Article ID: CBB511081415

Modeling systems-level dynamics: Understanding without mechanistic explanation in integrative systems biology (2015)

unapi

In this paper we draw upon rich ethnographic data of two systems biology labs to explore the roles of explanation and understanding in large-scale systems modeling. We illustrate practices that depart from the goal of dynamic mechanistic explanation for the sake of more limited modeling goals. These processes use abstract mathematical formulations of bio-molecular interactions and data fitting techniques which we call top-down abstraction to trade away accurate mechanistic accounts of large-scale systems for specific information about aspects of those systems. We characterize these practices as pragmatic responses to the constraints many modelers of large-scale systems face, which in turn generate more limited pragmatic non-mechanistic forms of understanding of systems. These forms aim at knowledge of how to predict system responses in order to manipulate and control some aspects of them. We propose that this analysis of understanding provides a way to interpret what many systems biologists are aiming for in practice when they talk about the objective of a “systems-level understanding.”

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Authors & Contributors
MacLeod, Miles
Nersessian, Nancy J.
Green, Sara
Kranke, Nina
Fleischmann, Kenneth R.
Moll, Remington J.
Concepts
Models and modeling in science
Data analysis
Systems biology
Computational methods
Philosophy of science
Biology
Time Periods
21st century
20th century, late
20th century
Places
United States
Switzerland
Institutions
Club of Rome
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