Numerical Parameter Space Compression and Its Application to Biophysical Models

Physical models of biological systems can become difficult to interpret when they have a large number of parameters. But the models themselves actually depend on (i.e., are sensitive to) only a subset of those parameters. This phenomenon is due to parameter space compression (PSC), in which a subset...

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Bibliographic Details
Published in:Biophysical journal 2020-03, Vol.118 (6), p.1455-1465
Main Authors: Hsu, Chieh-Ting (Jimmy), Brouhard, Gary J., François, Paul
Format: Article
Language:English
Subjects:
Biophysics
Models, Biological
Models, Theoretical
Physical Phenomena
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Summary:Physical models of biological systems can become difficult to interpret when they have a large number of parameters. But the models themselves actually depend on (i.e., are sensitive to) only a subset of those parameters. This phenomenon is due to parameter space compression (PSC), in which a subset of parameters emerges as “stiff” as a function of time or space. PSC has only been used to explain analytically solvable physics models. We have generalized this result by developing a numerical approach to PSC that can be applied to any computational model. We validated our method against analytically solvable models of a random walk with drift and protein production and degradation. We then applied our method to a simple computational model of microtubule dynamic instability. We propose that numerical PSC has the potential to identify the low-dimensional structure of many computational models in biophysics. The low-dimensional structure of a model is easier to interpret and identifies the mechanisms and experiments that best characterize the system.
ISSN:0006-3495
1542-0086
DOI:10.1016/j.bpj.2020.01.023