9. Conclusions and new questionsThroughout the dissertation I have investigated how knowledge is generated in contemporary systems biology through different research strategies. I have focused on heuristic strategies inspired by design thinking and (dynamical) systems theory for identifying generalizable principles of biological organization, and on the relation between design thinking and adaptationism. In this final chapter I first provide a summary of the central points in the thesis, expand on some of the conclusions, and reflect on new philosophical questions associated with current tendencies and future development of systems biology. This chapter is structured as follows. Section 9.1 summarizes the main points of the papers, while Sections 9.2 and 9.3 further deepen the discussion on the epistemic roles of general principles in biology. Section 9.4 examines the implications of an alternative strategy, large-scale modeling, that so far I have only briefly discussed. Large-scale modeling is an important part of the currently emerging branch called systems medicine where it is used in the production of digital patient models that can be used by the doctors and researchers working on ‘personalized medicine’ (Section 9.5). Examining the aims of systems medicine and evolutionary systems biology, Sections 9.6 and 9.7 reexamine the role of epistemic tools from engineering and physics in the life sciences.

9.1. Concluding comments on the research papersIn this project I have examined how different epistemic means (including cognitive, experimental and representational strategies) are combined in practice to generate new insights in biology. Heuristic strategies are indispensable when the complexity of the system exceeds our power of analysis. The ability to constrain a problem space makes scientific inquiry possible when the problem space is toolarge for random trial-and-error to be feasible. Because all aspects of a complex system cannot simultaneously be analyzed in detail, biologists must draw on a variety of strategies to identify andaccess the relevant units of analysis. As a result of the complexity and variation of biologicalphenomena, many biological explanations are context-dependent and have a narrow scope. Molecularbiology has decomposed organisms into tractable subsystems that have been analyzed in impressivedetail. This has generated a great amount of knowledge on sub-systems but the current disproportionality between knowledge generated on molecular pathways and biomedical progressalso calls for new strategies to recompose these insights and to approach living systems in different ways. Whereas some systems biologists aim for recomposition through large-scale modeling, others aim for generalizable insights through mathematical abstractions or definitions of design or organizing principles. It is the latter strategy that I have been concerned with in this thesis. While supporting the view that explanations in biology cannot be reduced to physical or biological laws, Ihave analyzed the motivation behind the increased focus on general principles of biological organization and discussed whether this quest is in conflict with the complexity of biological systems.I have defended the view that the search for general principles can be seen as complementary to theaim of understanding the details of complex biological processes.

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The first paper of the thesis (Section 4: “When one model is not enough: Combining epistemic tools in systems biology”) examined the broader question of how mathematical modeling and other epistemic means are combined in practice. Inspired by Rheinberger’s (1997) historical epistemology and recent philosophical work on models as ‘epistemic tools’ (Knuuttila and Loettgers 2011), I focused on the process of knowledge generation in biology where models with different constraints are combined. A case study, Alon’s work on identification and investigation of network motifs, was used to illustrate how engineering analogies can facilitate biological reasoning, and how mathematical abstractions can be used to identify organizational patterns and mechanisms in biological networks when the system is too complex to afford an unmediated functional decomposition. In this case, mathematical abstractions were used to decompose a complex transcriptional regulatory network into overabundant motifs, and specific functions were identified for each motif. To produce stable results in a biological context, abstract representational tools need to be combined with experimental approaches that provide material resistance to the investigation. To illustrate this combinatorial process, I drew on Rheinberger’s notion of ‘resonance’ that has affinities with the notion of robustness in the philosophical literature on modeling. I also used this case to introduce the design approach in systems biology, geared towards the search for so-called design principles. The research on network motifs shows how abstract ‘cybernetic’ models may not only be productive as sketches for more detailed explanations (model diversification) but also have an important role in facilitating the transfer of conceptual and representational resources across scientific domains and in categorizing different types of biological functions. I argued that the mathematical rigor of the modeling framework employed affords a comparison of functions of different types of circuits independent of their context in biological systems, and thereby allows for a conceptualization of dynamic functions of biological subsystems in engineering terms. At the same time, however, this abstract and engineering-inspired framework has been criticized for i) simplifying biological systems whose properties are highly context-dependent, and for ii) having adaptationist leanings. These implications were examined in three different research papers (Section 5, 6 and 7). I summarize the conclusions of these papers below.

The quest for general principles in systems biology raises the question of whether this strategy can encompass the complexity and context-dependency of biological mechanisms. To answer this question, the second paper (Section 5: “Tracing Organizing Principles: Learning from the History of Systems Biology”, with Olaf Wolkenhauer) explored the motivation behind the quest for general principles among proponents of general systems theory. We defined organizing principles as robust generalizations with applicability across different systems that display the same type of dynamic behavior. The similarity of higher-order principles, despite causal differences among the systems described, is possible because these are identified on a higher level of abstraction. General principles are not geared towards ‘covering’ specific causal relations through subsumption of specific examples but clarify similarities in higher-level relations of systems of similar (dynamic) types. To clarify this methodological aspect, we drew on insights from earlier research approaches, in particular from General Systems Theory. Bertalanffy (1969) suggested that an exploration of the applicability of general system principles could help prevent re-invention of the wheel in fields isolated from each other because these could facilitate the transfer of methodological frameworks across different

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research contexts. Examples of such transfer of resources are the formalization of feedback mechanisms in engineering and biology, network methods developed in graph theory with applicability in many different sciences, and equations shared among economics and population ecology. It was hoped that general systems theory could provide a unifying framework and thereby counterbalance the increasing fragmentation in science. At first sight, the emphasis on unification seems to be in conflict with the now well-accepted view that biology is autonomous and irreducible to physics. We have argued that this is not the case. Because the unification operates on a higher level of abstraction than explanation of concrete systems, the quest for general principles does not reduce the need for context-specific details but rather hints at complementary epistemic aims. General principles can provide useful means for identification and further investigation of causal mechanisms, and they can also take regulatory roles in the transfer of resources across domains and serve to organize theoretical commitments. Accordingly, we argued that there is no necessary conflict between these aims if we acknowledge the explanatory virtues of explanatory strategies operating on different levels of abstraction. I shall clarify these roles further in Section 9.2 and 9.3. First, I summarize the main points made about the other concern with the quest for design principles, namely adaptationist implications (discussed in Section 6 and 7).

The disanalogy between evolution of organisms and design of artifacts is one of the often-cited problems with design thinking in biology (Jacob 1977, Nicholson 2013). In particular, engineering approaches have been criticized for nurturing an adaptationist research program where problematic inferences are made from the current utility of traits to the reason for their origin (Lynch 2007a, 2007b). Adaptationism has been one of the central topics in philosophy of biology, especially since Gould and Lewontin’s (1979) seminal Spandrels-paper where they accused adaptationists for confusing Panglossian just-so stories with scientific explanations. Although Gould and Lewontin stressed the problems with the methodology of adaptationism, the debate in philosophy of biology has mainly centered on explanatory and empirical issues concerning the relative causal power of natural selection and non-selective evolutionary forces. Furthermore, the focus of the debate has been directed towards examples in evolutionary biology and evolutionary psychology, and not in fields such as physiology and systems biology where methodological adaptationism (MA) is also used. To counterbalance this focus, Section 6 (“A philosophical evaluation of methodological adaptationism”)discussed the implications of adaptationism as a heuristic, through an examination of two case studies from physiology and systems biology. A common defense for MA is that this reasoning strategy does not influence the ‘context of discovery’. In other words, the testability of adaptationist hypothesis is taken to render MA unproblematic. From this perspective MA can even be seen as particularly productive for discovering constraints on selection as adaptationist hypotheses are met with resistance through testing. While acknowledging that adaptationism can have heuristic value regardless of the truth value of the guiding assumptions underlying this strategy, I argued that the methodological concern stressed by Gould and Lewontin goes beyond the issue of testability in a Popperian sense. It also involves the dominance of adaptationism and the associated assumption that the job is not done until an adequate adaptive explanation for a particular trait has been established. It has been argued

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that Gould and Lewontin’s criticism would be like kicking at open doors if it were to be published today, since evolutionary biology has taken up the challenge of the Spandrels-paper and improved the requirements for testing adaptive hypotheses. In Section 6, however, I argued that the criticism is still relevant, in particular for discussing testing practices in research areas that study the evolutionary origin of global features like robustness of networks, modularity and network motifs. Furthermore, I provided arguments against the view that MA is the only heuristic needed. It is a highly productive heuristic for some purposes but it has limitations for addressing other questions such as the non-random effects of random mutations and non-selective constraints. In this paper I also explored the different ties to adaptationist implications when MA and design approaches are used in functional and evolutionary analysis. Section 7 provided further arguments and clarification of the relation between design thinking and adapationism in functional analysis.

Section 7 (“Design sans adaptation”, with Arnon Levy and William Bechtel) explored the scope of design thinking outside the framework of adaptationism. We argued that the search for design principles in some cases can be separated from adaptationist implications, even in cases where researchers draw on optimality assumptions. To demonstrate the difference, we examined two contrasting cases which focus on the development of intestinal crypts. One of these cases was used to illustrate how design thinking can be productive in non-evolutionary analyses. We described a ‘thin’ notion of design focused on the relation between a certain set of operations and a causal capacity that does not involve an explanation for the origin of this design. In biology, the problem space for candidate operations to realize a specific function can be vast. When this is the case, knowledge about the optimal solutions to a similar task in engineered systems can serve as a constraint to narrow the search space and thereby to suggest working hypotheses for further investigations. To illustrate how optimality assumptions can serve as a search tool, we related this role to a third case where a mathematical conception of robustness was used to constrain the possible mechanisms to be investigated experimentally. Thus, in these cases design thinking is coupled to a formalization of biological systems that serves to delineate the structurally and functionally possible. We do not deny that coupling of design thinking and adaptationism is common and that it can be very fruitful. Yet,we emphasize that the use of design thinking in general, and reverse engineering methodologies in particular, need not entail adaptationism or even evolutionary considerations. Likewise, our account does not imply that functional and evolutionary questions always can be meaningfully separated. Often the analysis is enriched if evolutionary considerations are involved. However, for some functional analyses, this aspect is not present and the design approach is employed to constrain the problem space through an analysis of functional constraints.

Reverse engineering approaches may also be employed in evolutionary analysis without adaptationist implications, e.g. in analyses of the potential for evolutionary change. Section 8 (“Integration challenges in evolutionary systems biology”, with Melinda Fagan and Johannes Jaeger) explored the role of a reverse engineering methodology in ESB in facilitating an ‘extended evolutionary synthesis’

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that goes beyond a naïve adaptationist analysis. The ultimate aim of evolutionary systems biology is to turn evolutionary biology into a more predictive science and to integrate insights on development and evolution as well as explanations defined on different levels of detail. Section 8 examined two related integration challenges in ESB, namely the task of combining approaches with different explanatory standards (general principles vs. mechanistic explanations) and of integrating studies of development and adaptive evolution. Historical and contemporary cases show how these explanatory frames and aims are often in conflict. Yet, examples from ESB demonstrate how identification of general (evolutionary) principles can synergize with the search for detailed mechanisms, and how there is a feedback between developmental dynamics and natural selection. The suggestion presented in Section 8 views the relation between explanations of historical (and contingent) processes and general evolutionary principles as analogous to the relation between mechanistic explanations and general principles (Section 5). General evolutionary principles inform about possible or probable developmental and evolutionary trajectories given a set of general constraints. Constraints can be ofdifferent kinds (physical, genetic, functional etc.). In the cases examined, developmental constraintsare expressed mathematically, build on a model that outlines the structure of gene regulatory dynamics. Although higher-level principles may not be able to help us predict specific evolutionary changes at a high level of accuracy, they can help provide insights to the general ‘tinkering potential’ of non-selective forces that are crucial as background for understanding the space of previous and future adaptive changes. Furthermore, they may comprise a key component in the understanding of the integrated effects of selective and non-selective factors, i.e. to meet Hugo de Vries’ challenge to not only explain the survival of the fittest but also the arrival of the fittest. Whereas adaptive evolution affects the possible developmental states of future generations,developmental trajectories define the possible phenotypes that selection can act on. I shall return to this point in Section 9.7 where I reexamine the role of engineering approaches in biology. First I expand on the discussion on the implications of formalizations in biology.

9.2. Revisiting generality and the role of formalizationsThe consequences of the increasing mathematical embedding of the life sciences for mechanisticaccounts of explanation has recently become a hot topic in philosophy of biology. The role of mathematical models, in analyzing the dynamics and organizational aspects of biological systems,has been stressed as a necessary aspect of the explanations seen in fields like systems biology, Evo-Devo and chronobiology (Bechtel 2007, Boogerd et al. 2013, Levy and Bechtel 2013, Brigandt 2013 and 2014a, 2014bin press). The last decades of research have revealed how many biological processesare non-sequentially organized into interacting cycles with multiple coupled feedback loops. An example is the ADP-ATP cycle that in a full analysis involves modeling of orchestrated glycolyticoscillations and links to other biochemical systems (Kummer and Olsen 2005, Bechtel & Abrahamsen 2011). Thus, the analysis and explanation of highly integrated systems with many interactingvariables must often rely on sets of differential equations that cannot be solved analytically. Some have seen the increased focus on dynamics and organization as a challenge to mechanistic accounts. For instance, Craver’s (2007) mutual manipulability criterion for explanatory relevance has been

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criticized for neglecting key organizational aspects that are required to explain biological phenomena such as robustness and phenotypic plasticity (Gross 2013, Brigandt 2014a in press). Similarly, Craver’s rejection of an explanatory role for mathematical models in biology has been met with criticism from researchers studying not only systems biology and EvoDevo but also examining Craver’s own example of the Hodgkin-Huxley model of neurotransmission (Levy 2013, Brigandt 2014b in press). Extending the mechanistic account towards a dynamical mechanistic account, others have emphasized the possibility of accommodating abstract organizational aspects (Bechtel 2007, Bechtel and Abrahamsen 2011, Levy and Bechtel 2013). This thesis has not been concerned with the scope of mechanistic explanations, but I have emphasized the existence of multiple explanatory aims in biological research practice. If we want to make sense of the role of general principles and abstract mathematical models we need to look beyond the aim of accurate representation. I have, however, stressed that a focus on general organizational or dynamic principles does not make the study of mechanisms obviate. Paraphrasing Levins (1984, 26), understanding phenomena is a matter of understanding the relation between the general and the particular. To explain a biological system it is often necessary to both i) analyze higher-level organizational aspects, and to ii) decompose biological systems into constituent parts in order to understand the lower level causal relations between these(see also Kaplan and Bechtel 2011). I have emphasized that general principles can serve important regulatory roles that go beyond the aim of explaining any one particular system. But rather than focusing on how mathematical abstractions can be used to reduce explanations, I have been concerned with their role in producing these.

In the following, I summarize and extend some of the points from the papers about the role of mathematical abstractions in systems biology. To understand how these go together, it is important to emphasize that there are different dimensions of generality. In the context of logical positivism, the generality of laws has often been taken to mean unrestricted validity and an expression of necessary causal relations for a large scope of phenomena or explanations. As a result of these high normative standards for laws, many philosophers of biology have opted for a deflationary view of laws and theories, motivated by the diversity and complexity of organisms (Beatty 1995). But if we instead of the notion of laws as necessary (in contrast to accidental and contingent) and exceptionless take a more pragmatic view and examine the roles generalizations play in biology and other sciences, we might be able to make sense of different epistemic practices pursuing different explanatory aims. Mitchell’s (2005) ‘pragmatic account of laws’ rejects this dichotomy between necessary and accidental, and highlights instead how knowledge claims can vary in their stability and level of ontology.1 From this perspective, there may be laws in biology, contingency can come in different types and strengths (in physics as well as biology), and generalizations can serve important pragmatic aims in biology without representing any real-world target in detail. Whereas Mitchell’s main focus has been to delineate different types of contingency and to defend the necessity of an integrative pluralism to capture biological complexity, my aim has been to examine more closely what she calls

1 Mitchell (2005), like Cartwright (1983), questions whether even physics would be able to live up to the strong normative criteria for laws but she approach the issue from a different angle. Mitchell stresses that on a much larger time-frame, physical laws (even the laws of gravitation) are historically contingent to astronomical events. I remain agnostic to the definition and status of laws in physics and biology but share Mitchell’s (2005) interest in different dimensions of generality and law-like statements.

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pragmatic aims of generalizations. In particular, I have asked what explanatory virtues a more abstract analysis may offer in biology. Bertalanffy’s (1969) emphasis on isomorphic relations of form hintsat generality on a higher level of abstraction where general principles can play important explanatory and regulative roles even if the causal relations underlying the typified behavior differ in the systems described. Although the search for general principles implies a reduction of details for the sake of abstract clarity, it does not necessarily entail a reduction of context-dependent explanations to general statements.

In the following I outline a set of explanatory roles that general principles may take in biological research. As clarified in Section 5, Bertalanffy (1969) emphasizes that the search for higher order laws or principles in General Systems Theory has nothing to do with reductionism. In fact, it is quite the opposite. It reflects an acknowledgement of our inability to understand biological complexity without referral to some abstract tools that may be used to establish connections to similar systems that we know better. Bertalanffy (1969) describes the higher order laws in General Systems Theory as ‘explanations in principle’. With this notion he emphasizes that explanations can come in different kinds but also in different degrees. His description and examples reflect an ambiguity that on one hand points to the role of higher order formalisms to serve as steps towards more detailed explanations, and on the other can mean an identification of the ‘principal’ or fundamental higher order properties of systems. Rather than seeing this as an inconsistency in his account, Wolkenhauer and I (Section 5) came to the conclusion that Bertalanffy points to several epistemic virtues of general principles that researchers may alternate between, and that at the same time are different from the strategy of reductive subsumption (that may nevertheless still play an important role in some scientific fields). I summarize and further clarify these roles below.

The first virtue I wish to emphasize has also been articulated in causal or mechanistic accounts, and corresponds to Winther’s (2003) description of a gradual and piecemeal de-idealizing process of model diversification. The idea is that abstract formalisms can serve as tractable coarse-grained sketches or templates that aid the formulation of more detailed causal or (dynamic) mechanistic explanations (Craver 2007, Weisberg 2007a, 2007b, Boogerd et al. 2013).2 The formalization thus can be a starting point from which more precise parameters can be incorporated through iterative coupling of modeling and experimentation. It should here be noted that design descriptions can become surprisingly close to the actual workings of biological systems, so that the description goes beyond a loosely applied analogy. This happens if the functional constraints are tightly overlapping between living and designed systems (Csete and Doyle 2002, Doyle and Stelling 2006).3 Engineering

2 This function has affinities with Humphreys’s (2004) description of the use of theoretical and computational templates to generate knowledge in computational science (that biology is increasingly becoming a part of). As examples of theoretical templates, Humphreys describe the general Lotka-Volterra-equations (Humphreys 2004, Cpt. 3) that Bertalanffy (1969) also uses to illustrate the value of coarse-grained descriptions as starting points for more detailed descriptions. A comparison of similarities between Humphrey’s account and the use of general principles in systems biology is however beyond the scope of this dissertation. 3 An example is the recent discovery of functioning gears in insects that are strikingly similar to those in engines: http://scitechdaily.com/natural-example-functioning-gear-mechanism-discovered-insect/, accessed 22/12-2013. Whereas some of the designs in engineering have been inspired by biological systems, other designs have been long known in engineering and have only recently been discovered in biology. See also (Calcott 2014 forthcoming).

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approaches may therefore emphasize stronger or weaker similarities between the two domains. Should the model turn out to be less generalizable than assumed, it may nevertheless serve an important role in the clarification of the difference between designed and biological systems (Knuuttila and Loettgers 2013). In summary, this function shows how formalizations, through incorporation of specific parameter values and modifications of the model, can serve a role as sketches or templates for mechanistic explanations outlining concrete causal relations. This level is what we in Section 5 called the ‘representation level’ where explanation is aimed at a realistic interpretation of causal interactions.

In some cases the primary interest is the pattern of behavior, not the detailed mechanisms at work in concrete systems. Sometimes an abstract model, as a ‘minimalist idealization’, may be a goal in its own right (Weisberg 2007a, 2007b). When systems biologists use the notion of organizing or design principles, they are typically not aiming for a finer-grained description but rather pointing to what Levy and Bechtel (2013) call abstract patterns of causal connectivity. The work on network motifs, described in Section 4 and 6, is one example. The regulatory functions of network motifs are deliberately modeled through idealizing assumptions and the explanatory virtue of the associated formalisms is not strictly dependent on their ability to accurately represent all the target systems. One would perhaps expect that further research would aim for ever more detailed descriptions. This is not always the case. Drawing on the work of Alon’s group, other researchers have explored the scope of motif functions relative to different parameter values (Wall et al. 2005, Tyson and Novák 2010). Whereas this involves additions of some details, the primary interest often remains at a level where functions can be generalized within certain ranges of values. For instance, Wall et al. (2005,supplementary material) explain that their “reference parameter values were selected in the interest of exploring model behavior rather than in the interest of understanding the dynamics of specific natural systems”. The aim is to identify the scope of parameters where the function of the motif is invariant. Thus, generalizable functions, rather than accurate representation, are often in focus. By abstracting from details, systems biologists can build connectivity models of biological networks and identify generalizable operations appealing to the same type of organization within a range of parameter values. This higher-level analysis can be valuable for the evaluation and facilitation of knowledge and methods across research contexts. This level was referred to as the ‘functioning level’ in Section 5. At this level, the ideal dynamic explanation is to know whether, say, a general type of motif works like a persistence detector, a switch, an amplifier etc., but not necessarily to fully account for the causal details of concrete real-world systems. Identifying accurate parameters would make the model more realistic but also context-dependent, and this is what many engineering-inspired systems biologists hope to avoid. They are not interested in specific instantiations but features that are shared among systems with a specific function, and the range of organizational schemes that can support this feature. This epistemic aim is thus based on the assumption that there exist functional constraints

limit the range of possible organizational features and shapes relative to a given function, e.g. a certain range of shapes that will allow a bird to fly or a system to maintain oscillations over time (Jaeger and Sharpe 2014, in press). The strategy is thus to use formalizations to guide the categorization of different types of functional or organizational patterns.

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While these virtues seem compatible with heuristic strategies designed for mechanistic explanations, some scholars have compared the role of general principles in biology to that of higher-order laws in physics (Morange 2005, Braillard 2010). In this framework, what I have called general principles are interpreted as non-causal explanations concerned with constraints rather than temporal processes. Therole of abstract principles is argued to be analogous to e.g. the ideal gas law that does not causally explain but determines (in a mathematical sense) relations between pressure, volume and temperature. Although the effects are causally mediated, in this view general laws and principles identify the range of possible and non-possible models, given specific conditions. Braillard (2010) compares this explanatory virtue to Sober’s (1983) equilibrium explanations that it are argued do not provide causal explanations of evolutionary events but rather explain why alternative causal routes are not possible, e.g. that the sex ratio would have been the same regardless of what causal scenarios preceded the observed event.4 One can therefore interpret some law-like mathematical abstractions as definitions of general relations accounting for a class of systems operating under similar formally defined constraints that do not causally explain but rather set the boundaries for causal explanations.5 Braillard (2010) distinguishes between different types of non-causal principles but gives special priority to what he calls design explanations (see table in Section 2). Design explanations denote the functional dependencies between a system function and system structure, e.g. the possible structural designs underlying specific functions (e.g. Alon’s network motifs). Laws of co-existence are exemplified in growth equations signifying e.g. surface/volume ratios and the relation between metabolic rate and growth types (Kleiber 1932, Bertalanffy 1950a, 1950b). Finally, principles of self-organization are found in abstract cell models in systems-theoretical branches of systems biology (Rosen 1991, Hofmeyr 2007). Braillard’s description correspond to what Section 5 defined as the ‘organizational level’. This level is concerned with abstract properties or relations that condition a specific operation, although the underlying causal relations (or mechanisms) may vary.6

I am not ready to offer an account on the relation between causal and non-causal explanations, since this would require an analysis of different accounts of causation and establishment of criteria for causal analysis. I therefore avoid making claims about causal- or non-causal characteristics of general principles, or about how well mechanistic accounts can accommodate the search for general principles. I however agree with Braillard (2010) that by grouping all biological explanations under one category we run the risk of overlooking the different epistemic roles that explanations on different levels of abstraction play in research. Sometimes de-idealizing general models to investigate causal interactions at the level of molecular mechanisms is important; sometimes it is not the aim. What interests me is to understand why more accurate and detailed explanations are not always the goal. For understanding biological practice it is therefore important to stress that formalizations used in biology differ in their ontological anchoring and have different explanatory forms and targets. These

4 For further discussion on the relation between equilibrium explanations and non-causal explanations and laws, see (Hamilton 2007, Braillard 2010, Gross 2011).5 As mentioned in Section 8, Richardson (2001) categorizes Kauffman’s analysis of Boolean networks in a similar way and draws on the difference between different types of explanations in physics. 6 Huneman (2010) draws very similar conclusions by examining what he defines as ’topological’ explanations in ecology and sciences focused on network modeling. Topological explanations are described as different from mechanistic explanations in ignoring the causal basis for the properties studied (e.g. fitness properties represented in phase space).

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differences raise the question of whether one can categorize the different principles according to the different roles described. Although such a categorization would be philosophically appealing, I consider the examples to be too plastic to allow for such clear labeling. The role of specific general principles is of course partly an inherent feature of (constraint of) the formal framework they are identified in - which can be more or less abstract. Organizing principles formulated in the framework of mathematical general systems theory or category theory would typically be more distanced from causal analysis of concrete systems than an engineering approach like the ones described in Section 4 and 7. But some examples span all of these levels. Braillard (2010) sees Alon’s network motifs as examples of non-causal design principles based on the rigid framework they were first defined in. Here, the mathematical models display how functions constrain the possible structures and vice versa. But sometimes the fruitfulness of network motifs is also defended as a starting point for identifying specific regulatory mechanisms (Alon 2007b). This happens when the framework is combined with experimental research regarding specific metabolic control mechanisms (Section 4). What role general principles have therefore not only depends on the internal constraints of different methodologies but also on the use of these in practice. The plasticity of general principles does not necessarily imply vagueness but also a flexibility that allows mathematical abstractions to operate on a middle ground between mechanistic explanations and vacuous theories.7 Some of the examples studied in this thesis are distant from the study of particular causal interactions (e.g. the properties of scale-free networks mentioned in Section 1), whereas other examples show how mathematical abstractions are used to outline the explanatory relevance of some, but not all, properties (e.g. Section 4 and 7). Combining the different levels of analysis can be productive because the mathematical abstractions place constraints on the causal processes and provide a higher-level clarify of different types of systems, whereas a detailed account is needed to explain the specific instances of a given property.8

Morange (2005) and Braillard (2010) both point to an aspect of explanation that is often overlooked, namely the explanatory relevance of constraints that indicate what cannot happen. Constraints do not explain how a mechanism is produced through interactions between component parts but denote the boundaries of such processes. As argued in Section 8, such constraints may serve a generative role in shaping biological processes. The DS theorist Huang states that: “[T]he wonderful diversity of cellular types is perhaps not achieved by an active process but instead the result of the limitation of ‘entropy’… Thus, the apparent high degree of ‘organization’ that we associate with complex living systems may actually simply reflect the deficit, or frustration, owing to internal constraints, of the noise-drive process in maximizing (a kind of) ‘entropy’ that seeks to realize all mathematically possible gene-expression configurations” (Huang 2011a, 2256). This view has close affinities with Kauffman and Goodwin’s ideas about inherent mathematical properties of complex system architectures that constrain developmental and evolutionary processes in a way analogous to how

7 Similarly. I see no need for a clear ontology of what ‘systems’ in systems biology means – there are good reasons for having a systems-concept that is flexible enough to cover systems of different kinds and sizes (cf. O’Malley and Dupré 2005). 8 Huneman (2010) calls this function ‘a constraining topological explanation’ and provides a rich analysis of the importance of such explanations in biology. I prefer to reserve the notion of topology to studies of network architecture, since this is how systems biologists typically conceive the term.

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laws of motion constrain the possible planetary movements (Kauffman 1993, Goodwin 1994). This aspect is relevant for understanding how formalizations can guide the production of biological explanations. In Section 1.5 and 5, I pointed out that theorem proving is used in systems biology, although this strategy is not very widespread in the life sciences. Because of the complexity of biological systems, it is hard to see how theorem proving could be a useful investigative strategy in these fields. To fully describe the role of theorem proving would require a deeper analysis than I can offer at this point, but reflecting briefly on the use of theorem proving throws some light on the explanatory virtue of design- or organizing principles in constraining the problem space of analysis.

Wolkenhauer et al. (2012, 59-60) describe the role of theorem proving in the following way: “In systems biology, theorem proving identifies conditions, which any member of a system’s model class must have for a given system’s property to hold. […] An organizing principle in this context specifies a category of systems to which a model belongs to; it describes an essential characteristic feature, a rule or law of which the function identified is an instantiation”. Wolkenhauer and colleagues outline similarities between theorems in mathematics and in biology. In the well-known Pythagoras theorem, the relations between the sides of a triangle with a right angle are restricted to a2+b2=c2, given certain assumptions (e.g. that we are not talking of triangles in spherical geometry). This description is part of our definition of triangles and so constrains the possible geometrical operations. Similarly, biological properties such as robustness can in some cases be articulated as mathematical constraints on the space of possible mechanistic models, given specific characteristics of the biological system. To make this clearer, consider the work of Eldar et al. (2002) that was described in Section 7. The case exemplifies the use of mathematical models to identify a large spectrum of function-precluding models for robustness of developmental processes in Drosophila. To identify the mechanism (in terms of network interactions) underlying the observed robustness against fluctuations in gene dosage and expression, Eldar and colleagues formulated a general ODE-based model of the dorsal patterning network, including a subset of regulatory proteins. In their model robustness was mathematically conceptualized as the steady-state distribution of a specific bone morphogenic protein against perturbations. They carried out extensive simulations (over 66,000) of different model networks based on random parameter sets (rate constants, protein concentrations and diffusion properties). Asmall number (about 0.3%) of the network models exhibited a high degree of robustness, comparable to the level detected in preliminary experiments. The properties of the mathematical model could be translated into biological properties, associated with the generation of a concentration gradient of a BMP inhibitor that were later demonstrated experimentally. What Wolkenhauer and colleagues stress is that theorem proving can take a similar role in identifying the function-precluding class of models - thereby constraining the problem space of relevant empirical hypotheses to investigate. In some cases theorem proving may even outline the necessary conditions for a system to display a specific capacity. To illustrate this point I mention an example that is used by Wolkenhauer et al. (2012) to clarify the similarities between theorems in mathematics and biology. 9

9 Similar examples of theorem proving in systems biology are Russo and colleagues’ proof of the features that make a transcriptional system capable of synchronizing oscillations with periodic inputs (Russo et al. 2011), and Wolkenhauer and colleagues’ stem cell niche dominance theorem (Wolkenhauer et al. 2011).

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Shinar and Feinberg (2010, 2011) use theorem proving to identify the necessary requirements to formally distinguish designs that work from designs that cannot work, or that might almost work, relative to a biologically relevant property. Specifically, they define the necessary and sufficient conditions for biochemical networks capable of displaying absolute concentration robustness (ACR) relative to a given molecular species, provided the assumptions of the mathematical definitions of the network and ACR conditions. ACR means that the concentration of a particular substance must be the same in all the positive steady states of the system, regardless of the concentrations of the other constituents of the network. No (known) biological system displays this property but many biological systems display a high degree of robustness against fluctuations in the concentrations of the other molecular components that make up the network. The hope is therefore that the theorem derived for the idealized situation can generalize a set of properties (i.e. structural constrains in the architecture of the networks) relevant for robust biochemical networks. Shinar and Feinberg (2010, 2011) draw on the framework of chemical reaction network theory to generate reaction diagrams signifying the combinations of chemical reactions (represented as arrows). First, they formulate a theorem that unites architectural attributes of networks with absolute concentration robustness. These properties represent the extent to which the constituent molecules of the network can be built by chemicalreactions within this network. This property is called the degree of deficiency and is a measure of the linear dependency among the reactions of the network. Building on these insights, Shinar and Feinberg investigate whether networks with a deficiency of zero (i.e. linear independence among the reactions of the networks) can display ACR. They come to the conclusion that this is impossible, regardless of the values of the rate constants. One the other hand, if a mass-action network has a deficiency of one and there are distinct non-terminal complexes that differ only with respect to the molecular component s, then the system has absolute concentration robustness relative to s, regardless of the other parameter values (Shinar and Feinberg 2011, see also Gunawardena 2010).

These rigid conclusions are of course relative to the assumptions made about the properties of the network and may therefore not be directly applicable to investigations of more complex reaction networks in biological systems. Yet, by delineating necessary and sufficient conditions for the simple toy examples discussed, the strategy may provide productive guidelines for important structural features of robust biological networks: “[T]he theorem presented here describes a general class of core subnetworks, which, taken by themselves, do give rise to ACR and which, to the extent that they approximate their more completely articulated parent networks, may confer to the fuller systems imperfect yet strong robustness” (Shinar and Feinberg 2010, 1391). Thus, the framework can be used to make explicit what robust networks have in common and to suggest relevant properties that can be investigated experimentally. Theorems can therefore serve a heuristic function; without fully determining the biological problem space, knowledge on necessary relations in idealized systems can serve as partial constraints on more complex problem spaces (cf. Nickles 1990). The identification of the function-precluding class of models is a limiting constraint but it has a productive aspect in pointing to the possible conditions under which a biologically relevant property is realized. As the work of Eldar et al. (2002) shows, this categorization is not always a derivational consequence of what is already known about specific systems but can be used as a search tool to identify relevant and

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yet unexplored systems properties or mechanisms. In the following section I reflect on the associated regulative roles of general laws or principles.

9.3. On the regulative role(s) of general principles With the notion of regulative roles I point to features that operate on a meta-level compared to specific instantiations of biological explanations and are associated with the development of explanatory frameworks. Theorizing in science is not only about explaining phenomena but also a matter of making sense of and organizing our theories about these phenomena. Cartwright states that: “Onething that ceteris paribus laws do is to express our explanatory commitments. They tell us what kinds of explanations are permitted” (Cartwright 1983, 48). According to Cartwright, abstract laws are the principles “that best organize our theorizing”, because they distill and clarify research results. But since Cartwright’s main focus is to demonstrate that the received view of laws in physics as true and universally applicable is mistaken, how they organize remains to a large extent unanalyzed. Morrison goes on to emphasize how mathematical abstractions “enable us to conceive of systems as being of a particular type, exhibiting certain kinds of behavior that allow us to classify them in terms of the general features” (Morrison 2009, 127). Similar formulations can be found in Berger’s account of dynamic explanations where she argues that for a particular type of explanation in biology, many causal details are explanatorily irrelevant. If, for instance, the aim is to understand general features of population dynamics: “[…] explanation is at least partially about organizing our knowledge in coherent and productive ways” (Berger 1998, 330). Organizing or design principles in systems biology constitute examples of types of systems with specific behaviors. This role of mathematical abstractions in empirical sciences is, as noted by other scholars (Giere 1988, Humphreys 2004, Morrison 2009), akin to Kuhn’s notion of exemplars (Kuhn 1970/1996). Kuhn described how exemplars play an important role in scientific training where these represent characteristic types of solutions to typified problems and ease the solutions of a new problem through similarity relations to the well-known exemplars. Exemplars also play important heuristic roles in research where new puzzles can be partly approached through the problem-solving space of well-known problems. In systems biology, a characteristic feature is that such exemplars are adopted from other disciplines such as physics and engineering for exploration of their applicability.10 Thus, explanatory frames and methodologies from other disciplines are used as heuristic strategies to explore the similarity between the dynamics of engineered, physical and biological systems. Importantly, the point stated here is different from the so-called “familiarity account” of scientific explanation suggested by Bridgman and others (summarized and criticized in Friedman 1974). The account proposes that to gain scientific understanding, unfamiliar phenomena need to be related to and derived from familiar ones. Although intuitively appealing, a problem arises if the subsumption of the unfamiliar cases to the familiar and fundamental laws is taken to be the essence of explanation. Friedman (1974) objects to this view by pointing out the absurd consequence that theories with

10 This way of articulating the transfer of higher-order formalisms in systems biology was suggested by my colleague Mads Goddiksen. I find this description helpful. One disadvantage of seeing organizing principles as examplars is, however, that the notion of examplars is tied to the theoretical framework of Kuhn’s account. As Nickles (1990, 24) notes, it is not a part of Kuhn’s account how examplars are redefined through interdisciplinary ‘bootstrapping’, so a different term for these functions may be preferable.

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components unrelated to derivations from fundamental and more familiar relations cannot be explanatory. But if we, instead of taking familiarity relations as necessary and sufficient conditions for explanation, view the transfer of frameworks for problem definitions and problem solution as a heuristic strategy, innovation in interdisciplinary research can be viewed as a matter of exploring the applicability – and possible extension through integration - of explanatory frameworks from other disciplines. For instance, biologists may use engineering design principles as a search tool for mechanisms in biology and vice versa. The heuristic strategy is a methodological reduction whereby something complex and unfamiliar is tentatively assumed to operate in a similar fashion to a known phenomenon. This does not entail a theoretical or an explanatory reduction of the unfamiliar to the familiar, but only uses the familiarity relations as a starting point for analysis or to bring clarify to our theorizing by highlighting theoretical patterns (see also Nickles 1996).11

The regulative ideal of general principles can be understood as a matter of establishing connections between higher-level descriptions of different systems and as a way to unify science. This role is similar to the one described by unificationist accounts of explanation, stressing the value of establishing connections or common patterns in different contexts (e.g. Kitcher 1989, 432, Winther 2009). Common explanatory patterns, or exemplars, help our understanding of unexplored systems with similar properties, and empirical resistance to unification can inform about the causal structure of the world (ibid 1989, 500). Nonetheless, my aim has not been to defend a view that gives the explanatory approach priority over unification and generality, but rather to evaluate the heuristic and regulative roles of general principles, given that these are emphasized in some research contexts in contemporary practice. Rather than seeing explanations as general and deductive argument patterns, I want to leave it open as to what can count as an explanation in different research contexts. Thus, the view I submit does not assume that unification is the “essence of scientific explanation” (Friedman 1974, 15), but just one of the important regulative roles of general principles. In addition, the generality of the principles need not be the result of a subsumption strategy but of abstraction. The high level of abstraction, rather than reducing functions between descriptions, is what affords a transfer of modeling frameworks across disciplinary domains. The end result is therefore not one coherent unitary world picture but something that probably rather resembles the opportunistic patching together of different frameworks in an integrative pluralism (Mitchell 2005) but where the coarse-grained patterns become visible as we move to a higher level of abstraction. To describe similar systems in biology, the general dynamic or ‘cybernetic’ model may be the same although thecausal models may differ. Generality does therefore not provide the only end point of scientific analysis but is one strategy among many where formalizations can serve as investigative tools tohighlight and clarify similarities of form regardless of materiel context dependency (Griesemer 2012). This strategy does not imply that context-dependencies and specific parameters become irrelevant for explaining particular biological systems. Rather, the quest for general principles can be seen as

11 I distinguish between three types of reduction: i) Theory reduction as the reduction of one theory to another, e.g. of an explanation in biology to a more general theory in physics, ii) Explanatory or causal reduction where higher-level phenomena are explained from lower-level interactions, e.g. explaining phenotypic traits in terms of changes in gene frequencies, and iii) Methodological reduction as a strategy to approach a complex system in analogy to a simpler or already known phenomenon, e.g. by decomposing a biological system to the workings of the parts or to abstract from details to search for topological patterns.

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complementary to, and even supportive of, the development of finer-grained and more context-dependent explanations. Abstract formalizations in systems biology can thereby be particularly useful when the system is too complex to be handled by bottom-up analysis and the theorizing becomes too fragmented for answering general questions.

I have defended the epistemic value of the search for general principles in biology but also highlighted that this heuristic and explanatory strategy cannot stand alone. To gain a full understanding of biological systems, experimental approaches that investigate the detailed causal relations, operating in concrete systems, are indispensable. The systems biology community has in recent years widely debated and acknowledged the potential pitfalls of abstract network inference methods if unconnected to experimental practices. Automated pattern detection, relying on software recognition of quantitative patterns in large datasets, has sometimes been promoted as an ‘unbiased’ research strategy. In contrast to this view, philosophers and practicing systems biologists have criticized the reliance on low-quality datasets, and on equally biased demarcation criteria for modular decomposition into specific patterns (Krohs 2010, 2012, Leonelli 2012b, 2014 in press). Krohs refers to this type of research as ‘convenience experimentation’. This is not a criticism of data-intensiveresearch as such but of a practice that draws grand conclusions based on a modeling approach that is dissociated from the experimental study of biological systems, except for the use of ‘convenient’ but noisy data from automated experiments.12 To overcome such problems, reverse engineering methodologies are increasingly combined with investigations of the molecular operations underlying the capacities denoted by the more general design principles. Section 8 emphasized how some researchers aim to use reverse engineering methodologies to integrate experimental and theoretical approaches to development and evolution. But there are of course many different ways to do this that are currently under investigation.

One of the major challenges in this PhD project has been to keep up with the literature published in and about systems biology. During the last three years, systems biology has not only grown in size but also developed into new branches such as evolutionary systems biology and systems medicine. In the following sections I outline further possibilities and challenges associated with the recent extensions of systems biology. I first clarify the motivation for these extensions and then reflect on the merits of the methodological developments initiated and needed to fulfill the ambitions of these fields. In particular I examine the ramifications of advanced whole-cell or whole-organism models that by some is argued to be the future of biomedical research. This type of modeling challenges the assumed tradeoff between virtues of models such as realism, generality and precision, and raises new questions about alternative decompositional and recompositional strategies to build in silico modelsof whole biological systems. After reflecting on the prospects of such strategies, I outline recent

12 Some have seen data-driven science as an oxymoron because there is no scientific methodology free of theoretical assumptions. But scientific inquiries can be more or less rooted in theory, and as exemplified in Alon’s work, the search for largely unspecified patterns can lead to important insights. Instead of the notion of data-driven research, the notion of ‘data intensive research’ is often preferred. To emphasize the lack of theoretical grounding of some scientific inquiries, the notion of ‘exploratory experimentation’ has gained in different scientific contexts (Steinle 1997, Elliot 2007, O’Malley 2007, Waters 2007).

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developments and ambitions towards a personalized systems medicine relying on digital patient models (Section 9.5). In light of the challenges for large-scale modeling, I return to the role of systems-theoretical and engineering-inspired strategies in medical and evolutionary studies (Section 9.6-9.7).

9.4. Aiming for completeness: whole-cell models So far I have assumed a tradeoff between the virtues of various models because all aspects of a complex system cannot be modeled and analyzed simultaneously (Levins 1966). New projects in systems biology aim to break this tradeoff through the ‘epistemic enhancement’ of computer simulations (Humphreys 2004, Karr et al. 2012). The aim is to integrate the fragmented pieces of data and knowledge in in silico models of whole cells, whole organs or, in time, whole multicellular organisms. The dream of a complete whole-cell model is not new (Crick 1973), but it is only recently that the required resources, such as biological information and computational power, have reached a point where the dream may be turned into serious research projects. The motivation behind such projects is not hard to understand; Rather than drawing on problematic idealizations for the sake of simplicity, the hope is to incorporate as many details as possible in order to create a complete representation of biological systems. Complete should here not be understood in terms of material replicates but in terms of mathematical descriptions that are maximally detailed, given the knowledge available, for prediction and causal analysis of detailed molecular interactions. If successful, such simulations will not only allow researchers to mimic the behavior of biological systems in silico but also to observe effects of interventions on the model, and thereby to access in simulations what cannot be obtained through experimental research due to practical challenges or ethical reasons. Simulations in this context therefore are not only expected to yield predictions by black-boxing causal links, like most simulations known in biology and climate science today, but also to allow for a profound mechanistic understanding of all causally relevant processes.

In 2001, Kitano envisioned that the modeling of the ‘human systeome’ was soon to be a realistic option. Such a model would provide a “detailed and comprehensive simulation model of the human cell at an estimated error margin of 20% by the year 2020, and to finish identifying the system profile of all genetic variations, drug responses, and environmental stimuli by the year 2030” (Kitano 2001, 25). How close are life scientists to realizing these ambitions? The results of the first (and so far only) whole-cell model (WCM) were announced last year in a joint publication by researchers from Stanford University and the Craig Venter Institute (Karr et al. 2012). The WCM computational model simulated the life cycle of the human urogenital parasite Mycoplasma genitalium that is the smallest functioning organism found in nature with only 525 genes. I shall use this example to clarify the challenges of modelling biological systems in simulations at the highest possible resolution. Despite the simplicity of Mycoplasma genitalium compared to other model organisms, creating the WCM requires an astonishing integrative effort and includes a synthesis of the results of over 900 publications and more than 1,900 experimental parameters. Integration in this context does not mean to build one unified model bottom-up but to incorporate the outputs of a mosaic of different models in one simulation. There exists no single computational model that can encompass the variety of biological processes from transcription to metabolism to protein folding etc. Accordingly, the WCM is not one large system of similar equations but consists of many different types of models interpreted

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by a computer algorithm that numerically integrates values produced by the different models. The researchers functionally decomposed the system into 28 modules, corresponding to different cellular functions.

The 28 modules were modeled separately, according to the modeling tools best suited for the specific processes and related to 16 types of cell variables (left column on Figure 9A below). The next step was to integrate results based on different modeling techniques such as ODE modeling, Boolean networks and constraint-based modeling (such as flux balance analysis). Accordingly, the accuracy of estimates for the different models also differed significantly. Karr et al. (2012) describe the integration of submodels as the key challenge of the project. To make integration of these different models possible, the researchers relied on the assumption that the interactions between modules can be neglected for short time-scales (1s interval presented as dark arrows on the figure below). For each second, the output values of the submodels are retrieved for the cellular variables, and these values are updated in the next simulation round to display the temporal process of a cell cycle.

Figure 9A. Illustration of the principle behind the whole-cell model of M. genitalium. The left column lists the 16 types of cell variables according to 28 submodules representing specific processes assumed to be independent on short timescales. See text for further clarification. Source: Karr et al. (2012).

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The predicted growth dynamics, including concentrations of specific metabolites and cellular chemical compositions, were evaluated in comparison to independent experimental data that were not used for construction of this model. The model was able to recapitulate many significant aspects with a reasonable degree of accuracy compared to experimental findings.13 From a more general perspective this modeling effort provides one of the few examples of an attempt to completely recompose the information generated in molecular biology, biochemistry and systems biology. In this process, large-scale models not only accumulate models, but can also serve as vehicle for generation of new knowledge because such simulations in themselves constitute a kind of experimental system that can be explored (Winsberg 2003, Carusi et al. 2012). For instance, whole-cell models can facilitate discoveries regarding e.g. how disruption of genes can affect growth rate, and through iterative cycles with experimentation they can produce more accurate estimates of parameters determining phenotypes (Sanghvi et al. 2013). In addition, the WCM has been used to examine the effects of adding genes to the host genome in synthetic biology, opening the way for computer-aided design of microorganisms with properties such as the ability to metabolize pollutants (Tomita 2001, Karr et al. 2012). Research projects have been initiated to build a similar whole-cell model of E. coliwhose genome is almost an order of magnitude more complex than that of M. genitalium. To realize this goal, model organism databases are absolutely central. Such requirements and challenges will be outlined in the following section. But before that I want to flag some possible prospects of large-scale modeling for future medical research and practice.

9.5. Systems biology - the future of medicine? Parallel to the attempt of modeling whole-cell models of bacteria, several large-scale modeling projects take up the astonishing challenges of modeling human organs or even the whole human body. Examples are the Blue Brain Project, E-Cell, The Silicon Cell Project, The Virtual Cell Project, the Virtual Liver Project and the Virtual Physiological Human.14 The shared aim of these projects is a detailed understanding of all relevant causal interactions that link processes on lower and higher levels (Snoep and Westerhoff 2005, Wanner et al. 2005, Hunter et al. 2010, 2013). The ambitions for such projects are high. In the official video for the Blue Brain Project it is stated that in the new in silico neuroscience there will be “nothing we cannot measure, no aspect of the model we cannot manipulate, and there will be no question we cannot ask”.15 The prospects of medical applications of such modeling efforts are tantalizing, and initiatives to support research towards personalized medicine have been initiated. Ultimately the hope is that it will be possible to create digital models of individual patients and thereby to target prevention and treatment of disease much more effectively.

13 For instance, the model predicted the cell cycle to take 8.9 hours, compared to experimental observations of 9 hours. The accuracy of the predicted number of essential genes compared to experimental observation was 79 %. For further comments on these results see Gross (2013, 166). 14 More information on the projects to be found at the following webpages: http://bluebrain.epfl.ch/, http://www.e-cell.org, http://www.siliconcell.net/, http://www.nrcam.uchc.edu/, http://www.virtual-liver.de/ and www.vph-noe.eu. All databases accessed 12/12-2013. 15 Video available at the Blue Brain project webpage, http://bluebrain.epfl.ch/, accessed 27/12-2013.

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Among the common problems in medical practice are different responses to treatment among patient groups, the side-effects of drugs, and that serious illnesses are discovered too late (Weston and Hood 2004). As systems biology expands into systems medicine, hopes are high for the development of a personalized medicine where the dosage and combination of drugs are tailored to individual patients. Furthermore, the aim is to develop a health care system that to a much higher extent is capable of predicting, and thereby preventing, future diseases (Hood et al. 2004).16 Hood and Flores (2012) outline the prospects for what they call P4 medicine. P4 stands for predictive, preventive, personalized and participatory, and the goal is set for a consumer-driven medicine focused on prevention of individual disease states rather than the hierarchical approach of evidence-based medicine capturing effects of treatments only at the level of the statistical average. The basis for such a P4 medicine is the increasing availability of low-cost data in genomics, metabolomics and proteomics that, if their expectations hold, in time will be converted to disease-relevant information that not only physicians can interpret but also allows for every-day self-monitoring of current health states with hand-held devices. The European Commission has from October 2011 initiated a project to set out a roadmap for the ‘digital patient’, a computer program that will generate a virtual version or “3D avatar” of individual patients with the aim of simulating disease processes and drug responses to make medically relevant predictions at the level of the individual patient. As the basis for the development of such amodel the project called The Virtual Physiological Human (VPH) aims for an integrated and systematic understanding of biological processes spanning all relevant levels and time-scales through the use of mathematical modeling (Hunter et al. 2010, 2013, Moss et al. 2012).17 The starting point for the VPH project in the years 2007-2013 has been to divide the labor among different institutions specialized in specific body parts or diseases, and the hope is that these results can be integrated in models that allow for computer assisted prediction at the level of individual patients. The effects of the project are not only the long-term dream of a “complete patient model” with a resolution akin to the whole-cell model of M. genitalium. So far projects under the label of computational physiology have aimed for developing coarser grained computer models, such as heart models, that allow for simulations of normal and diseased states and for observation of the effects on drugs (Carusi et al. 2012, Hunter et al. 2010).18 These results provide some optimism that medically relevant result can be generated from models that are very complex but not to the extent that they reproduce biological complexity completely. Thus, the success of the VPH project is not to be judged upon the success of reaching the vision a complete and all-encompassing model of the human body but by the advancement of models that can predict health states and thereby allow for prediction and more effective treatment of diseases. Through the integration of results from molecular biology, systems biology and digital imaging it may for instance be possible to digitalize some aspects of drug testing

16 Strictly speaking, systems medicine is much more than an extension of systems biology. As Clermont and colleagues(2009) emphasize, a great part of systems medicine draws on clinical medicine and population epidemiology. For a sociological analysis of expectations associated with personalized medicine based on the history of western medicine, see (Tutton 2009). 17 For more information, see www.digitalpatient.net and http://www.vph-noe.eu/, accessed 15/12-2013. 18 Such models for instance rely on a homogenization principle in which a block of cells (rather than single cells) are considered the smallest units of the systems and many biochemical details are not included. Still, these models are extremely complex and, as argued by Carusi, Burrage and Rodríguez, the setup is not one model related to a human invivo target but a whole model-simulation-experiment system (MSE system) intertwined in complex representational relationships (Carusi et al. 2012).

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and thereby limit the risks of experimental treatments, unforeseen side-effects, and the need for animal testing. Furthermore, the digital future of medicine may allow for prevention of diseases that today typically are diagnosed too late to avoid severe suffering. If the aims of systems medicine are reached within the timeframes aimed for in the different projects, it may even happen in the near future. Hood and Flores (2012) envision that in 2022 every patient will have their whole genomes sequenced as part of their medical journal, and that self-monitored blood-sample testing, akin to blood-sugar tests used today by diabetics, will be commonly used to detect biomarkers that precede diseases.

It has taken some time for systems biology to develop clinically relevant results. Early studies of biological networks focused mainly on network architecture and despite the advances in systems biology, there still exists a chasm between the evaluation of insights developed in the last decade of systems biology and the clinical benefits of these results.19 The emergence of systems medicine is aimed at bridging this gap between systems biology and clinical practice. Whereas systems biology so far has mainly focused on the modeling of molecular networks, the emergence of systems medicine emphasizes that the choice of modelling strategies should involve considerations of aspects that increase the understanding of multi-level diseases, a goal that is ‘rationally bounded’ by social factors and perspectives regarding the time-frames for generating clinically relevant result. Systems medicine is an approach that at the same time aims for generality (i.e. understanding fundamental relations between disease states and clinical or molecular measures) and for prediction of specific disease states building on knowledge of variability among individual patients. For this purpose, the network view has been emphasized as a fruitful framework that can facilitate integration and modeling of big datasets and re-conceptualize living systems in terms of dynamic effects of nonlinearly interacting components. Network medicine is an important aspect of systems medicine and is – as a potential precursor of P4 medicine – aiming for a more individualized medical practice and an integrated view of diseases. Network medicine promises the identification of “diseasomes”, i.e. subnetworks for specific clinical pathophenotypes (Barabási et al. 2011). The approach incorporates knowledge about the organizing principles of complex networks and the increasingly rich datasets on human-specific molecular interaction data as a starting point for the development of models of ‘disease modules’. Disease modules represent a group of network interactions and network components associated with a specific disease phenotype. A disease module is not necessarily identical with topological and functional modules, but it is generally assumed that there are overlapping features among functional modules and modules affecting the loss of a given capacity in the system. Much effort has been put into identification and prediction of disease genes (for an overview of methods, see Barabási et al. 2011), and a surprising level of overlapping connections between different disease modules have been identified. This suggests that many diseases are more interconnected than typically assumed.

Disease networks (shown as the middle layer on Figure 9B) display nodes as diseases linked by shared disease-associated genes. These can be used as starting points for investigations of links between diseases, and to develop a patient-oriented network pharmacology where therapies can be more

19 European Commission (2011). European Perspectives on Personalised Medicine. Luxembourg,http://ec.europa.eu/research/health/events-06_en.html. Accessed 17/12-2013.

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effectively directed at multiple targets and be adapted in accordance with the higher sensitivity of individual diagnostics. Barabási and colleagues (2011) argue that there is a strong need for understanding the combinatorial disease mechanisms, even for classical Mendelian diseases such as sickle cell disease. The disease is caused by a single point mutation in the -hemoglobin gene (where glutamic acid is substituted by valine at position 6), but the pathophenotypes among different patients vary from mild unsymptomatic anaemia to acute chest syndrome and strokes. This is one example of why more knowledge of individual or patient-group specific effectors for disease-modifying processes are needed to provide better treatments. This involves an analysis of the organizational aspects of larger systems and thereby an analysis beyond ‘disease genes’. An example of what the broadened perspective implies is recent research on obesity that shows how the disorder is associated with genetic and epigenetic factors influencing the broader class of metabolic networks and protein-regulatory networks (lowest level on Figure 9B). Furthermore, obesity is associated with seven other diseases, such as diabetes mellitus and asthma (middle layer on Figure 9B) that often need to be taken into account during treatment.

Figure 9B. Illustration of layers of networks, or a ‘network of networks’ relevant for understanding diseases such as obesity. Source: Barabási 2007.

Barabási further argues that in order to understand the spread of obesity in global and local contexts, we also need to understand influences operating on the highest level of social links, family ties and a range of environmental influences (upper level on Figure 9B). What is needed, in Barabási’s view, istherefore a framework where knowledge of the organizing principles of such network scan be used to develop models that can incorporate data with different levels of detail, even specified according to individual patients. Network medicine may provide a framework for predicting the effects of

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medical (and possibly engineering-inspired) treatment. As a further extension of systems medicine, O’Malley (2011) reflects on the prospects of constructive personalized medicine. This possible future approach builds on the integration of synthetic biology with personalized medicine towards the development of material biological constructs, which have the capacity to suggest medical interventions that may be used to ‘rebuild’ healthy states. As examples of such future biological constructs in medical practice, O’Malley (2011) mentions tumor-attaching bacteria and modified pancreatic stem cells that enhance the construction of normally functioning cells in patients with diabetes. These approaches are still far from the stage of clinical trials but may be possibilities in the near future.

With the implementation of methods from systems biology and synthetic biology in medicine, we may face a brave new world of medicine – with mind blowing new possibilities but also social implications. Needless to say, there are many social and political aspects of the implementation of personalized medicine, not least regarding privacy and security in the accumulation and access to a vast amount of medical data for individual patients. Other implications are changes in disease concepts, shifts in the conceptualization of the relation between society and the individual, and issues of social responsibility as medicine becomes democratized, consumer based and ‘participatory’. Thevision of P4 medicine is focused on cost-effective improvement of wellness and partly self-monitored prevention of disease. Hood and Flores (2012) highlight several times that the aim of P4 medicine is to “quantify wellness and demystify disease”. But it is unclear what this means, and whether a quantitative perspective on wellness and medicalization of everyday life through self-monitoreddevices will provide a more ‘holistic’ health system. I believe that these questions will be among the major issues in future biomedical practice and will require inputs from philosophers, historians, sociologists, policy makers and scientists alike.

Before the visions of personalized medicine can be realized, there are many challenges to face, and many epistemological issues remain to be better understood and explored. Even if it has been possible to model a simple organism, as seen in the previous section, it does not follow that the development of complete digital patient models is realistic within the time-frame set for the prospective projects.It is even unclear whether it would be possible in principle, and how such complex models can be validated. The question of representational relations becomes increasingly difficult to address as models span multiple scales (spatially as well as temporally). How far can the resolution and size of models be up-scaled before they become too complex to understand? Would digital patient models of high resolution increase our understanding, or would a more useful option be to aim for a coarser grained level? As outlined in Section 5, a major concern as models and simulations become increasingly advanced is the growing complexity and thereby intractability of the models themselves (Mesarovi et al. 2004; Wolkenhauer and Green 2013). Since it is not possible to construct an analytical model encompassing all the details of a cell, even when modeling the simplest organism using advanced computer technology, researchers have to rely on decompositional strategies (Karr et al. 2012). Modelling different processes across multiple levels of organization requires a large set of strategies for decomposition and integration of model components, and the proper way to integrate and ‘recompose’ the vast amount of information is a major issue in the emerging field of systems medicine.

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The challenge is not only practical and technical but also theoretical, since to infer explanations from simulations in itself requires a theoretical foundation for the choice of tools for modeling and analysis (Krohs 2010, 2012). As the system of analysis gets bigger, these choices become increasingly critical. The main problem is that biological systems are spatially inhomogeneous, and ordinary differential equations are not sufficient to capture the spatial coupling of biochemical and biophysical interactions. Partial differential equations and agent-based simulations can better handle these challenges but quickly lead to intractable models. As we saw in the previous section, Karr et al. (2012) dealt with this problem by dividing the system into 28 submodels and assumed that the processes operating in these submodels are independent on short time-scales. But it is an open question whether a similar strategy can be used to model complex eukaryotic organisms. In Section 5, Wolkenhauer and I compared the aim of large-scale modeling and Borges’ and Carrol’s fictional stories where the accuracy of maps have increased to a point where the maps are as big as the countries represented and therefore lose their function as maps. This is of course to overstate the problem, but the relevant level of detail of scientific representations is not only a philosophically interesting problem but also an aspect of scientific and social implications. It is a serious worry among practitioners that so much time and funding is spent on developing models that might be too complex to be useful. Some are even concerned that the complexity of the models will exceed the phenomena represented because the focus on linear molecular causation in terms of regulatory pathways tends to overlook the simplicity of the dynamic behavior and the existence of what Huang (2011, 2248) calls ‘nested binary choices’ such as the possible fates of embryonic stem cells (see also Fell 2007, 98). These concerns show that the implications of heuristics not only involve a risk of oversimplifying the system analyzed, but also may lead to a neglect of the simplicity of biological patterns (Gross 2013). This point will be further clarified in Section 9.6.

Another major challenge for large-scale modelling operate at the level of data integration, i.e. before the construction of large-scale models takes place. To model complex systems, new experimental and computational tools to generate data with higher temporal and spatial resolution are needed (Sauer 2005, Wanner et al. 2005, Hunter et al. 2010). Central for the aim of building large-scale models is the division of labor on data generation and modeling efforts to interpret and integrate this information. To support the development of the whole-cell model of M. genitalium, Karr and colleagues have developed an online database and software source called WholeCellKB.20 Similarly, to realize the VPH project and personalized medicine, integrative efforts and new infrastructures for generating, storing and interpretation of human-specific data and relations to knowledge on model organisms are needed (Clermont et al. 2009, Hunter et al. 2013). One of the main challenges, apparent in the work on existing bio-ontologies such as the Gene Ontology, is the difficulty of incorporating experimental measures from different epistemic cultures into a unified platform for global science (Leonelli 2012b).21 It is often assumed that such databases offer a straightforward solution to

20 For more information, see https://wholecell.stanford.edu. The webpage provides an active interface where thousands of whole-cell simulations and data on M. genitalium can be downloaded. Accessed 15/12-2013. 21 Note here that the philosophical and biological notions of ‘ontology’ differ. A bioontology is a medium for building standards to facilitate the transfer of results, based on what different biological communities take to be evidence (not what, in a more abstract sense, exists in the world).

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fragmentation in the life sciences. However, Leonelli (2013a) demonstrates an increasing awareness in the data curation community of different epistemic cultures. Data curation is a difficult task because ontologies at one hand aim to centralize and standardize means of reference for all users and on the other hand to make data available for de-centralized or local uses of the databases. It is therefore of increasing importance to understand how data and models mediate between these different cultures, and how knowledge is translated from model organisms to network models of human diseases, and between bioontologies and biobanks (Ankeny and Leonelli 2011, Hewitt 2011, Leonelli 2012b, Leonelli and Ankeny 2012).

Because of the lack of access to human specific data, either because of practical difficulties or ethical considerations, model organisms such as yeast, fruit flies and mice have played important roles for insight into development and human disease. In addition to these data, new initiatives provide means of developing human-specific bioontologies based on clinical imaging, omics-fields and experimental practice through computational physiological models (Hunter et al. 2013). Systems biology methods may become key parts of medical practice for translating clinical measures and biological knowledge into simulations. Large-scale models can here provide important information on the extent to which biological systems are near-decomposable to an extent that allow for separation of processes at short time-scales as assumed by Karr et al. (2012). But to reach this aim, top-down approaches may still be necessary, in particular “when the execution of all experiments necessary to validate sufficiently detailed models is limited by time, expenses (e.g. in animal models), or basic ethical considerations (e.g. human experimentation).” (Clermont et al. 2009, 82.2). Whereas large-scale models are worth-while aiming for on a longer time-frame, and might be valuable even if the ambitions are not realized, top-down approaches can thus be highly valuable for a coarser grained, but more time-efficient, estimation of disease-relevant aspects and for pointing out general relationships.

To summarize this section, the new experimental techniques and modelling tools offer new exciting possibilities for biomedical research and clinical practice. Meanwhile, several researchers are concerned that up-scaling models through computational enhancement will not yield a more profound understanding but only reproduce complexity. It is possible that this view is too pessimistic, but it is clear that up-scaling models is no straight-forward solution to the fragmentation of results. Opting for this strategy does not diminish the importance of critically examining how the models are constrained by experimental procedures, material systems, choices regarding parameters to include and models to merge, and on the way in which different models are integrated and data computed. The ramifications of such attempts are far beyond the aim of this project and are to a high extent still to be determined, but it shall be exciting to follow these developments in the future. In the current project, my focus has mainly been to reexamine the role of research strategies in systems biology that aim for generality through abstraction. In the following sections I shall however revisit the role of abstract approaches in the movements from systems biology to the new branches of systems medicine and evolutionary systems biology. In particular, I examine the role of systems-theoretic schemes for re-conceptualizing the relation between genotypes and phenotypes that is of key importance to progress in medicine (Section 9.6) as well as evolutionary biology (Section 9.7). The following

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sections thereby reflect on approaches alternative to bottom-up modelling of as many details as possible but also point to possible synergistic effects of the different strategies.

9.6. Revisiting the implications of ‘systems-heuristics’ in biology A shift towards non-reductive approaches to evolution and medicine has long been called for. One of the more entertaining criticisms of reductionist approaches in biology is written by a cancer biologist, Lazebnik (2002), who provocatively accused biological and biomedical research for doing research in a way that amounts to the absurdity of the Chinese proverb ‘to search for a cat in darkness that is not even there’. Behind the sarcasm is a serious concern with the lack of progress in understanding and treating cancer – despite the efforts and funding put into this area and the large amount of knowledge accumulated on molecular pathways. Lazebnik’ suspicion is that lack of progress reflects a deeper problem, namely that the strategies employed by biologists are bound to fail. To illustrate the concern with the approach of molecular biology, Lazebnik compares biologists’ and engineers’ respective solutions to the task of fixing a radio. He argues that the strategy pursued by biologists - to identify and localize malfunctioning components – is an unfeasible strategy in biomedical research as well as engineering, especially if this search for relevant components cannot rely on knowledge about how the system is organized. Lazebnik proposes that biologists instead could learn from the heuristics of engineers aiming first to identify the wiring pattern of the system to study how different parts affect the overall behavior of the system. This reduction of biological organization to electrical wiring diagrams has been highly criticized, and I have examined similar problems with Alon’s approach in Section 6. But even if the particular solution is problematic, Lazebnik’s criticism may still be relevant. The paper points to a serious worry regarding the prospects of understanding complex diseases in terms of broken components and mechanisms. The need for re-conceptualizing disease and evolution will be further explored below.

A central problem in biomedical research as well as evolutionary biology is the difficulty of understanding the relation between genotypes and phenotypes. As mentioned in Sections 2, 3, and 8,it is becoming increasingly clear that there is no straightforward way to predict phenotypes from genotypes. Many independent projects in biological research over recent decades have concluded that biological systems are strikingly robust to perturbations. While this feature may limit the number of biochemical details necessary to analyze, it raises the challenge of uncovering the organizational patterns behind the nonlinear effects of altered levels of gene expression. A key point of focus has therefore been to understand what features make biological systems resilient to some changes and fragile to others (Kauffman 1993, Newman 1994, 2011, Stelling et al. 2004). These aspects have great importance for understanding disease as well as evolution, and imply an intensification of the reliance on mathematical and computational modeling in making sense of the dynamic relations between molecular and phenotypic levels. Below I provide a short review of a systems-approach to disease and thereafter reexamine the role of engineering approaches in evolutionary systems biology.

Throughout the thesis I have returned several times to the relation between detailed descriptions and general principles. I have described both aspects as explanatorily relevant but emphasized the need for detailed explanations to describe concrete real-world causal relations. Sometimes, however, the relevant relations are not possible to grasp without a higher-level description of the system. In other

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words, some phenomena cannot be understood as sequential changes on a lower ontological level. In such cases, causal relevance is directly related to the higher-level description. Gross (2013) articulates this as ‘the relevance of irrelevance’ in systems biology, emphasizing the importance of understanding the aspects that make higher-level phenomena invariant to many changes on lower levels. Importantly, the systems-theoretical or cybernetic perspective does not imply that the devil cannot be in the details, but rather that the generic organizational features determine what details are relevant for understanding and manipulating system behavior. Below I outline ways to conceptualize disease and evolution that build on the idea of diseases as network perturbations and evolutionary change as shifts in dynamic states (Section 9.7).

To illustrate a systems-perspective on disease I outline two different approaches to describe cancer in a way that goes beyond the gene- and pathway-centric views of network modeling that have dominated much of (molecular) systems biology. These systems-oriented approaches are to be understood in relation to what is popularly referred to as the Somatic Mutation Theory; the view that carcinogenesis is the result of a progressive loss of gene functions due to mutations, resulting in altered pathways in the cells and thereby to clonal expansion (Weinberg 2007, Hanahan and Weinberg 2000, 2010). In contrast to this view, a central feature in the emerging approach of systems medicine is the re-conceptualization of disease as a perturbation of complex multilevel networks, associated with a resulting failure of tissue organization (del Sol et al. 2010, Soto and Sonnenschein 2011). One such approach was described in Section 8 (subsection 2.2) namely Huang’s conceptualization of stem cell properties as high-dimensional attractor states. The idea of describing cancer as attractor states was first suggested by Kauffman over 40 years ago, and builds on Waddington’s (1940, 1953) much older ideas of epigenetic landscapes, but the access to genome-wide gene regulatory network data has recently allowed gene expression profiling that provides an empirical link between observational quantities and state vectors (Huang et al. 2005, 2009).22 Huang and colleagues aim to understand the generic features, derived from patterns of expression modeled as trajectories in state space, where the relation between the ‘normal state’ and the diseased states are understood as shifts in dynamical regimes rather than changes in specific (linear) molecular pathways.

The reason that many genetic or molecular details are considered explanatorily irrelevant in Kauffman’s, Goodwin’s and Huang’s view (Section 8) is that many such changes do not have corresponding phenotypic effects. Rather, complex systems typically converge on a small number of stable attractor states within a certain range of parameters. To look solely for genetic variations between subgroups with characteristic phenotypes (traits in evolutionary biology and diagnostic features in biomedical research) may not allow for a description of the change-relating effects if the underlying networks are organized in different ways, i.e. if the epigenetic landscapes or phase space portraits differ. Accordingly, Gross (2011) describes the systems perspective on disease, as exemplified by Huang’s attractor view, as an alternative or complement to the view of diseases as ‘broken mechanisms’. In Huang’s view, biological processes are at the same time simpler and more complex than the ‘linear pathway paradigm’ indicates. They are simpler in the sense that the number

22 This case is described in greater detail by Gross (2011) who also discusses in depth the relation between this view and the idea of diseases as broken mechanisms.

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of stable attractor states is very low compared to the number of mechanisms that can realize these states. This means that the pathway-oriented heuristic of molecular biology may enlarge and overcomplicate the problem space, rather than the opposite, because all these details focus attention away from the simple and similar patterns of dynamic states. But this framework also highlights the complexity of biological processes in stressing that the dynamic processes of biological networks are considered too complex and too entangled to be captured in the linear view of causation illustrated with arrow-arrow schemes of molecular interactions. Biological causation may be too complex for the rationally bounded human mind to visualize without the aid of mathematical representation. For Huang (2011a) the relevant level for biological understanding is where the higher-level effects canbe modelled as the concerted changes in expression levels of all the N nodes in a gene expression network over time, while the topology remains invariant.23 Thus, the property analyzed does not regard change-relating sequential steps in isolated pathways but cite the changes of each element in virtue of the changes in states of all other network components over time. Since there is no one-to-one mapping between the computation on pathway level and the higher level dynamic behavior,Huang argues that the arrows used to denote pathways in network diagrams have been over-interpreted as causal explanations in biology and that this explains the gap between our understanding of molecular details and ability to predict higher-level states. It also points to a problem for the aim of integrating all the information on pathways studied in isolation. Instead, Huang proposes a framework that models the physical formalizations of equilibrium states of the gene expression network into arrows in state space that then, in Huang’s view, “represents a biological process in its entireness; it is a true ‘path’” (ibid 2251). Grasping causation in this way is however only possible through mathematical models, and it raises important concerns regarding the empirical anchoring of such strategies of abstraction.

As described in Section 8, the explanatory discrepancy between strategies that aim to provide detailed mechanistic explanations and those that seek general principles has caused tensions in biological research. In my view, the tensions to a large extent reflect unsupported explanatory imperialism whereby one explanatory frame is promoted as all-encompassing alternatives to other explanations.For instance, Huang et al. (2011a, 2249) argue for a change in explanatory standards in biology towards fundamental ‘first principles’ based on the mathematical model of GRNs from which properties of cells will follow as a necessary consequence. Section 8 (subsection 2.2) described this strategy as akin to the covering law model, and it is not difficult to see why experimentalists have been reluctant to adopt a scheme that considers experimentally based conclusions ‘hand waiving metaphors’ that need to be replaced with a methodology that derives biological explanations from mathematical ‘first principles’ (Huang 2011a, 2249). But, as argued in Section 8, not only can the strategies coexist; a full understanding of the complex genotype-phenotype map may only be possible to reach if such strategies can be combined. This also seems to be recognized by Huang and colleagues in other writings: “[T]o predict the actual specific epigenetic landscape of the human genome, the detailed knowledge of the actual wiring diagram of the genomic GRN is required” (Huang et al. 2009,

23 It should be noted that Huang’s view in this respect is very different from Alon’s (Alon 2007a). Huang is skeptical of the view of biological networks as a flow of information like in electrical networks (although he himself uses the metaphor of a wiring diagram). These differences illustrate that what constitutes the ‘systems view’ is by no means unified.

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875). The synergy between these frameworks is also expressed in a paragraph where an explanation of a specific pathway diagram is seen as being analogous to a road map where the state space resemble the topography of the landscape covered (Huang et al. 2011, 2257). Thus, whereas precise molecular pathways inform about specific causal links in the system (and thereby define the system of analysis), the state space can uncover the hidden variables that clarify why some roads are taken and other only rarely accessed.

I shall return to the implications of the DS framework in the context of evolutionary systems biologybut first outline an alternative – but also systems-theoretic – approach to cancer that also emphasize the complementarity of the different explanatory strategies. This approach is described in Appendix D (a FEBS minireview written together with Olaf Wolkenhauer). The paper examines the problematic aspects of reductionistic approaches to cancer and suggests that a focus on organizingprinciples may provide new insights into how tissues are functionally organized. Similar to Huang’s work, the motivation for Wolkenhauer’s approach stems from the slow progress in cancer research that currently mainly focuses on mutations associated with particular genes, molecular species or pathways. The narrow focus on subsystems is partly justified by the role of these components in cellular processes like apoptosis or DNA repair mechanisms that again are central for understanding clonal expansion. What is unclear, however, is whether the genetic variation among tumor cells and normal cells is the cause of cancer or the result of cancer. This is not just a semantic issue but also a question of whether cancer is a cell-based or tissue-based problem with important consequences for how the disease is best investigated.

The underlying assumption of the Somatic Mutation Theory, i.e. that cancer is a cell-based problem caused by mutations and altered pathways, is also the target of Lazebnik’s (2002, 1404) previously mentioned criticism as he notes that: “the mystery of what the tumor suppressor p53 actually does seems only to deepen as the number of publications about the protein rises above 23,000”. Generally speaking, the question is whether one can determine – at the molecular level – what aspects are relevant for understanding carcinogenesis if cancer is a matter of failure of tissue organization. For sporadic cancers it has proven tremendously challenging to determine what types of mutations are relevant to delineate properties for tumor cells and normal cells, and to distinguish between benign and malignant tumors. The difficulty of decomposing cancer to explanatory properties at the level of genetic mutations and molecular pathways is due to the multiplicity of causes and heterogeneity among cellular phenotypes, and some even suggest that there are no “cancer cells” (Lazebnik 2010, Bertolaso 2011, Gross 2011). If there are no cancer cells, understood as special cells with qualitatively different properties, but the problem is the organization of cells, one might have to approach the problem differently to increase the understanding of cancer. As an alternative to the Somatic Mutation Theory, Soto and Sonnenschein have proposed what they call a Tissue Field Organization Theory(TOFT) (Sonnenschein and Soto 1999, Soto and Sonnenschein 2011). The key idea is that carcinogenesis is comparable to problems of organogenesis during early development.24 If cancer is

24 This aspect has some affinities with Huang’s account. But whereas Huang’s work is focused mainly on physical constraints to gene expression levels in development, Soto and Sonnenschein emphasize top-down control or ‘downward causation’ at the tissue level. They thus study organizational aspects at different levels. Wolkenhauer and colleagues explore an even more abstract approach that aims to go beyond the numerical approach of DS theory to formulate non-

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a problem of tissue-organization, it has implications for the way the disease is best investigated, since modeling then has to encompass processes at multiple levels. Towards this aim Soto and Sonnenschein call for further development of experimental methodologies involving 3D cultures, whereas Wolkenhauer has pursued a more abstract approach (Wolkenhauer and Green 2013). Drawing on tools from Mathematical General Systems Theory and category theory, Wolkenhauer and colleagues aim to identify the range of possible control relations between stem cell propensities and tissue fate (Wolkenhauer et al. 2011). It is still unclear what the medically relevant outputs of such strategies are for this approach, and I refrain from methodological forecasting in this respect. But regardless of whether this more abstract approach will succeed or not, it is worth noticing the increasing concern regarding the limitations of current approaches and the call for new ways of conceptualizing and investigating complex diseases.

The systems-conception of disease not only has implication for very complex and serious diseases like cancer, but may also change the view we perceive diseases and disorders that currently are considered a simple matter but which – upon closer inspection - are not. In the previous section I mentioned Barábasi’s description of how understanding obesity involves an understanding of causal networks on multiple levels (Barabási 2007, Loscalzo and Barabási, 2011). To illustrate how the systems view of disease differs from the ‘broken mechanisms’ view, Gross (2011) examines an attractor-view of the metabolic syndrome. I shall not go into the discussion on whether such diseases can be encompassed within the mechanistic framework or not, but I shall merely use the example to illustrate the ramifications of a different view on this disease. The metabolic syndrome signifies a cluster of symptoms of metabolic problems such as insulin resistance, obesity, cholesterol abnormalities and high blood pressure. Furthermore, the disorder is considered a major risk factor for cardiovascular diseases and type 2 diabetics. Similar to Barabási’s ‘network of networks’ for obesity (Figure 9B in Section 9.5), environmental and social factors as well as genetic features influence the development of metabolic syndrome. What is particularly striking is that the disorder can be triggered by persistent over-nutrition, but is not easily reversed by a shift to a diet with few calories. From a dynamic network perspective, the disorder can be described as a shift in dynamic regimes where the system converges towards a different stable state characterized by an enhanced secretion of signaling molecules that inhibit the efficiency of insulin signaling (Gross 2011). The disease state is stabilized around a strong attractor, mediated by a positive biochemical feedback loop, and changing the diet is insufficient to push back the system to a normal state. In this example there are no broken mechanisms but an altered dynamic network state that can be persistent to alternation of various pathways. This has implications for treatment that now becomes a matter of intervening in a dynamic network that involves processes beyond the metabolic pathways that were the focus of traditional studies. This view is probably not a valuable perspective for understanding all diseases, but systems medicine may provide a much needed shift of perspective for understanding diseases where traditional approaches have had limited effects.

numerical organizing principles (Wolkenhauer et al. 2011 and Appendix D). For a description of the differences and potential for integration of the Somatic Mutation Theory and the Tissue Field Organization Theory, see (Bertolaso 2009, 2011). For references to the variety of hypothesis on the nature of cancer, see Appendix D.

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Whereas Barabási’s network medicine, the DS framework suggested by Huang and colleagues, and the focus on tissue organization suggested by Soto, Sonnenschein and Wolkenhauer are examples of approaches that try to go beyond the pathway-centered conception of disease, the use of the DS framework in evolutionary biology (Section 8) also promotes a different way of thinking about the relation between evolution and development. In this framework the alteration of allele frequencies through natural selection is only one aspect of a complex feedback loop encompassing developmental, physical, genetic and selective constraints. The systems-oriented views are from one perspective more ‘holistic’ in emphasizing system-level processes that are not visible from the lower level of molecular interactions. At the same time, however, the reconceptualization of disease as perturbations in network states seems to reduce away some of the significance of mechanistic explanations of sequences of molecular events that have been used as counterarguments against a reduction of biological systems to physical laws (e.g. Machamer et al. 2000). It is therefore relevant to ask whether the quest for general principles in biology is associated with an increasing ‘physicalization’ of the life sciences, or a development that turns biology into an ‘engineering science’ (Lynch 2007b).

9.7. What do physics and engineering have to do with biology?

Systems dynamical descriptions often resemble physical explanations. Gross (2013, 153) notes that in Huang’s framework “[g]enes and their products are treated as ‘anonymous’ particles of a system whose interactions generate regular behavior of a higher level of observation, almost like the molecules in a gas”. Commenting on the analogy, Huang (2011a) adds that an important difference between gas expansion and biological development is that the latter is channeled towards specific attractors and thereby cannot fill the whole space like the gas molecules. In Huang’s view, what channels these trajectories are “the fundamental constraints rooted in the laws of physics of complex dynamical systems” (ibid, 2256). As stated in Section 8 and 9.6, the use of DS modeling in biology is therefore often associated with reductionistic leanings and has been met with skepticism from experimental biologists. view indicates that biological explanations are

without the general perspective. But as we have argued in Section 8, based on the group of Jaeger’s work, the quest for general principles in developmental and evolutionarycontexts can synergize.

Let me mention a few more examples that highlight a similar view. The molecular biologist and computer scientist Eugine Koonin (2011) have called for the formulation of ‘laws of evolution’, akin to laws in physics. He compares the role of population size to temperature in statistical mechanics, but adds to this account that neither physics nor biology can be fully defined in terms of such laws or do without specific explanations.25 Koonin argues that a full explanation in both fields requires an uncovering of the “interplay between stable, predictable patterns (laws) and unpredictability of specific outcomes” (Koonin 2011, 5). The hope is that general principles can be found that show the simplicity behind the apparently complex behavior of similar systems. The same idea is underlying

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the exploration of dynamical systems theory in Jaeger and colleagues’ work, as examined in Section 8. The work of Jaeger and colleagues suggests that even very abstract top-down approaches, such asDS modeling, and bottom-up experimentation can be productively combined through systems biology methods, namely parameter fitting through reverse engineering and evolutionary simulations. General models that display the constraints on variation can provide a complement to detailed approaches focused on molecular interactions, and can thereby be informative for understanding the nonlinear relations between genotypic and phenotypic changes. As described in Section 8, modeling of developmental processes as flows towards attractors in phase space allows for an identification of the possible trajectories, given changes in initial conditions. When formalizations constrain the problem space of relevant mechanisms, they address the question of why some operations, rather than others,are realized in biological systems. By increasing the understanding of physical and functional constraints on biological functioning and potential of evolvability, approaches inspired by physics and engineering can thereby inform about the boundaries of processes that shape biological variation.

Whereas an understanding of the limits of biological variation alone not provide a causal explanation of a real-world phenomenon, it may have explanatory power in guiding the understanding of genotype-phenotype mapping. Similar to the accounts of Jaeger’s group, Morange (2005, 2011) argues that a deeper understanding of physical constraints is needed to fullyunderstand the realized instantiations of real-world mechanisms, i.e. for why some forms are not accessible to natural selection. But whereas Jaeger and Sharpe (2014, in press) stress the need of identifying functional constraints for biological design principles, Morange emphasize how mathematical and physical approaches take over where traditional biological explanations reach their limits (Morange 2011). Instead of physical explanations I have preferred the more open, but admittedly also more vague, notion of general principles. The reason is that I find it important to emphasize that the formulation of organizing or design principles is not confined to explanationsalready developed in physics. Furthermore, an understanding of living systems involves anunderstanding of functions (cf. Jaeger and Sharpe 2014, in press). Tools from engineering can be fruitful too, and some organizational principles in biology may be confined to the biological realmonly

Emphasizing the importance of what Morange calls physical explanations and what Jaeger calls design principles does not imply that biological explanations will be reduced away by more fundamental principles but that living systems, qua their status as bio-physical systems, must partly be understood through chemical and physical constraints that set boundaries for biological operations, and that biological systems share with engineered systems some boundaries of functional constraints. Biological systems have found ways to deal with physical constraints, e.g. by incorporating energy from the environment in metabolic processes where processes with positive and negative Gibb’s free energy are coupled. To explain biological functions such as development and metabolism we need not only understand the metabolic pathway but also the boundaries that constrain these processes and the complex biological context that situate these processes. Thus, the law-like principles that systems biologists seek seems to operate at the intersection of the different disciplines. This also means that biological explanations are typically multi-level. Let me briefly clarify this with an example. Physical

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constraints are important parts of the explanation for why some respiratory designs are common because these denote the diffusion properties of gasses across a surface. But to determine the importance of these in a biological context, biological aspects such as the size and metabolic type of the organism, environmental features and selective constraints are equally relevant. The increasing focus on physical constraints and mathematical abstractions therefore needs not to challenge the explanatory power and autonomy of biological explanations but add to biological analysis by outlining constraints that biological processes are at the same time afforded and limited by. Thus, rather than representing a return to the reductive ideals of traditional philosophy of science where physics was seen as a model discipline (Section 1), proponents of systems biology have emphasized the need for multilevel analysis – what Noble (2012) calls a “theory of biological relativity”. This implies that the molecular or physical level is a not causally privileged level of analysis as reductionist approaches imply. In fact, the current developments in the life sciences where researchers aim to clarify organizational features may instead bring back the whole organism as the reference point of biological analysis (Goodwin 1994, Nicholson forthcoming).

At this point it should be emphasized that the existence of physical explanations in biology by no means is a new tendency. What is new is perhaps the recognition of the extent to which such constraints are needed to explain biological variation and variability, and why these aspects are important for understanding diseases as well as evolution. Although modeling of biological systems as a ‘landscape’ or state space is more than half a century old, the availability of genome wide expression data means that these tools have regained their relevance for understanding how constraints on trajectories drive these systems towards equilibrium states. The current ‘network fever’ in systems biology also emphasizes a renewed focus on more general constraints on biological operations. Whereas Morange (2011) has stressed the need for “physical explanations” in biology, this thesis has focused more on engineering-inspired methodological frameworks and the heuristic role these play in biology. I have discussed the inherent risk of this heuristic to oversimplify biological phenomena, through metaphors such as genes as ‘blueprints’ for building phenotypes, and of implying adaptationism (in the comparison of intentional design and optimization through natural selection). I therefore finish the dissertation by reexamining the relation between engineering approaches and evolutionary studies in biology.

The debates in systems biology have shown a need to be wary of the implications of the heuristic frameworks, including modeling assumptions and metaphors, because the unreflected use of these implies a risk of overstating the similarities between designed and living systems, in particular between intentional design and evolution. But, as I have argued through the thesis, the problem does not lie in the falsehood of engineering metaphors. Strictly speaking these can only be false if taken literally. The problem lies in the unreflected use of these, and in reasoning strategies that would be problematic for some engineered systems as well. Lewens (2002, 2004) has pointed out that the adaptationist implications of inferences from current utility to reasons for origin would also have limits for explaining the technological and intentional origin of designed systems. From the workings

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of a design it is not always safe to infer the intentions behind the design since the design process is also bounded by various constraints regarding cognitive, material, economic resources and time available to develop designs. When biologists and philosophers criticize engineering approaches in biology, the fact that much of engineering is actually tinkering is often overlooked and, as such, assumptions of optimal design would be equally problematic in a historical analysis of artefacts. In neither of the two domains it can be presupposed that the design is always ‘functionally optimized’. But there exist a limited scope of designs that can support the function at all. In defending a non-adaptationist design-approach, Section 7 argued that design thinking and adaptationism can sometimes be dissociated in functional analysis. The distinction we draw to define a ‘thin’ notion of design has affinities with Mayr’s ultimate-proximate distinction. To understand the search for general principles of evolution in ESB (as in Section 8) we however need to go beyond this distinction.

9.7.1. Towards a systems view of evolution?

Evolutionary systems biology raises the question of whether engineering approaches can play anyrole in more advanced evolutionary studies. It is sometimes argued that an important difference between biology and engineering is that the origin of the design matters in the former context, whereas it does not in the latter. Lewontin (1996), for instance, emphasized this point in his paper on “Evolution as Engineering”. Lewontin recognizes that artificial designs also undergo a historical process of manufacture but sees this description as largely irrelevant to the engineer, because a designed system like a car can be fixed without knowing the history of the design. In contrast, he argues, to fully understand biological systems we need to understand these in an evolutionary context. Many have taken such differences to indicate a profound difference between the two domains that casts doubt on the role of engineering approaches for understanding evolution. Against this view, Calcott (2014 forthcoming) offers a fresh perspective by establishing a connection between software development and evolvability in biology. What Calcott calls diachronic engineering principles in software engineering have strong affinities with what evolutionary systems biologists call ‘evolutionary design principles’ or ‘generic principles of evolution’. In the following, I reflect on the connections between Calcott’s account and the use of engineering inspired approaches in evolutionary systems biology.

The focus of Calcott’s paper is the question of whether biological and designed systems share universal principles that constrain the way they change over time. Whereas an adaptationist approach makes connections between intentional design and natural selection, the ‘evolvability approach’ promoted by Calcott examines the structural and functional similarities of systems in terms of the possible space of future changes in the two domains. The former can, as discussed in Section 6, be productive but also problematic. The latter implies an extension of biological questioning in Mayr’s framework. Understanding evolution not only is a matter of understanding why a feature evolved, and how the trait functions, but also what mechanisms made the evolutionary change possible (Calcott 2009, 2013). As I shall clarify below, these mechanisms go beyond the effects of natural selection (and beyond the issue of intentional design in engineering).

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Recent research in population genetics, Evo-Devo and evolutionary systems biology is focused not only on ultimate why-questions but also on questions regarding the capacities of change in different lineages and the sequence of mechanisms that makes these changes possible. The distinction between an analysis of functional capacities (Section 7) and the analysis of the capacity of evolutionary change (Section 8) has parallels in what Calcott (2014 forthcoming) calls synchronic and diachronicengineering goals. Whereas the synchronic goal reflects the current performance of the software program, diachronic goals regard the degree to which the source code can be modified in the future. Software development typically progresses through modification of previous versions of software codes. Calcott stresses that the software product’s history can be essential if one is to understand why the program works, or does not work, and how it can be further modified. Moreover, because the diachronic goal is difficult to achieve for complex software systems, software engineers have stressed the need for principles and ‘design patterns’ that can guide the construction of functionally robust and open-ended software (Gamma et al. 1995). Calcott emphasizes the similarity between this engineering aim and the interest in general features underlying evolvability in biological research in e.g. the ‘theory of facilitated phenotypic variation’ (Kirchner and Gerhart 1998, Gerhart and Kirschner 2007). Biologists and philosophers who criticize the connection between engineering and biology tend to overlook these connections between engineering and biology (e.g. Lynch 2007a, 2007b, Pigliucci and Boudry 2011). Yet, such thinking opens a space for a productive role for engineering approaches in evolutionary (systems) biology.

As mentioned, Calcott’s description of diachronic engineering principles in software development has strong affinities to the ideal of evolutionary design principles in systems biology, and some of the principles may even be similar. Calcott stresses the similarities between redundancy, or neutral change, in the genotype-phenotype map (Wagner 2005a, 2005b, 2008) and what is called code-refactoring in software engineering. Refactoring builds on redundancy in the mapping between the source code and the program and can therefore be a source of innovation without being visible at the level of program operation. The level of refactoring can therefore be seen as analogous to variability (in contrast to variation that is visible at the phenotypic level) that has been argued to be crucial for understanding the relation between robustness and evolvability. This interest was also central in the work of Jaeger’s group (Section 8). The case emphasized that if we want to predict and explain evolutionary trajectories, we need to understand how the dynamic developmental system can contain ‘hidden variables’, i.e. differences in genetic variation that make different organisms or species respond to perturbations in different ways. This can be thought of as analogous to two different software systems that currently can perform the same ‘synchronic’ function but that contain different possibilities for further modification. This extended perspective does not imply that biological systems can be reduced to a matter of understanding these aspects of software-engineering. However, the criticism of engineering approach must take engineering disciplines into account that go beyond the perspective of mechanical engineering or simple perceptions of software systems as being straight-forwardly determined by a ‘blueprint’ of codes.

Engineering inspired approaches may therefore to some extent be compatible with evolutionary analysis and may even help to facilitate the understanding of the complex mapping between

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genotypes and phenotypes. Calcott suggests that software-engineering offer a conceptual framework for many aspects relevant for modelling of biological systems. For instance, software engineers distinguish between interface and implementation; the former is the relation between inputs and outputs, and the latter defined specifically how this input is computed to yield an output. In mechanical engineering, modules are often built to be functionally independent. However, in biological networks and in software systems, modules are often displaying some independency while also interacting with other modules (Section 6). Understanding what engineers call ‘interface’ is central to the construction of whole-cell models. As described in Section 9.3, modeling large biological systems implies assumptions about the extent to which different submodules are operating independently over short time-scales. One of the central question for future modelling in systems biology is the relation between interface and implementation and how such relations between the different model components can be integrated in a way that does justice to cross-system and cross-level relations in biology.

Furthermore, an extension of engineering approaches towards an analysis of evolutionary design principles associated with evolvability, parallel to diachronic engineering principles in software development, may open the way for fruitful interconnections. In ESB, the explanatory value of ‘evolutionary principles’ shall be understood in a research context where evolutionary systems biology as well as systems medicine emphasize the need for a stronger predictive power of evolutionary explanations (Papp et al. 2011). As a concluding remark I suggest an interpretation of the goal of these new approaches as reverse and forward tinkering. The preference for the notion of reverse tinkering needs some clarification. As described in Section 6 and 7 there is an evolutionary as well as a capacity-oriented dimension to reverse engineering. But evolutionary biologists, in particular researchers within Evo-Devo, are sometimes interested in another question regarding the gradual sequence or trajectories of changes in structures over evolutionary time-scales. Calcott (2009) sees the latter as a ‘second origin question’ that is answered by what he refers to as lineage explanations. These regard the mechanisms of gradual changes in e.g. the evolution of eyes or feathers and can be combined with either generic or operational descriptions regarding the developmental constraints or selective pressures driving the change. These explanations also have a parallel in software engineering where one may ask what series of changes in coding may have preceded the current program and what the potential for further development is (Calcott 2014 forthcoming). The lineage explanations in Calcott’s examples regard detailed mechanistic descriptions of individual-based phenomena. However, generic models in structuralist accounts (Goodwin 1982, 1994) and EvoDevo (Collins et al. 2007) as well as some network approaches in ESB (Hogeweg 2012, Steinacher and Soyer 2012) also investigate general aspects of evolutionary change. The quest for generalizable features is reflected in the notions such as ‘generic principles’, ‘generative principles’, ‘isomorphic principles’, or ‘evolutionary design principles’. The principles are different, and are based on different theoretical frameworks, but the common idea is that general constraints and principles of evolution can inform about aspects of the ‘tinkering potential’ of the system (Hogeweg 2012). Because of the renewed focus on non-selective constraints in shaping evolution we may understand the attempts of historical and forward modeling as reverse and forward tinkering rather than an adaptationist analysis based on functional end-points of evolution. And unlike forward

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engineering in synthetic biology the aim is not to shape new designs but to understand past trajectories and predict future evolutionary states. Thereby the aim is to turn evolutionary biology into a predictive, and not only historical, discipline.

Because these analyses are grounded in investigations of the ‘tinkering potential’ of naturally evolving lineages, we can understand the goal of prediction as forward tinkering. Similar to the apparent tension between complexity and general principles, it therefore becomes relevant to ask whether the aim of predictability is in conflict with the inherent contingency of biological systems. Gould’s (1989) statement about the different outcome of a replayed tape of life is often taken to mean that evolutionary theory can only be a historical theory with highly context-dependent explanations.To what extent is the aim of ESB to turn evolutionary biology into a predictive discipline with law-like principles in conflict with the view of contingency of biological evolution? To answer this question, it is important to keep in mind that there are different dimensions of contingency, and the study of adaptive variation and of phylogenetic inertia need not be in conflict but can – following Gould (1977/2006) - be viewed as different aesthetic styles or focal points within the same evolutionary theory. One can say that evolution is highly contingent in the sense that evolution can take an infinite number of roads, making it highly unlikely that something like Homo sapiens wouldresult from a rewound tape. But, as stressed in Section 8, it does not follow that evolutionary roads are randomly curved.

Let me clarify this point with some of Gould’s own examples. Gould was, as a paleontologist, just as stunned by the common morphological patterns across phyla as by the diversity of species (Sterelny 2007). In other words, he was interested in what factors could explain why so many logically possible morphologies were not biologically realized. In the short piece “Size and Shape”, Gould states: “Yet, however much we celebrate diversity and revel in the peculiarities of animals, we must also acknowledge a striking ‘lawfulness’ in the basic design of organisms” (Gould 1977/2006, 319). Gould saw this as strongly evident in the structural and physical constraints governing the correlation between size and shape of organisms. As organisms grow larger, the volume and surface area grow disproportionally (what in biology textbooks is known as the volume-surface ratio). While vertebrates can increase surface area by compartmentalization of organs, including convoluted surface areassuch as lungs and villi in intestines, other land-living organisms such as insects are constrained to remain small. Similarly, the laws of gravity greatly constrain the evolutionary roads for different lineages in terms of types of skeletons to support the weight on land, and an animal of the size of an elephant or dinosaur cannot have the body proportions of a dog.27

The spandrels-analogy examined in Section 6 exemplifies how such constraints in Gould’s view go beyond the domain of life: when building ships, buildings, and machines, there are similar structural constraints at work. Multiple papers contain comparison to architectural constraints when building churches. For instance, Gould (1977/2006) clarifies that in the attempt to upscale the designs of medieval churches in England, it was discovered (through tragic events where churches collapsed)

27 In his typical entertaining style, Gould describes how many science fiction movies are not obeying these laws: “We may be sure that even if the giant ants had somehow circumvented the problems of breathing and growth by molding, their sheer bulk would have grounded them permanently” (Gould 1977/2006, 321).

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how the disproportionality between weight and surface area with increasing size of a building must be taken into account. Similar to the constraints on bone structure or external skeleton with the size of the animal, the churches could not be up-scaled without adding intermediate supports to the ceiling or making the church narrower. Such relations have by researchers been attempted to be articulated mathematically. Thompson’s (1917/2004) theory of ‘transformed coordinates’, mentioned in Section 5, was one of the pioneer attempt to outline the mathematical patterns underlying the constrained variation observed in living organisms. But Thompson had no reply to how such structural rules could be inherited. Gould (1983/2006) declares his support of Thompson’sproject that represents an alternative to what he calls ‘vulgar Darwinism’; the attempt to analyze organisms ‘part by part’ by rationally constructing the selective drive towards adaptive endpoints(also criticized in Gould and Lewontin 1979). From the perspective of adaptationists, the endpoints are not completely random but signify ‘good adaptive tricks’ (Dawkins 1976, Dennett 1995). What they consider random are the paths to get there, and how variation is generated in the first place is therefore not considered the key to understand evolution. Gould takes the opposite stand in viewing the specific endpoints as largely contingent but the processes leading to these as constrained, although manifold.

The relevance of this perspective for evolutionary systems biology is that the boundaries that constrain variation may perhaps be called laws or principles of evolution. These not only limit the possibilities for selection but afford these by generating and stabilizing patterns upon which selection may act (Goodwin 1982, Kauffman 1993, Jaeger and Crombach 2012). Thus, as stated by Gerhart and Kirschner (2007, 8588): “The burden of creativity in evolution, down to minute details, does not rest on selection alone”. This perspective does not question the status of selection as a key driving force of evolution, but stresses the need to also answer questions related to the actual level of randomness in variation, the extent to which evolutionary changes happen gradually, and the level of contingency for evolutionary trajectories and endpoints. I expect these issues to be among the main topic of future evolutionary research and something that crosscut many different research traditions.

The issue of constraints on form and morphological shapes has so far mainly been taken up by neo-Rationalists, developmental biologists and recently by the field called Evo-Devo, and not by systems biologists (Müller and Newman 2003, Collins et al. 2007, Callebaut et al. 2009). But the engineering-inspired approach of systems biology has made progress in outlining functional constraints for biological designs, sometimes tied to processes of selection and sometimes not (Section 6 and 7). Some are optimistic that ESB may provide the integrative framework for understanding the entangled relations between the different types of constraints and thereby clarify the relation between development and evolution (Section 8). But many questions still remain to be answered. One of the big open questions is the degree of freedom for evolutionary trajectories. Although progress has been made it is still the case that “our ignorance of the laws of variation is profound” (Darwin 1859, 167) 28

28 This often-cited expression (in the beginning of the summary to Chapter V) is for reasons unknown deleted in somerecent editions of The Origin of Species. In this chapter, called “Laws of variation”, Darwin reflects on ‘correlational laws’ of growth and form in different lineages independently of utility (referring to Geoffrey St.-Hilaire’s work, among others). Gould and Lewontin (1979) declare with regret that these aspects of Darwin’s theory have been neglected or even dismissed in the neo-Darwinian framework.

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Because so little is known about how tightly evolution is constrained, it is rarely specified at what level of biological organization, and on what level of detail, the predictions of future ESB are aimed (Papp et al. 2011). Evolutionary simulations may prove an important tool for these investigations. These have the advantage that researchers can “focus on evolutionary trajectories and transitions,rather than the substrates or end projects of natural selection” over a large time span (Jaeger and Crombach 2012). Thus, in silico modeling can largely extend and complement experimental approaches by a search for evolutionary patterns across multiple levels of biological organization. In particular, non-supervised (or non-goal directed) simulations have great potential for informing about the structural side effects of selection and for outlining the range of possible future trajectories of the evolving in silico system (Hogeweg 2012). Such studies have already led to surprising insights. As an example, I mentioned in Section 6 how Cordero and Hogeweg (2006) showed that hierarchical structures with overabundance of network motifs (feedforward loops) can emerge by non-selective tinkering of promoter regions in simulations of evolving yeast networks.

The increasing awareness that non-selective factors are not random allows for a predictive view that does not imply theorizing in terms of naïve adaptationism. The results give renewed hope to carry out a rule-based research program with affinities to Riedl’s (1978) and Goodwin’s (1982) ideas of a rational morphology that could provide generalizations on development and evolution.29 But evolutionary simulations of large and complex organisms will probably face similar problems as the ones described for whole-cell models to study metabolism, and the difficulty of modelling organisms such as fruit flies, despite the extensive research that have been conducted on these for more than half a century, calls for modesty regarding the results expect in the near future. With that being said,the rapid developments in the life sciences in the last decades is truly overwhelming. It will be exciting to follow the development of systems biology and systems medicine towards the aim of improving the predictive power of medicine and evolutionary studies, and how these developments will affect our conceptions of disease, evolution and practices for treatment and heath care.

29 What today is referred to as constraints have affinities with Rupert Ridl’s (1978) concept of ‘burden’ that at the same time reflects the sources of and limit to variation (and thereby to innovations and future phenotypes).

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