Overview and Evaluation of Recent Methods for Statistical Inference of Gene Regulatory Networks from Time Series Data

Abstract

A challenging problem in systems biology is the reconstruction of gene regulatory networks from postgenomic data. A variety of reverse engineering methods from machine learning and computational statistics have been proposed in the literature. However, deciding on the best method to adopt for a particular application or data set might be a confusing task. The present chapter provides a broad overview of state-of-the-art methods with an emphasis on conceptual understanding rather than a deluge of mathematical details, and the pros and cons of the various approaches are discussed. Guidance on practical applications with pointers to publicly available software implementations are included. The chapter concludes with a comprehensive comparative benchmark study on simulated data and a real-work application taken from the current plant systems biology.

Keywords: Arabidopsis thaliana; Bayesian networks; Bio-PEPA; Chemical model averaging; Circadian regulation; Gaussian graphical models; Gaussian processes; Gene regulatory networks; Hierarchical Bayesian models; Network inference scoring scheme; Sparse regression.