Efficient Inference of Parsimonious Phenomenological Models of Cellular Dynamics Using S-Systems and Alternating Regression

• Type of Material: Article
• Authors: Bryan C. Daniels, University of Wisconsin; Ilya Nemenman, Emory University
• Language: English
• Date: 2015-03-25
• Publisher: Public Library of Science
• Publication Version: Final Published Version
• Copyright Statement: © 2015 Daniels, Nemenman.
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1371/journal.pone.0119821
• Title of Journal or Parent Work: PLoS ONE
• ISSN: 1932-6203
• Volume: 10
• Issue: 3
• Start Page: e0119821
• End Page: e0119821
• Grant/Funding Information: The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.; This research was supported in part by the James S. McDonnell foundation Grant No. 220020321 (IN), a grant from the John Templeton Foundation for the study of complexity (BCD), the Los Alamos National Laboratory Directed Research and Development Program (IN and BD), and NSF Grant No. 0904863 (BD).
• Research Categories: Health Sciences, General; Biology, General

Abstract

The nonlinearity of dynamics in systems biology makes it hard to infer them from experimental data. Simple linear models are computationally efficient, but cannot incorporate these important nonlinearities. An adaptive method based on the S-system formalism, which is a sensible representation of nonlinear mass-action kinetics typically found in cellular dynamics, maintains the efficiency of linear regression. We combine this approach with adaptive model selection to obtain efficient and parsimonious representations of cellular dynamics. The approach is tested by inferring the dynamics of yeast glycolysis from simulated data. With little computing time, it produces dynamical models with high predictive power and with structural complexity adapted to the difficulty of the inference problem.