Inferring Biological Networks from Time-Course Observations Using a Non-linear Vector Autoregressive Model

Abstract

Cellular functions are increasingly viewed as being regulated through networks of molecules working in parallel. Discovery and characterization of these networks is fundamental to understanding diseases and developing treatments. In this work we present a method for inferring biological networks from time-course measurements collected on the molecular components of the networks. We employ the framework of Granger causality which has been previously used for network inference from time-course data. However, whereas previous work focused on the linear component of molecular interactions, in this work, we extend the method to accommodate higher order interactions. Using data generated from an in silico E. coli network, we show that by modeling higher order interactions, the proposed method improves on earlier work that only considered linear interactions. We further show empirically that our proposed model selection criteria provides a good balance between sensitivity and specificity in discovering molecular interactions. Lastly, we illustrate the utility of this method by performing network inference on experimental data collected on the receptor tyrosine kinase cell signaling pathway.