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Modeling complexity: cognitive constraints and computational model-building in integrative systems biology
Miles MacLeod1, Nancy J Nersessian2
1Department of Philosophy, University of Twente, Drienerlolaan 5, 7522 NB, Enschede, The Netherlands. m.a.j.macleod@utwente.nl.
History and Philosophy of the Life Sciences
|January 10, 2018
Summary
Human cognition, particularly simulative mental modeling, is central to computational modeling in systems biology. This cognitive role shapes problem-solving approaches and influences the scope and outcomes of biological system research.
Area of Science:
- Integrative systems biology
- Philosophy of science
- Cognitive science
Background:
- Systems biology relies on computational modeling and simulation for complex biological problems.
- The role of human cognitive resources in computational modeling remains underexplored.
- Existing philosophical accounts acknowledge but do not fully detail cognitive contributions.
Purpose of the Study:
- To investigate the specific roles of human cognition in computational modeling within systems biology.
- To analyze how modelers use cognitive tools to manage complexity in biological systems.
- To understand how cognitive processes influence model-building and representational choices.
Main Methods:
- Focus on the practices of systems biology modelers.
- Analysis of how cognitive complexity is handled through simulation and other tools.
- Examination of simulative mental modeling processes.
Main Results:
- Human cognition, especially simulative mental modeling, is central to model-building in systems biology.
- Cognitive processes shape and constrain representational choices in what is modeled.
- These constraints help explain the nature of problem-solving and its limitations in the field.
Conclusions:
- Cognitive resources are indispensable for computational modeling in systems biology.
- Understanding cognitive roles clarifies the scope and limitations of current problem-solving strategies.
- This perspective can rationalize why results may not always meet expectations.
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