An overview of the statistical methods used for inferring gene regulatory networks and protein-protein interaction networks

Abstract

The large influx of data from high-throughput genomic and proteomic technologies has encouraged the researchers to seek approaches for understanding the structure of gene regulatory networks and proteomic networks. This work reviews some of the most important statistical methods used for modeling of gene regulatory networks (GRNs) and protein-protein interaction (PPI) networks. The paper focuses on the recent advances in the statistical graphical modeling techniques, state-space representation models, and information theoretic methods that were proposed for inferring the topology of GRNs. It appears that the problem of inferring the structure of PPI networks is quite different from that of GRNs. Clustering and probabilistic graphical modeling techniques are of prime importance in the statistical inference of PPI networks, and some of the recent approaches using these techniques are also reviewed in this paper. Performance evaluation criteria for the approaches used for modeling GRNs and PPI networks are also discussed.

Figure 1

Central dogma of molecular biology.

Figure 2

Expression estimation in RNA-Seq.

Figure 3

Qualitative probabilistic network (red) for a Bayesian network (blue).

Figure 4

State-Space model.

Figure 5

Markov chain (blue) and common cause (red).

Figure 6

An integrated cellular network.