Efficient inference of parsimonious phenomenological models of cellular dynamics using S-systems and alternating regression

BRYAN DANIELS, Ilya Nemenman

Research output: Contribution to journalArticle

18 Citations (Scopus)

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.

Original languageEnglish (US)
Article numbere0119821
JournalPLoS One
Volume10
Issue number3
DOIs
StatePublished - Mar 25 2015
Externally publishedYes

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Linear Models
Systems Biology
Glycolysis
Yeasts
glycolysis
Linear regression
Yeast
linear models
yeasts
kinetics
Biological Sciences
Kinetics
methodology

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)

Cite this

Efficient inference of parsimonious phenomenological models of cellular dynamics using S-systems and alternating regression. / DANIELS, BRYAN; Nemenman, Ilya.

In: PLoS One, Vol. 10, No. 3, e0119821, 25.03.2015.

Research output: Contribution to journalArticle

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