Systems biology and the integration of mechanistic explanation and mathematical explanation

Highlights

• Discusses the integration of mechanistic and mathematical styles of explanation.

• Analyzes several case studies from systems biology.

• Presents qualitative phenomena whose explanation requires a quantitative account.

• Draws conclusions for a broader ontological conception of mechanisms.

Abstract

The paper discusses how systems biology is working toward complex accounts that integrate explanation in terms of mechanisms and explanation by mathematical models—which some philosophers have viewed as rival models of explanation. Systems biology is an integrative approach, and it strongly relies on mathematical modeling. Philosophical accounts of mechanisms capture integrative in the sense of multilevel and multifield explanations, yet accounts of mechanistic explanation (as the analysis of a whole in terms of its structural parts and their qualitative interactions) have failed to address how a mathematical model could contribute to such explanations. I discuss how mathematical equations can be explanatorily relevant. Several cases from systems biology are discussed to illustrate the interplay between mechanistic research and mathematical modeling, and I point to questions about qualitative phenomena (rather than the explanation of quantitative details), where quantitative models are still indispensable to the explanation. Systems biology shows that a broader philosophical conception of mechanisms is needed, which takes into account functional-dynamical aspects, interaction in complex networks with feedback loops, system-wide functional properties such as distributed functionality and robustness, and a mechanism’s ability to respond to perturbations (beyond its actual operation). I offer general conclusions for philosophical accounts of explanation.

Introduction

Current philosophical accounts of integration and integrative explanations can be seen as a philosophical alternative to reduction and reductive explanation (Brigandt, 2013a, Brigandt and Love, 2010, Brigandt and Love, 2012b, Plutynski, 2013). By assuming that the knowledge from several fields can be logically deduced from a more fundamental (lower-level) theory, the model of theory reduction proved unable to capture the complex relations among biological ideas. Initial accounts of integration conceptualized the non-reductive yet systematic relations among fields as being provided by particular theories (so called interfield theories; Darden and Maull, 1977, Maull, 1977), but nowadays broader accounts are available, which do not require theories to be central epistemic units for all of biology (Brigandt, 2010, Leonelli, 2013, O’Malley, 2013). One approach is to use the notion of a mechanism, which is currently popular in philosophy of science (Craver and Darden, 2005, Glennan et al., 2002, Machamer et al., 2000).

For various branches of experimental biology, including molecular biology, cell biology, and neuroscience, accounts of mechanistic explanation have proven fruitful, among other things because they are able to articulate the nature of multifield, multilevel explanations—as opposed to purely reductive explanations—given that different disciplines can contribute to the elucidation of a mechanism and mechanisms involve the interaction of entities on several levels of organization (Bechtel, 2006, Craver, 2007, Darden, 2005). Moreover, mechanisms are discovered in a piecemeal fashion, and mechanistic research may alternate between downward, reductive episodes and upward, integrative episodes (Bechtel, 2010, Bechtel, 2013, Craver, 2005). As a result, such philosophical accounts pay attention to discovery and the change of knowledge and research strategies and thereby address not only the product of science (e.g., a finished explanation) but also the practice and dynamic process of science (Brigandt, 2013a, Brigandt, 2013b, Gerson, 2013, Griesemer, 2013).

Systems biology is nowadays thriving, and it is clearly an integrative and interdisciplinary approach. It attempts to explain complex biological systems using a variety of conceptual and experimental resources. The focus is on system-wide behavior rather than the properties of a few isolated components or causal pathways of a system. Due to the availability of information about a plethora of molecular and cellular entities provided by functional genomics, proteomics, and metabolomics projects, systems biology nowadays has a clear molecular-mechanistic face. At the same time, the characteristic feature of systems biology is its reliance on mathematical models (Wolkenhauer & Muir, 2011). This may be at odds with philosophers’ conceptualization of mechanistic explanation as the explanation of a whole in terms of its physical parts, including the spatial organization of and qualitative interactions among parts. Philosophical accounts of mechanistic explanation have usually been silent about how a mathematical model could contribute to an explanation at all; in fact, causal-mechanistic accounts have been developed as an alternative to traditional models of explanation as derivation from quantitatively formulated laws (Brigandt, 2013a, Craver, 2007). Yet systems biology does appear to explain using equations and quantitative models (Baetu, in press, Fagan, 2012).

In this paper, I argue that among its many other integrative aspects, systems biology is working toward an integration of mechanistic explanation and mathematical explanation, i.e., a kind of explanation that includes both reference to spatial parts and mathematical equations. This calls for a broader philosophical conception of what a mechanistic explanation is. My thesis is in line with Bill Bechtel’s and Adele Abrahamsen’s notion of ‘dynamic mechanistic explanation’ (Bechtel, 2011, Bechtel, 2012, Bechtel, 2013, Bechtel and Abrahamsen, 2010, Bechtel and Abrahamsen, 2011), but will go beyond previous accounts in two ways. First, given that mechanists such as Carl Craver (2007) have highlighted the difference between merely modeling and explaining a phenomenon, rather than just indicating that mathematical modeling plays some epistemic role, I specifically argue that mathematical models are indispensable ingredients of some explanations in molecular biology. In fact, while it is trivial that a mathematical representation is needed when accounting for the quantitative aspects or precise temporal dynamics of a system, I will point to several qualitative phenomena to be explained where a quantitative explanation is still required. Second, beyond the example of circadian rhythms discussed by Bechtel and Abrahamsen, I address systems biology as a larger domain and survey several cases that illustrate why equations can be explanatorily indispensable.

The following section gives a brief overview of systems biology. Section 3 provides a general account of how mathematical equations can have explanatorily relevance, but also of when a mathematical model can omit mechanistic detail and still be explanatory. Section 4 surveys several cases from systems biology that illustrate both that its mathematical models are developed and tested based on molecular-mechanistic information, and that there are qualitative explananda necessitating a quantitative explanation. In addition to drawing implications for philosophical accounts of mechanistic explanation and explanation in general, the concluding section will also use the case studies to call for a broader ontological conception of mechanisms.