Robustness of dynamic gene regulatory networks in Neisseria

Abstract

Gene regulatory networks are made of highly tuned, sparse and dynamical operations. We consider the case of the Neisseria meningitidis bacterium, a causative agent of life-threatening infections such as meningitis, and aim to infer a robust net- work of interactions across sixty proteins based on a detailed time course gene expres- sion study. We consider the problem of estimating a sparse dynamic Gaussian graphical model with L1 penalized maximum likelihood under a structured precision matrix. The structure can consist of specific time dynamics, known presence or absence of links in the graphical model or equality constraints on the parameters. The authors developed a new optimization algorithm for constrained penalized maximum likelihood, which returns a sequence of networks along a solution path. In this paper, we propose a gener- alized cross-validation approach to select a suitable penalty parameter and a bootstrap sampling approach to robustify the network.