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In this paper we aim to give an analysis and cognitive rationalization of a common practice or strategy of modeling in systems biology known as a middle-out modeling strategy. The strategy in the cases we look at is facilitated through the construction of what can be called mesoscopic models. Many models built in computational systems biology are mesoscopic (midsize) in scale. Such models lack the sufficient fidelity to serve as robust predictors of the behaviors of complex biological systems, one of the signature goals of the field. This puts some pressure on the field to provide reasons for why and how these practices are warranted despite not meeting the stated goals of the field. Using the results of ethnographic study of problem-solving practices in systems biology, we aim to examine the middle-out strategy and mesoscopic modeling in detail and to show that these practices are rational responses to complex problem solving tasks on cognitive grounds in particular. However making this claim requires us to update the standard notion of bounded rationality to take account of how human cognition is coupled to computation in these contexts. Our account fleshes out the idea that has been raised by some philosophers on the "hybrid" nature of computational modeling and simulation. What we call "coupling" both extends modelers' capacities to handle complex systems, but also produces various cognitive and computational constraints which need to be taken into account in any computational problem solving strategy seeking to maintain insight and control over the models produced., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

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.

Designing, building, and experimenting with physical simulation models are central problem-solving practices in the engineering sciences. Model-based simulation is an epistemic activity that includes exploration, generation and testing of hypotheses, explanation, and inference. This paper argues that to interpret and understand how these simulation models function in creating knowledge and technologies requires construing problem solving as accomplished by a researcher-artifact system. It draws on and further develops the framework of "distributed cognition" to interpret data collected in ethnographic and cognitive-historical studies of two biomedical engineering research laboratories, and articulates the notion of distributed model-based cognition to answer the question posed in the title., (Copyright © 2009 Cognitive Science Society, Inc.)