How to infer gene networks from expression profiles

Abstract

Inferring, or 'reverse-engineering', gene networks can be defined as the process of identifying gene interactions from experimental data through computational analysis. Gene expression data from microarrays are typically used for this purpose. Here we compared different reverse-engineering algorithms for which ready-to-use software was available and that had been tested on experimental data sets. We show that reverse-engineering algorithms are indeed able to correctly infer regulatory interactions among genes, at least when one performs perturbation experiments complying with the algorithm requirements. These algorithms are superior to classic clustering algorithms for the purpose of finding regulatory interactions among genes, and, although further improvements are needed, have reached a discreet performance for being practically useful.

Figure 1

Flowchart to choose the most suitable network inference algorithms according to the problem to be addressed. (^*): check for independence of time points (see text for details); (BN): Bayesian networks; (DBN): Dynamic Bayesian Networks.

Figure 2

Bayesian networks: A is conditionally independent of D and E given B and C; information-theoretic networks: mutual information is 0 for statistically independent variables, and Data Processing Inequality helps pruning the network; ordinary differential equations: deterministic approach, where the rate of transcription of gene A is a function (f) of the level of its direct causal regulators.