Abstract
Modern integrative systems biology defines itself by the complexity of the problems it takes on through computational modeling and simulation. However in integrative systems biology computers do not solve problems alone. Problem solving depends as ever on human cognitive resources. Current philosophical accounts hint at their importance, but it remains to be understood what roles human cognition plays in computational modeling. In this paper we focus on practices through which modelers in systems biology use computational simulation and other tools to handle the cognitive complexity of their modeling problems so as to be able to make significant contributions to understanding, intervening in, and controlling complex biological systems. We thus show how cognition, especially processes of simulative mental modeling, is implicated centrally in processes of model-building. At the same time we suggest how the representational choices of what to model in systems biology are limited or constrained as a result. Such constraints help us both understand and rationalize the restricted form that problem solving takes in the field and why its results do not always measure up to expectations.
Highlights
Modern computational science is complex: cognitively, technologically, and collaboratively
Modern integrative systems biology defines itself by the complexity of the problems it takes on through computational modeling and simulation
In this paper we focus on practices through which modelers in systems biology use computational simulation and other tools to handle the cognitive complexity of their modeling problems so as to be able to make significant contributions to understanding, intervening in, and controlling complex biological systems
Summary
Modern computational science is complex: cognitively, technologically, and collaboratively. Our primary goals in this paper are (1) to help understand how these ‘‘uni-modal’’ computational modelers are able to handle the complexity of their modeling problems cognitively so as to build at least partially accurate models, and in turn make sometimes profound contributions to the understanding of the systems they are modeling; and (2) to understand, to the extent possible given our data, the ways in which cognitive capacities and constraints play a role in the representations they build and methodological choices they make These choices might initially appear ineffective given the epistemological goals of the field, but can be rationalized on cognitive grounds. Wimsatt for instance has leveraged Levin’s original discussion of modeling strategies for simplifying and idealizing
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