A Cognitive Study of Modeling in Systems Biology

In this chapter we present some results and insights of a 4-year study of cognitive practices in systems biology. Systems biology is of interest to cognitive scientists and philosophers for a number of reasons including its interdisciplinarity and its application of systems-level and mathematical explanations to biology, but one source of interest is the ability of modelers to engage with highly complex nonlinear biological networks. In this study our group tracked model-building processes among modelers in two systems biology labs during the course of their graduate degree projects. We were able to identify the main cognitive strategies by which these modelers were able to keep complexity under control, develop an intrinsic understanding of their systems, and turn this into informative inferences on their systems. They were able to this while keeping within cognitive constraints, such as working memory constraints. Computational simulation and visualization play a large role here. We identify mesoscopic and middle-out modeling as important general step-by-step strategy when it comes to managing complexity. Overall these observations can help give insights for new modelers on the kinds of day-to-day processes they may need to engage in to build models effectively and provide some theory to experienced modelers on how and why modeling might need to be constrained on cognitive terms.