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Related Experiment Video

Updated: Jan 20, 2026

The Use of Chemostats in Microbial Systems Biology

The Use of Chemostats in Microbial Systems Biology

Published on: October 14, 2013

Mesoscopic modeling as a cognitive strategy for handling complex biological systems.

Miles MacLeod¹, Nancy J Nersessian²

¹Department of Philosophy, University of Twente, Drienerlolaan 5, 7522 NB, Enschede, the Netherlands.

Studies in History and Philosophy of Biological and Biomedical Sciences

|August 19, 2019

Summary

Middle-out modeling in systems biology uses mesoscopic models. This strategy is a rational cognitive approach for complex problem-solving, enhancing modeler capabilities through human-computation coupling.

Keywords:

Bounded rationality Distributed cognition Mental modeling Mesoscopic modeling Middle-out strategy Model building Systems biology

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Area of Science:

Systems Biology
Computational Biology
Cognitive Science

Background:

Computational models in systems biology often lack predictive fidelity for complex biological systems.
This necessitates justification for the use of mesoscopic (midsize) models.
The middle-out modeling strategy is a common practice in the field.

Purpose of the Study:

To provide a cognitive rationalization for the middle-out modeling strategy.
To analyze the use of mesoscopic models in systems biology.
To demonstrate that these practices are rational responses to complex problem-solving.

Main Methods:

Ethnographic study of problem-solving practices in systems biology.
Analysis of the middle-out modeling strategy and mesoscopic models.
Updating the concept of bounded rationality to include human-computation coupling.

Main Results:

Middle-out modeling and mesoscopic models are cognitively rational strategies.
Human cognition coupled with computation extends modeler capabilities.
This coupling introduces cognitive and computational constraints.

Conclusions:

The middle-out modeling strategy is a warranted practice in systems biology.
Understanding human-computation coupling is crucial for effective modeling.
Mesoscopic models, when coupled with cognition, support complex problem-solving insight and control.