Inferring a nonlinear biochemical network model from a heterogeneous single-cell time course data.

ABSTRACT: Mathematical modeling and analysis of biochemical reaction networks are key routines in computational systems biology and biophysics; however, it remains difficult to choose the most valid model. Here, we propose a computational framework for data-driven and systematic inference of a nonlinear biochemical network model. The framework is based on the expectation-maximization algorithm combined with particle smoother and sparse regularization techniques. In this method, a "redundant" model consisting of an excessive number of nodes and regulatory paths is iteratively updated by eliminating unnecessary paths, resulting in an inference of the most likely model. Using artificial single-cell time-course data showing heterogeneous oscillatory behaviors, we demonstrated that this algorithm success

SUBMITTER: Shindo Y 

PROVIDER: S-EPMC5931614 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature