This difference is captured by the different use of explanation in Chapter 2, ”The Truth Doesn’t Explain Much”, and the rest of the book How the laws of physics lie (Cartwright 1983). What Cartwright describes as phenomenological models are close to what Richardson (1997) calls causal models and Griesemer (2012) calls an empirical models. The characteristic feature is that these aim for realistic descriptions of real-world phenomena (although the representational means vary), whereas laws express general relations.

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system. In contrast, the explanatory power of general laws is not related to truth but to generality. General laws have more explanatory power for explaining more (classes of) phenomena. But for Cartwright they only explain more in a theoretical sense. If fundamental laws are ‘true’ they are so because these are true of the objects in the model, i.e. without the ceteris paribus modifier they are false (Cartwright 1983, 157). Thus, these are true only because we prepare the description of the phenomenon in a way where we can make a law apply to it, but it does not give us explanatory power regarding concrete causal relations. The explanatory virtues of phenomenological models and general laws are therefore very different. For the former, to explain how concrete phenomena ‘work’ is in focus whereas generality and unification of explanations is the focus for the latter.8

Can we describe the use of models from physics, mathematics, and engineering in systems biology in a similar way as Cartwright’s description of laws in physics, i.e. as signifying idealized principles that play a theoretical role but do not represent any real-world causal system? Different fields in biology have different aims and different explanatory standards, and systems biology might resemble what Winther (2003, 2006b) calls formal biology rather than compositional biology. Winther distinguishes these approaches through an analysis of the partitioning frames in different biological fields, i.e. the theoretical and experimental commitments made for the study of a system through abstraction. Formal biology is characterized by the use of formal laws to signify the relations among mathematically expressed objects and it shares methods with theoretical physics, whereas compositional biology is focused on the articulation of the concrete causal relations and functions of parts and wholes. Examples of compositional biology are molecular biology, developmental biology,and physiology, whereas examples of formal biology are population genetics and theoretical ecology. So, does this mean that systems biology is an example of formal biology? Systems biology draws on a formal framework and the search for general principles and ‘laws of biology’ are often highlighted as a key research aim (Westerhoff et al. 2009, Wolkenhauer et al. 2012). Yet, several scholars describe models and explanations in systems biology as mechanistic (Richardson and Stephan 2007, Boogerd et al. 2013, Levy and Bechtel 2013). Systems biology therefore rather appears to be a combination of aspects from formal as well as compositional biology, but to different degrees as captured in O’Malley and Dupré’s (2005) distinction between a pragmatic and systems theoretical stream (Section 2.3). But since the reliance on mathematical abstraction is a general feature of systems biology, it becomes pressing to understand how different explanatory strategies are related in systems biology, and to clarify what type of insights the abstract strategies can afford.

From Winther’s description of formal and compositional biology it is not immediately clear how the partitioning frames could be integrated, since he considers the modes of theorizing to be ‘fundamentally distinct’ (Winther 2003, vii). To describe the characteristics of compositional biology, Winther builds on Cartwright’s (1983) causal analysis and Cummins’ (1975) functional account. But to make sense of mathematical or theoretical explanations that occur most explicitly in formal biology, Winther finds little guidance in Cartwright’s framework. Instead, he draws on Friedman’s

8 Levins’ description of trade-off between generality, realism and empirical adequacy regards a similar point (Levins 1966, Levins 2006).

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(1974) account that views theoretical explanation as a subsumption of a series of different laws under more general law. This account describes relations in a theoretical world understood as “some

larger, relatively abstract and unobservable theoretical [formal mathematical!] structure [oftheoretical physics]” (Winther 2003, 213). In contrast to Cartwright and Cummins, Friedman is not concerned with mechanisms and material causation but with explanations of laws and theories. The significant difference between these accounts, and the emphasis on principles as well as mechanistic explanations in systems biology, therefore raises an important question about the relation between these explanatory aims. Is this a relation in tension or are there productive connections between these different epistemic aims? And, more generally, if mathematical abstractions in biology have explanatory power in a theoretical realm only, how are we to make sense of their role in increasing our understanding of biological phenomena?

Winther (2003) describes the role of general formalizations in biology as the complementary roles of model diversification and model merging. The former, model diversification, is a matter of decreasing the level of generality and is exemplified in the abstract models in evolutionary genetics and theoretical ecology that provide coarse-grained “how-possible explanations” of specific real-word cases (Winther 2003, 310). An example is the general Lotka-Volterra model to which details can be added and idealizations corrected to make the model applicable to cases regarding real-world populations. Model diversification is thus the use of general representations of the important relations in a system as a sketch used for the purpose of arriving at a more realistic representation in elaborated models. In contrast, model merging should be understood in relation to Friedman’s idea of unifying models and is a matter of increasing levels of generality. As a paradigm example Winther (2003) reflects on how the Price Equation subsumes the results of other models of the relation between variation and selection and thereby unifies these. Like Winther, I am interested in understanding the heuristic value of such unification attempts in systems biology where different structure-function relations are categorized under the same label of a general principle. But I shall give priority not to subsumption strategies but to the use of mathematical abstractions to identify biological mechanisms (Winther 2012). I aim to throw more light on this process – by demonstrating how mathematical models are combined with different epistemic tools and how they can facilitate the transfer of resources across disciplinary boundaries in order to identify general organizing or design principles.

Drawing on Bertalanffy’s (1969) description of the explanatory and regulatory roles of general principles, I argue that general principles can play different epistemic roles in the life sciences. The most obvious role of mathematical abstractions in biological research is, as described above, to serve as sketches for more elaborate or diversified models (e.g. Winther 2003, Humphreys 2004, Craver 2007, Weisberg 2007a, 2007b,). This role is of key importance for dealing with biological complexity and explains why the quest for general principles can support the goal of finer-grained explanations of concrete causal relations. Sometimes the aim is however not to de-idealize the formalization in question. Because general principles operate on a higher level of abstraction, they can take other regulative roles in research. I argue that these deserve more philosophical attention. The clari yprovided by subsuming specific explanations under a more general categorization is only one important aspect. I emphasize how higher-order formalizations can facilitate the transfer of resources

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(concepts, models etc.) across different scientific domains and thereby lead to the development of new insights and approaches to biological systems (Section 5 and 9). Furthermore, I clarify the role of mathematical abstractions in identifying the space of possible biological mechanisms underlying a specific biological capacity (Section 7, 8 and 9). General principles rarely describe real-world causal relations in an accurate way but they can serve important regulative roles as instantiations of explanatory schemes for classes of phenomena. The aims of general and specific explanations are therefore often complementary, and there need not be any tension between the quest for general principles and biological complexity. These strategies can even be mutually supportive.

Sometimes the existence of different explanatory strategies does however cause tensions in biological research. This happens when one methodological approach is promoted as the only valid scientific strategy. Disagreements regarding the relevant level of abstraction for scientific analysis can for instance be seen in the historical debate between neo-Rationalists and neo-Darwinists, where the former aimed for general principles of biological development that were largely independent of genetic variation among individuals, and neo-Darwinists who emphasized the value of understanding the historical (and thereby highly contingent) origin of specific traits (Section 8). These disagreements on the relevant level and kind of explanation are tightly connected to conceptions of living systems and how these evolve. The following section introduces the debate on adaptationism, and Section 3.4 outlines the relevance of this debate in the context of contemporary systems biology research.

3.3. Natural selection – the true general principle of biology?

The previous sections have assumed that biology, in contrast to physics, generally lack fundamental principles that unify biological research. Darwin’s theory of evolution through natural selection is often seen as one of the few general theoretical assumptions shared by biologists. The mechanism of natural selection would be questioned by very few biologists, and although natural selection cannot be applied predictively and deductively to special cases, the core idea of natural selection has been seen as an attempt to treat biological processes as influenced by deeper laws (Depew and Weber 2005). But despite the broad acceptance of natural selection as a key aspect of organismal evolution, there exist major disagreements on the extent to which natural selection determines the direction of evolution, on the dynamics and unit of natural selection, and on the heuristic value of adaptationism.9

Whereas some scholars find the focus on the effects of natural selection within a space of constraints sufficient for explaining the origin of traits, other scholars have pointed to the importance of development and to constructive aspects of non-selective forces (cf. Mayr 1983, Lynch 2007a, 2007b, Jaeger et al. 2012). Below I briefly outline the criticism of adaptationism and clarify the relevance of this debate for a new branch of systems biology called evolutionary systems biology.

The neo-Darwinian approach to evolution ‘modern evolutionary synthesis’. This notion refers to the developments in the 1940s and 1950s that synthesizedDarwin’s theory of evolution with Mendelian genetics. With discovery of DNA

9 Adaptationism is the strategy of assuming that organisms have been optimally designed by natural selection. This heuristic will be further clarified below and in Section 6.

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hereditary basis of evolution, evolutionary studies to a large extent became a matter ofunderstanding the selective effects of variation among allele frequencies in different populations. This focus on adaptation by natural selection is what Gould and Lewontin (1979), in their seminal Spandrels-paper, call the adaptationist research program. The guiding assumption is that biological traits reflect successful adaptive strategies, maintained by evolution due to beneficial functions that have enhanced the chances of survival among some individuals in the population. Gould and Lewontin’s (1979) criticize adaptationists for overlooking the non-selective sources of organismal evolution and for employing a too static picture of organisms. Specifically problematic in their view is the inference from current utility of a trait to its selective origin, without considering the interdependency of biological traits or alternative explanations for the shaping of biological structures. This problem they illustrate with an analogy to the origin of spandrels, the rounded triangular-shaped structures forming the intersection of arches in cathedrals (see Figure 3A). Spandrels serve decorative purposes, but this information alone is insufficient to conclude that this feature explains their origin. In fact, it would in Gould and Lewontin’s view be wrong to do so since spandrels are by-products of the decision to build a cathedral with a dome in need of stabilizing structures.10 Thus, the spandrels cannot be viewed as structures that are designed independently ofother architectural ‘traits’.

Figure 3A. The spandrels of San Marco. Source: Gould and Lewontin (1979)

Furthermore, Gould and Lewontin stressed the need to replace the view of organisms as passive receivers of challenges posed by the environment, with a view that encompasses the reciprocal relationships between organisms and environments (Levins and Lewontin 1985; Gould 1977/2006). Another important aspect in the 1979 paper is the productive role of evolutionary constraints that were the focus of earlier developmentalist or neo-Rationalistic traditions but largely neglected after the modern synthesis. Rather than seeing natural selection as the sole driving force of organismal evolution, Gould argues that non-selective

constraints can be seen as co-determinants of evolutionary change: “First, the constraints of inherited form and developmental pathways may so channel any change that even though selection induces motion down a permitted path, the channel itself represents the primary determinant of evolutionary direction. Second, current utility permits no necessary conclusion about historical origin” (Gould

10 For a criticism of the analogy see (Houston 2009, Olson 2012). Although the analogy can be problematized, it has been powerful for illustrating the problem with adaptationist inferences.

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1982, 383).11 Accordingly, the key debates in evolutionary biology have regarded the relative causal and explanatory role of natural selection and constraints, and the evidential weight of demonstrations of beneficial functions (Amundson 1994, Orzack and Sober 2001, ed.).

To account for the self-organizing effects of non-selective constraints on evolution, some researchers have investigated the nonlinear dynamics of biological networks using statistical and dynamical systems theory approaches (Kauffman 1993, Webster and Goodwin 1996, Jaeger and Crombach 2012). Pursuing different modeling approaches, these researchers aim to identify higher-order principles underlying the transformation between different system state descriptions. This emphasis is a shift from the gene-centered view and has led to heated debates about the explanatory priority of genetic factors and higher-level principles (Smith 1992). As a consequence of different views on explanatory standards and of the relevant factors for evolutionary explanations, studies of development and evolution have often developed in parallel (Depew and Weber 1995). Apart from the benefits of scientific specializations and competitive dialectics between the two approaches, this separation of studies of heredity and sources of variation has left an unfortunate gap in the understanding of the connections between development and evolution (Pigliucci 2009). Some researchers have therefore called for an ‘extended evolutionary synthesis’ to integrate these aspects (Carroll 2000, Pigliucci and Müller 2010, eds.).

As a very recent development in systems biology, the notion of evolutionary systems biology (ESB) has been coined to signify a new branch of systems biology that integrates systems biology methods with evolutionary biology. Proponents of this new field hope that ESB may be a candidate for an integrative framework that realizes such a synthesis (reviewed in O’Malley 2012). Below I clarify the motivation behind this approach and will be further analyzed in Sections 8 and 9.

3.4. The pitfalls of the design approach and the emergence of ESB

The emergence of evolutionary systems biology can be understood not only as an aim to bridge the gaps in evolutionary biology but also as a reaction to the limitations of systems biology methods. Reverse engineering projects have successfully offered new insights into general principles of biological systems (reviewed in Salvado et al. 2011). But the design-inspired approach has also been criticized for having severe limitations, in particular to imply a systematic oversimplification of the functional organization of biological systems and a misleading design-perspective on evolution that nurtures the adaptationist approach. Because the data are often of poor quality, the stability and relevance of the results of network inference methods have been questioned.12 Reverse engineering

11 This view is linked to much earlier theories about constrained developmental and evolutionary change such as Thompson’s (1917/2004) rational morphology and Waddington’s (1953, 1957) idea of canalization in epigenetic landscapes (see Section 8 and 9). Waddington’s landscape was in the 1960s formalized by René Thom as part of the development of catastrophe theory where trajectories in phase space are nonlinearly related to changes in initial conditions. This approach has inspired structuralist approaches to development in biology (e.g. Goodwin 1994) as well as modern evolutionary systems biologists (Manu et al. 2009a, 2009b, Jaeger and Crombach 2012). 12 A complicating factor is that structural and functional modularity not always have overlapping decomposition schemes.One of the insights of recent biological research is that functionality in biological systems can be delocalized in regulatory networks (Mazurie et al. 2005, Knabe et al. 2008, Krohs 2009b).

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methodologies, whether drawing on small or large datasets, face a particularly pressing problem of under-determination because several mathematical models fit the data set (Marom et al. 2009, Krohs 2012). In short, the degeneracy between structure and function makes it likely that the result of topological analyses are merely mathematical constructs that are disconnected from the workings of concrete biological mechanisms. Design analyses therefore need to be supplemented by methodologies that provide independent sources of evidence (Richardson 2007).

These problematic aspects of network inference methods are indeed recognized by the scientific community, and initiatives such as the DREAM project, Dialogue for Reverse-Engineering Assessment and Methods, strive to improve such methods (Kremling et al. 2004, 2005, Stolovitzky et al. 2007, eds., Marbach et al. 2010, Prill et al. 2010). Many problems associated with reverse engineering methods can be accommodated through the use of better quality data and more sophisticated models, and by combining reverse engineering methodologies with detailed experimental studies. But on a more fundamental level, some researchers have raised concerns about the whole idea of approaching biological systems as if they were designed machines. Several scholars have contended that machine metaphors are at odds with biological complexity and particularly misleading for understanding evolutionary dynamics (Jacob 1977, Pigliucci and Boudry 2010, Boudry and Pigliucci 2013, Nicholson 2013, Pauwels 2013). Criticizing the engineering approach, the population geneticist Michael Lynch states that: “Five popular concepts in biology today - redundancy, robustness, modularity, complexity and evolvability - invoke a vision of the cell as an electronic circuit, designed by and for adaptation” (Lynch 2007a, 803). As we shall see in Section 6, the research on network motifs exemplifies what critics have seen as unwarranted inferences from current utility of a trait to its evolutionary origin, because the overabundance of network motifs has been taken as evidence for the selection. Such debates show the continued relevance of Gould and Lewontin’s (1979) criticism of the adaptationist program, and the need for reflections on the implications of heuristics in general.

One of the sources of criticism of systems biology from evolutionary biologists is that systems biologists sometimes make evolutionary claims, even though the methodologies of systems biology mainly afford conclusions regarding functional capacities of biological systems. Thus, evolutionary considerations are often included merely as a ‘downstream appendix’ to functional analysis (Knight and Pinney, 2009). The lack of evolutionary grounding of systems biology has however also been positively promoted to distinguish systems biology from other fields. Functional analysis, or what Mayr called proximate questioning, has often been considered the main or even only aim of systems biology. For instance, the introduction to the first edited volume on philosophical foundations of systems biology presented systems biology in contrast to evolutionary biology: “Evolutionary biology studies how living systems came to be, whereas systems biology studies how living systems are: a biology of becoming versus a biology of being. This is a profound difference” (Boogerd et al. 2007, 9). For many projects, functional analysis remains the key focus, but other researchers have pushed for the expansion of systems biology to include evolutionary analysis. The motivation for this

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recent development is based on a wish to overcome adaptationist leanings in biology and to extend the methodological and explanatory scope of evolutionary biology as well as systems biology (O’Malley 2012).

Many proponents find that biological analysis is severely compromised when evolutionary aspects are not part of the biological analysis. Some researchers even argue that proximate and ultimate questions cannot and should not be separated, and that systems biology therefore ultimately needs evolutionary biology (Cain et al. 2008, Laland et al. 2012, Steinacher and Soyer 2012). When discussing the implications of adaptationist implications in papers 3 and 4 I defend the view that some functional questions about biological designs can be separated from the question of their origin. That being said, it is important that some causal capacities of extant organisms cannot be meaningfully understood without an evolutionary perspective. For instance, some pathogens have such a short generation time and high mutation rate that the process of infection of the host operates on the evolutionary timescales of the pathogen. To understand some host-pathogen interactions, e.g. the case of RNA viruses, therefore requires an integration of epidemiology, immunology and evolutionary analysis (Grenfell et al. 2004). Furthermore, knowledge about evolutionary trajectories can often guide functional investigations by suggesting mechanisms based on knowledge about similar traits in related species. Therefore, an integration of functional and evolutionary studies is often of mutual benefit. Systems biology has much to gain from a combination of engineering approaches with methods from evolutionary biology. But some evolutionary systems biologists also claim that evolutionary biology ultimately needs systems biology to progress (O’Malley 2012).

Systems biology methods are particularly useful for dealing with models with large numbers of interactions and for exceeding the statistical sequence analysis capabilities of population genetics (Tirosh et al. 2007). Dynamic modeling can be combined with bottom-up experimentation and evolutionary simulations to explore the potential for predicting future evolutionary states (Jaeger et al. 2012). It is often said that evolution is fundamentally stochastic and contingent but recent results indicate that it may be possible to predict some general patterns of genomic evolution. For instance, experimental evolution of bacterial antibiotic resistance shows that the development of resistance is constrained by interactions between different mutations (Weinreich et al. 2006, Koonin and Wolf 2010). Such insights may in the future allow for a connection between evolutionary biology and medicine in permitting informed decisions on combinations of antibiotics in medical treatment (Papp et al. 2010). Although many systems biologists are optimistic regarding the future of such research, examples and tools for prediction of medically relevant trajectories are still sparse. The ambition to predict evolutionary trajectories, even for simple organisms, may turn out to be extremely difficult unless these are tightly constrained by physical, selective, and developmental constraints. It is still to be determined how plastic genome organization is, and how tightly constrained evolutionary trajectories are in different organisms. But the data and tools to answer such questions are emerging and several researchers have called for more effort to be put into the search for what they call ‘laws or evolution’ (Koonin 2011), ‘evolutionary design principles’ (Steinacher and Soyer 2012) or ‘generic principles’ of evolution (Jaeger and Monk 2013). Thus, also in the context of evolutionary studies,

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we face the question of the relation between the quest for general principles and the complex and contingent subject matter: evolving systems. In Sections 8 and 9 I examine the role of such principles and the combination of different modeling strategies in evolutionary systems biology.

Aside from to the strategy to accommodate the limitations of design approaches through inclusion ofevolutionary aspects, there exists a systems theoretic stream with explicit aspirations to go beyond the design perspective in addressing - in a formal way - what aspects characterize life and how biological systems are organized. This stream remains occupied with proximate questions but articulate the aim as identification of organizing principles, rather than design principles. The main question addressed is how to formally articulate the organizational schemes that make living systems special, i.e. the principles that ensure the self-maintenance of cells, tissues, and whole organisms and the reciprocal relations between these. To address such aspects, these researchers draw on earlier systems theoretic approaches. For instance, attempts have been made to develop abstract cell models drawing on Rosen’s theoretical framework, category theory, and (mathematical) general systems theory (e.g. Mesarovi et al. 2004, Mulej et al. 2004, Hofmeyr 2007, Wolkenhauer and Hofmeyr 2007, Letelier et al. 2011). Thus, this methodology shares with engineering approaches the aim to identify general principles of functional organization in biology and to seek integration or unification through abstraction, but the formal framework differs. This approach is currently less influential in systems biology research than the data-intensive approach focused on modelling of molecular networks, but it is an aspect of systems biology that raises important philosophical questions that will be explored in Section 5. Furthermore, this approach exemplifies the revival of classical questions regarding the definition of live and the ontological status of the organism (Morange 2005, Nicholson 2014 forthcoming).

To introduce what may be candidates for general principles in biology, I have listed some examples in Table 1 and categorized these according to different research fields. The table is only a tentative overview of examples but it may serve to illustrate how different field have aimed to define general principles in various ways. First, it is important to note that biological principles are not only identified in top-down approaches but also play a role in bottom-up methods where some biological mechanisms have been generalized as common biochemical strategies. For instance, ATP is a general energy currency; phosphorylation-dephosphorylation is a common strategy to activate and inactivate molecules; and many processes associated with protein synthesis, cell divisions etc. are the same across different cell types and biological kingdoms. Although such generalizations play an important role in bottom-up approaches, top-down approaches often more explicitly articulate the quest for general principles as a research aim and try to produce mathematical articulations of these. What in the modern engineering approach are called design principles have affinities with Rashevsky (1960) and Rosen’s (1967) ‘optimality principles’. The guiding idea is to formulate principles that in engineering would provide the optimal solution to a given biological problem and to use this as a heuristic guide for identifying such examples in biological systems. Furthermore, such principles play a regulative function in categorizing the scope of organizational features that afford specific types of functions (Jaeger and Sharpe 2014 in press). As an example I have mentioned Savageau’s (1989) ‘Demand Theory’ where principles of gene regulation are based on the extent to which these needs to be expressed in order to maintain normal cell functions. Savageau has been a source of inspiration

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for Alon’s work, and Alon’s group have further investigated the systems studied by Savageau (Shinar et al. 2006, Alon 2007a).

Description and field Examples of general principles ReferencesMolecular biol., BiochemistryGeneral biochemical and biomolecular principles

Signaling cascades, Phosphorylation-dephosphorylation cyclesGeneral mechanisms for heredity and gene regulation

Goldbeter & Koshland (1981)Overview in (Berg et al. 2002)

SYSTEMS APPROACHESSystems Eng., DST, MCA, Cybernetics, Relational Biol.Optimality principles

Systems BiologyDesign principles

Evolutionary Systems Biol.Evolutionary Design PrinciplesGeneric PrinciplesLaws of evolution

Branching angle in vascular systemsDemand Theory of gene regulation

Network motifsRobustness, modularity

General trajectories of evolutionary change leading to design principlesCorrelation between population size and mutation rate

Rashevsky (1960), Rosen (1967) Savageau (1989)

Alon (2007a), Savageau (2001)Csete and Doyle (2002), Velazquez(2009)

Soyer, ed. (2012),Hogeweg (2012)Koonin (2011)

Theoretical biol., Parts of Evo-Devo and Developmental biologyFixed patterns of morphological organization in development and evolution (generic forms)

Cartesian transformationsConserved chorda and digit of vertebrae Generic models of limb evolution Morphological patterns in plant development (e.g. Fibonacci series)

Thompson (1917/2004, 1942) Ridl (1978), Galis (1999)Collins et al. (2007)Goodwin, Kauffman and Murray (1993), Goodwin (1994)

General System Theory

Higher Order Laws/Isomorphic principles

Allometric scaling relationsExponential equation (growth/decay)Logistic lawGrowth equationsPrinciples of open systems

Bertalanffy (1950a, 1950b, 1967, 1969)

MGST, Systems biologySystems TheoryOrganizing principles

Feedback underlying regulation, control and adaptation of dynamical systemsBounded Autonomy of Levels

Closure to efficient causation

Coordination principlePrinciples of tissue organization

Wiener (1948), Mesarovi et al. 2004

Mesarovi and Takahara (1970, 1975)Rosen (1991), Letelier et al. (2001) Hofmeyr (2007)Wolkenhauer and Hofmeyr (2007)Wolkenhauer et al. (2011)

Table 1. An overview of some general examples in biological research.

As mentioned above, evolutionary systems biologists have called for an analysis of design principles that take the evolutionary origin of the observed patterns into account. Thereby they hope to clarify whether these result from adaptation or other evolutionary processes. It is expected that general evolutionary patterns can be found and that these general insights can be framed in terms of what is sometimes referred to as ‘evolutionary design principles’ (Steichacher and Soyer 2012), ‘generic principles’ (Hogeweg 2012), or ‘laws of evolution’ (Koonin 2011). The general principles sought in evolutionary systems biology may be inspired by design thinking (e.g. Poyatos 2012) or may draw on earlier developmental approaches that have sought ‘generic principles’ behind morphological

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shapes (Jaeger and Crombach 2012, Jaeger and Monk 2013). Principles akin to the latter has recently been studied in a stream of evolutionary developmental biology, Evo-Devo, that search for generic principles that explain the general patterns seen in e.g. limb evolution (Table 1, Collins et al. 2007). At the bottom of the table I have listed some examples from General Systems Theory that have been emphasized as higher order laws (Bertalanffy 1969) or organizing principles (Wolkenhauer et al. 2011). These are for instance allometric scaling relations such as the relation between animal size and metabolic types and formal frameworks signifying dynamics based on feedback control. Such examples will be further examined in Section 5. I could have listed many more examples, and the relations between general principles defined in different fields is in need of further analysis. As a first step I have mainly aimed to establish a framework for understanding the motivation behind the search for such general principles, given that biological systems are perceived as being too complex to allow for unified descriptions (e.g. Smart 1963, Beatty 1995, Mayr 2005).

My starting point is thus that some biologists, unlike Mayr’s (2005) ‘heterogeneity view’, do search for general principles by abstracting from system-specific differences, and I am interested in understanding why. At the same time, I am interested in understanding what the increasing mathematical embedding in the life sciences means for the way (systems) biologists think about living systems. The greater use of mathematical modelling in current biological research is also acknowledged by Bechtel and Richardson in the foreword to the 2010-edition of the book on Discovering Complexity. Bechtel and Richardson (1993/2010, xix) stress that this tendency does not imply a return to nomological explanatory models and that “solutions are generated not by constructing proofs from these equations but through numerical simulations”. This statement is currently questioned by the use of theorem proving in the systems theoretic stream outlined above (e.g. Shinar and Feinberg 2010, 2011, Wolkenhauer et al. 2011, 2012). However, Bechtel and Richardson’s general conclusion is not; the use of theorem proving does not reflect a return to the explanatory ideals of logical positivism. Despite the increasing use of very abstract frameworks in biology, I have found no reasons to think that these challenge the importance of strategies designed for detailed and context-dependent explanations. Rather, the epistemic value of mathematical abstraction in general, and theorem proving in particular, reflect the existence of complementary and explanatory aims and regulative roles. The thesis is therefore not a deference of the covering law model in biology. In fact, I will not be concerned with the definition of laws and the question of whether laws exist in biology. What I aim to do is to reexamine the roles that general formalizations play in biology from a pragmatic perspective where the heuristic and explanatory value of the quest for general principles is investigated. I highlight a different dimension of generality where unification does not necessarily imply theory reduction or a neglect of biological complexity and variation, but rather represents an effort to establish cross-disciplinary systems-theoretical tools that counterbalance the fragmentation of science in the 20th century.

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3.5. Overview of research papers

To ease the reading of the dissertation, I provide a brief overview of the research papers. The five research papers that make up the core of this thesis (Section 4-8) are ordered according to topics but also according to the chronological order in which they have been written. Section 4 investigates the broader question of how knowledge generation is possible through the combination of limited epistemic means. I focus on Alon’s search for design principles in biological networks, and in particular on the role of a more rigid mathematical framework to conceptualize biological functions. Section 5 further explores the motivation for articulating general biological principles and places the quest for general principles in a historical context. The paper traces the quest for general principles back to General Systems Theory and cybernetics and asks whether we can learn something about the current practice by studying this historical perspective. Sections 6 and 7 both address the relation between design thinking and adaptationism. Section 6 analyzes the implications of methodological adaptationism in functional and evolutionary analysis, and Section 7 investigates the possibility of dissociating design thinking from adaptationism. Section 8 integrates several of the topics explored in the previous papers: the combination of general and abstract mathematical models and experimentation, and the tension and possible reconciliation of the general and the specific, and of studies of development and evolution. The content of the individual research papers is summarizedbelow.

Section 4. When one model is not enough: Combining epistemic tools in systems biologySara Green, Published in Studies of History and Philosophy of the Biological and Biomedical Sciences

This paper addresses the question of how knowledge generation is possible when the epistemic means available for analysis are limited. I argue that a central aspect of scientific analysis is the combination of epistemic tools with different constraints. Drawing on Rheinberger’s (1997) historical epistemology, I claim that to understand the use of models in scientific practice we need not only focus on the relation between models and real-world targets but also the relations between the models. I exemplify this with Uri Alon’s work on network motifs where a network representation affords an investigation of network topology in comparison to engineered networks. I analyze how the group combined different models with different constraints; reflect on the role of experimentation for stabilizing the findings; and analyze the affordances and limitations of conceptualizing biological functions in a mathematical language from control- and graph theory. Finally, I clarify similarities between Rheinberger’s epistemology and the philosophical literature on robustness.

Section 5. Tracing Organizing Principles: Learning from the History of Systems Biology Sara Green and Olaf Wolkenhauer, Published in History and Philosophy of the Life Sciences

What motivates the search for general principles in systems biology? Is a realistic representation of biological phenomena a necessary requirement for the epistemic value of such principles? And is there a tension between the quest for general principles and the complexity and contingency of biological systems? To address these questions, this paper draws on the historical background of

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current systems theoretic stream. The first attempt to institutionalize cross-disciplinary efforts to find general principles of relevance for biology dates back to the foundation of societies for systems sciences in the 1950s. We show how Bertalanffy’s work can aid understanding of the explanatory and regulative roles of general principles. We reflect on the systems theoretical efforts to counterbalance fragmentation and overspecialization in the life sciences and compare this to interpretations of the current situation in systems biology. Further, we examine the epistemic roles of general principles in unifying descriptions of typified classes of systems and providing coarse-grained representations for the identification of causal mechanisms. We argue for the importance of minding the level of abstraction of such principles, because the different explanatory functions operating at different levels of abstraction make general principles complementary to detailed causal explanations,rather than alternatives.

Section 6. A philosophical evaluation of adaptationism as a heuristic strategy Sara Green, Resubmitted to Acta Biotheoretica

The engineering approach in systems biology has been criticized for having adaptationist leanings. The implications of adaptationism have been widely debated in philosophy of biology, specifically in response to Gould and Lewontin’s (1979) seminal Spandrels-paper. However, the debate has primarily focused on questions regarding the relative causal power of selection and non-selective forces, on requirements for testing optimality models, and on the explanatory status of evolutionary explanations. In this paper I return to the methodological concern raised by Gould and Lewontin and reexamine the relevance of their criticism in light of functional as well as evolutionary analyses. I draw on case studies from zoophysiology and systems biology to demonstrate how the implications of methodological adaptationism differ in functional and evolutionary contexts and on different levels of analysis. I contend that the concerns regarding methodological adaptationism cannot be reduced to an issue of testability in a Popperian sense but regards the broader context of testing practices that involve issues such as the dominance and wide acceptance of guiding assumptions. I defend the productivity of methodological adaptationism but dispute the methodological imperialism of strong methodological adaptationism.

Section 7. Design sans adaptation Sara Green, Arnon Levy and William Bechtel*. In review in European Journal of Philosophy of Science

In the context of philosophy of biology, the notion of reverse engineering is typically defined as an adaptationist reasoning strategy, whereas the term when used in systems biology typically is imported more or less directly from engineering. This makes the connection to adaptationism unclear and in need of philosophical analysis. We investigate the relation between design thinking and adaptationism in recent case studies and argue that these in some cases can be dissociated. Our analysis draws on a comparison of two cases that are similar in terms of the system studied and their reliance on design thinking but differ in their connection to adaptationism. We describe a thin notion of design that does not imply assumptions about the origin of the design, but allows for a given causal capacity through a specific organizational scheme. Furthermore, we demonstrate how optimality assumptions can

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serve as search tool to constrain a theoretical search space for designs that can realize a specific capacity. Similarly, we demonstrate how a mathematical conception of optimal design and robustness-criteria can serve as a constraint on the possible underlying designs that can guide the identification of biological mechanisms.

*The authors contributed equally to the writing of this manuscript

Section 8. Integration challenges in Evolutionary Systems Biology Sara Green, Melinda Fagan and Johannes Jaeger*, draft of a paper to appear in a special issue on evolutionary systems biology, commissioned by Biological Theory

Evolutionary systems biology (ESB) is a recent branch of systems biology that aims to extend the scope of evolutionary biology as well as systems biology. ESB applies systems biology methods to the study of evolutionary trajectories. In this paper we analyze the challenge of integrating developmental and evolutionary research fields that have long developed in parallel due to conflicting explanatory schemes. We clarify the challenges of this integration project though an examination of historical tensions between neo-Darwinian and neo-Rationalistic approaches regarding the role of non-selective constraints, the causal power of genes, and different explanatory standards. The latter is further clarified with an analysis of contemporary stem cell research where some researchers draw on the framework of dynamical systems theory to identify general principles of stem cell properties and others pursue an experimental approach focused on molecular details. In these cases, the explanatory differences cause tensions between groups that could benefit from integration. As a contrasting case that shows the synergistic effects of this integration, we analyze a case in evolutionary systems biology where reverse engineering methods are used to bridge the gap between theoretical DS approaches and experimental research, and to integrate insights on developmental and evolutionary dynamics. We argue that all of these aspects are necessary for understanding the ‘tinkering potential’ of evolving lineages and hence for the possibility of predicting the space of future evolutionary trajectories.

*The authors contributed equally to the writing of this manuscript

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