The fox knows many things, but the hedgehog knows one big thing.
—Attributed to Archilochus in Isaiah Berlin (1953).
Evolutionary systems biology (ESB) aims to integrate methods from systems biology and evolutionary biology to go beyond the current limitations in both fields. This article clarifies some conceptual difficulties of this integration project, and shows how they can be overcome. The main challenge we consider involves the integration of evolutionary biology with developmental dynamics, illustrated with two examples. First, we examine historical tensions between efforts to define general evolutionary principles and articulation of detailed mechanistic explanations of specific traits. Next, these tensions are further clarified by considering a recent case from another field focused on developmental dynamics: stem cell biology. In the stem cell case, incompatible explanatory aims block integration. Experimental approaches aim at mechanistic explanation while dynamical system models offer explanation in terms of general principles. We then discuss an ESB case in which integration succeeds: search for general attractors using a dynamical systems framework synergizes with the experimental search for detailed mechanisms. Contrasts between the positive and negative cases suggest general lessons for achieving an integrated understanding of developmental and evolutionary dynamics. The key integrative move is to acknowledge two complementary aims, both relevant to explanation: identifying the space of possible dynamic states and trajectories, and mechanistic understanding of causal interactions underlying a specific phenomenon of interest. These two aims can support one another in a joint project characterizing dynamic aspects of evolving lineages. This more inclusive project can lead to insights that cannot be reached by either approach in isolation.
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Note that the search for design principles does not imply a designer or necessarily rely on a comparison between intentional design and natural selection.
We do not dispute the possibility that tensions between fields can be productive in research in shaping specialized fields. However, when relevant aspects are not integrated due to such disagreements, it may hinder progress in science.
The terms, due to Smith (1992), will be clarified in the following section. The notions of neo-rationalism and neo-Darwinism are perforce somewhat ambiguous and refer to two streams of biological research drawing on common organizing concepts rather than well-defined research programs. Smith’s (1992) notion of neo-rationalism is slightly misleading since it gives the impression that this stream involves a purely philosophical program. With these caveats in mind, we shall however continue with these terms for the sake of simplicity.
See Amundson (2001) for a historical review of the 18th- and 19th-century debates on the notions of heredity, homology, and constraints.
Historically, these issues have been related. As we shall see in the following, neo-rationalists argued against the focus on selection as well as on genetic details in neo-Darwinian approaches. But the issue of adaptationism is orthogonal to the question of the relevant level of explanation. A very powerful criticism of the focus on adaptive function has come from evolutionary geneticists who have emphasized how neutral evolution should also be considered a driving force of evolution (e.g., Kimura 1985; Lynch 2007a, b).
An exception is of course stabilizing selection, but the basic point is the same in this case—the interest is in selection of functionally beneficial traits rather than non-selective processes that generate variation or stabilize patterns.
It should be noted that neo-Darwinian explanations can be general too. Similar disagreements regarding general and detailed explanations also exist within this approach, e.g., between proponents of equilibrium studies and researchers arguing for the need for detailed phylogenies and population histories (Amundson 2001). Similarly, not all approaches to development focus on general principles as structuralists do. However, we here concentrate on the discrepancies between neo-Darwinism and neo-rationalism.
Goodwin has become known for rather provocative statements on the unimportance of genes. But, in particular in his later work, Goodwin argued that genes are indeed important to understand evolution and development. What he stressed was, however, that this role can only be understood against the background of the organization of developmental systems (Jaeger and Monk 2013).
Whereas some, including Mayr himself, saw development as “proximate” biology, one could say that development in this view becomes even more “ultimate” than natural selection because selection is based on variation generated by these processes (Amundson 2001).
See, e.g., Melton and Cowan (2009, p. xxiv), and Ramelho-Santos and Willenbring (2007, p. 35). Stem cells vary in their differentiation potential, being designated as pluri-, multi-, oligo-, or unipotent depending on the range of cell types they can produce. These distinctions do not affect the arguments here. For simplicity, we refer to stem cells as “multipotent” in what follows.
Though DS theorists often refer to the elements of GRNs as “genes,” they do not represent inert DNA sequences alone, but rather focus on the gene’s mRNA and/or protein products.
The full quotation: “As long as such gene expression dynamics allows for oscillation between on and off states of gene expression, this course of differentiation appears universally” (Kaneko 2011, p. 408).
Some philosophers of biology defend accounts of mechanisms that do require laws, generalizations, or abstraction (e.g., Glennan 1996). However, the prevailing view of mechanistic explanation in biology is that laws or general principles are not required.
There is considerable debate within the philosophy of biology as to whether this norm is the sole guide to constructing mechanistic explanations. However, the basic point that mechanistic explanations aim to capture (some) features of real biological systems is widely accepted.
This may be too strong; DS models do predict that gene manipulations in reprogramming should produce "oscillation" in expression levels. But the models offer no guidance as to which genes those are, which undercuts the appeal from an experimental point of view.
In this context, parameter space refers to the space of the regulatory network, sometimes called “genotype space,” although parameters are not only determined by DNA sequences but also by environmental influences. Just as in the case of state space, parameter spaces can be subdivided into discrete regions. In this context, boundaries refer to bifurcation events.
Hugo de Vries famously said that “natural selection may explain the survival of the fittest, but it cannot explain the arrival of the fittest” (De Vries 1940, pp. 825–236). That is, natural selection cannot explain why some forms are produced in the first place, but only why some traits are preserved in a population.
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We would like to thank Orkun Soyer, Maureen O’Malley, and Sabina Leonelli for organizing the workshop on ESB and the KLI for hosting this event. We are grateful to the participants of the workshop for many fruitful discussions, and to Gerd Müller and Werner Callebaut for taking the initiative to have a thematic section based on important themes discussed at the workshop. Johannes Jaeger would like to thank Karl Wotton for the hedgehog and the fox, as well as Nick Monk and the late Brian Goodwin for countless inspiring discussions on the philosophy of science. Sara Green acknowledges support from The Danish Research Council for Independent Research/Humanities for funding to the project Philosophy of Contemporary Science in Practice. Melinda Fagan’s research on this paper was supported by the Mosle Foundation and a Faculty Innovation Fellowship from the Humanities Research Center at Rice University. Johannes Jaeger’s research group is supported by the MEC-EMBL agreement for the CRG/EMBL Research Unit in Systems Biology.
Sara Green, Melinda Fagan, and Johannes Jaeger contributed equally to this work.
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Green, S., Fagan, M. & Jaeger, J. Explanatory Integration Challenges in Evolutionary Systems Biology. Biol Theory 10, 18–35 (2015). https://doi.org/10.1007/s13752-014-0185-8