Modeling Complexity: Cognitive constraints and computational model-building in integrative systems biology

Miles Alexander James MacLeod, Nancy J. Nersessian

Research output: Contribution to journalArticle

  • 1 Citations

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.
LanguageEnglish
Article number17
JournalHistory and Philosophy of the Life Sciences
Volume40
DOIs
StatePublished - Mar 2018

Fingerprint

Cognitive Constraints
Computational Model
Systems Biology
Modeling
Cognition
Computational Modeling
Computational Simulation
Problem Solving
Cognitive Resources

Cite this

@article{24b9e15a47d9409c97a130265b38bc2b,
title = "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.",
author = "MacLeod, {Miles Alexander James} and Nersessian, {Nancy J.}",
year = "2018",
month = "3",
doi = "10.1007/s40656-017-0183-9",
language = "English",
volume = "40",
journal = "History and Philosophy of the Life Sciences",
issn = "0391-9714",
publisher = "Stazione Zoologica Anton Dohrn",

}

TY - JOUR

T1 - Modeling Complexity

T2 - History and Philosophy of the Life Sciences

AU - MacLeod,Miles Alexander James

AU - Nersessian,Nancy J.

PY - 2018/3

Y1 - 2018/3

N2 - 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.

AB - 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.

U2 - 10.1007/s40656-017-0183-9

DO - 10.1007/s40656-017-0183-9

M3 - Article

VL - 40

JO - History and Philosophy of the Life Sciences

JF - History and Philosophy of the Life Sciences

SN - 0391-9714

M1 - 17

ER -