Inference of gene regulatory networks incorporating multi-source biological knowledge via a state space model with L1 regularization.

ABSTRACT: Comprehensive understanding of gene regulatory networks (GRNs) is a major challenge in the field of systems biology. Currently, there are two main approaches in GRN analysis using time-course observation data, namely an ordinary differential equation (ODE)-based approach and a statistical model-based approach. The ODE-based approach can generate complex dynamics of GRNs according to biologically validated nonlinear models. However, it cannot be applied to ten or more genes to simultaneously estimate system dynamics and regulatory relationships due to the computational difficulties. The statistical model-based approach uses highly abstract models to simply describe biological systems and to infer relationships among several hundreds of genes from the data. However, the high abstraction gene

SUBMITTER: Hasegawa T 

PROVIDER: S-EPMC4146587 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature