Roles of Diagrams in Computational Modeling of Mechanisms

Roles of Diagrams in Computational Modeling of Mechanisms William Bechtel (bechtel@ucsd.edu) Department of Philosophy and Center for Chronobiology, University of California, San Diego, La Jolla, CA 92093-0119 USA Adele Abrahamsen (aabrahamsen@ucsd.edu) Center for Research in Language, University of California, San Diego La Jolla, CA 92093-0119 USA Abstract As tools in science, diagrams not only serve as vehicles for communication but also facilitate and constrain scientific rea- soning. We identify roles that diagrams play when computa- tional models and synthesized organisms are used to recom- pose mechanisms proposed to explain biological phenomena. Diagrams not only serve as locality aids for constructing computational models but also help in identifying ways to manipulate these models and interpret the results. Moreover, they serve as blueprints for constructing synthetic organisms and then guide the interpretation of discrepancies between these organisms and computational models. Keywords: diagrams; computational models; mechanistic explanation; circadian rhythms Introduction Cognitive scientists have contributed analyses and exper- iments on the roles diagrams play in reasoning and problem solving (e.g., Hegarty, 2004, 2011; Tversky, 2011) and have even designed new diagram formats that facilitate learning in math and science (Cheng, 2002, 2011). However, there have been only a few studies of the roles diagrams play in the natural sciences (Nersessian, 2008; Gooding, 2010). The most obvious role, evidenced by the ubiquity of diagrams in talks and publications, is communication of methods, re- sults, and proposed mechanistic explanations (Perini, 2005). Less visibly, but crucially, diagramming is a tool that scien- tists use to reason about phenomena (Bechtel & Abraham- sen, 2012) and the mechanisms that might explain them (Sheredos, Burnston, Abrahamsen, & Bechtel, in press). In many fields of biology, such as cell and molecular bi- ology, the primary goal of research in the 19 th and 20 th cen- turies was to identify and decompose mechanisms to deter- mine their parts (e.g., proteins) and operations (e.g., catalyz- ing particular chemical reactions). As recognized in the new mechanistic philosophy of science, the organization of these parts and operations must also be determined to arrive at a basic mechanistic explanation of a phenomenon of interest (Bechtel & Richardson, 1993/2010; Bechtel & Abrahamsen, 2005; Machamer, Darden, & Craver, 2000; Thagard, 2003). That is, to understand how the parts and operations contrib- ute to producing the phenomenon, researchers must recom- pose the responsible mechanism either conceptually or physically. Through most of the 20 th century this involved proposing a simple sequence in which the operations might occur, perhaps using mental simulation to verify its plausi- bility (Bechtel, 2006). By the last decades of the century, however, the operations of numerous biological mecha- nisms were understood to display nonlinear, continuous dynamics and complex interactions. As sequential organiza- tion broke down, so too did biologists’ ability to mentally track the functioning of the proposed mechanisms. Hence, they turned first to computational models and later to syn- thetic organisms as tools for recomposing mechanisms, with an emphasis on investigating the complex dynamics and interactions of operations by which a mechanism generates a phenomenon. In this paper we identify some of the roles diagrams play in the design of computational and synthetic models of mechanisms in actual scientific practice. Computational modeling in biology, in contrast to that in much of cognitive science, has been grounded in considera- ble knowledge of the physical parts and operations of the mechanisms being targeted (Bechtel & Abrahamsen, 2010). Diagrams showing how different parts are thought to oper- ate on each other serve as locality aids that “group together information that is used together” in the mechanism itself and hence often in computational models of its dynamics (Jones & Wolkenhauer, 2012, p. 705). But such diagrams also figure centrally in conceiving how manipulations made to the computational model correspond to possible perturba- tions of the mechanism, thereby relating experiments on models to experiments on actual mechanisms or to patholo- gies known to result from damage to actual mechanisms. Moreover, as the efforts to recompose mechanisms increas- ingly take a step beyond computational modeling to synthe- sizing organisms, a diagram can serve both as a blueprint for synthesizing an organism and as a medium for adjudicat- ing mismatches in behavior between organism and model. We focus on one domain of biology, circadian rhythms: the daily oscillations in a variety of physiological and be- havioral processes in species ranging from bacteria and fun- gi to plants and animals. The phenomena of greatest interest involve three characteristics of these rhythms: they are en- dogenously generated, entrained to the day-night cycle on our planet, and sustained over time (not dampened). Their complex dynamics have made circadian rhythm research a model case for developing computational models and syn- thesized organisms to determine how a proposed mecha- nism might account for relevant phenomena. By examining specific exemplars of this research, we show how diagrams can play an important role in the reasoning that goes into computational modeling and synthetic biology.