Using dynamic noise propagation to infer causal regulatory relationships in biochemical networks.

ABSTRACT: Cellular decision making is accomplished by complex networks, the structure of which has traditionally been inferred from mean gene expression data. In addition to mean data, quantitative measures of distributions across a population can be obtained using techniques such as flow cytometry that measure expression in single cells. The resulting distributions, which reflect a population's variability or noise, constitute a potentially rich source of information for network reconstruction. A significant portion of molecular noise in a biological process is propagated from the upstream regulators. This propagated component provides additional information about causal network connections. Here, we devise a procedure in which we exploit equations for dynamic noise propagation in a network under n

SUBMITTER: Lipinski-Kruszka J 

PROVIDER: S-EPMC4384829 | biostudies-literature | 2015 Mar

REPOSITORIES: biostudies-literature