Modeling complexity: cognitive constraints and computational model-building in integrative systems biology

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.

Rethinking Ethnography for Philosophy of Science

“…In the context of our analysis, we found the ISB labs provided opportunities to examine the applicability of this notion for characterizing and interpreting computational modelbuilding in this area. We tracked model development and probed the reasoning behind modelers' decisions Nersessian, 2013a, 2013b;MacLeod and Nersessian 2018;Chandrasekharan and Nersessian 2019).…”

“…This preliminary research enabled us to better focus our research questions, and to identify relevant data sources. In particular, they focused our attention on the various model-building practices through which bioengineering scientists attempt to manage the complexity of biological systems by utilizing engineering methods, concepts, materials, and values (e.g., Nersessian and Patton 2009;Nersessian 2012a;MacLeod and Nersessian 2013a;MacLeod and Nersessian 2018;Nersessian, forthcoming).…”

The applicability of mathematics in computational systems biology and its experimental relations

“…Principally detailed and precise models allow modelers to identify differences or errors between their models and the data, and use this to infer missing information in an account of a system. For example in one of the cases I examined the modeler was tasked with producing a model of a system for which experimenters had produced a pathway (MacLeod & Nersessian, 2018). However when a model of that system was put together it proved incapable of accounting for particular dynamics of the system.…”

Gluing life together. Computer simulation in the life sciences: an introduction

“…In modelling, not only different disciplines and styles of reasoning meet, but also the person of the scientist with her lab instruments and technologies. The authors argue that the modeller and the model form a “cognitive coupled system” (MacLeod and Nersessian 2018, p. 4). In these coupled systems, the scientists mentally simulate as well: they actively engage in cognitive simulations in order to understand and predict a limited number of aspects and outcomes of the systems they study, as well as to test hypotheses and make actual choices for their models.…”

“…The limited human cognitive ability to tackle complexity, therefore, not only informs, but effectively limits, simulation and model building. With the growing complexity of models and systems, the authors expect “cognitive factors to play an increasingly prominent role” rather than a decreasing one (MacLeod and Nersessian 2018, p. 25).…”