Genetic network inference: from co-expression clustering to reverse engineering

Abstract

Advances in molecular biological, analytical and computational technologies are enabling us to systematically investigate the complex molecular processes underlying biological systems. In particular, using high-throughput gene expression assays, we are able to measure the output of the gene regulatory network. We aim here to review datamining and modeling approaches for conceptualizing and unraveling the functional relationships implicit in these datasets. Clustering of co-expression profiles allows us to infer shared regulatory inputs and functional pathways. We discuss various aspects of clustering, ranging from distance measures to clustering algorithms and multiple-cluster memberships. More advanced analysis aims to infer causal connections between genes directly, i.e. who is regulating whom and how. We discuss several approaches to the problem of reverse engineering of genetic networks, from discrete Boolean networks, to continuous linear and non-linear models. We conclude that the combination of predictive modeling with systematic experimental verification will be required to gain a deeper insight into living organisms, therapeutic targeting and bioengineering.

Automating Genetic Network Inference with Minimal Physical Experimentation Using Coevolution

“…The field of gene network inference is a rapidly burgeoning subfield in system biology [15], and is concerned with inferring genetic regulatory networks based on the results of a set of tests performed on the network in question. Many different models of the underlying genetic network have been used, usually classified based on the amount of biological detail inherent in the model (see [8] and [12] for an overview).…”

“…In addition to the type of model, several methods have been used to infer genetic networks, including clustering algorithms (see [8] for an overview), correlation metrics [1], linear algebra [6], simulated annealing [23] and genetic algorithms [26] [11].…”

“…As pointed out in [8], it is desirable to minimize the number of input/output pairs required, so that a minimum of experiments have to be conducted. Also, the type of experiment required should be as cheap as possible in terms of experimental difficulty and accuracy of acquired output data.…”

The cognitive phenotype of Down syndrome: Insights from intracellular network analysis

“…94 This approach to constructing and validating models is also referred to as reverse engineering. Kurata and coworkers, 95 D'haeseleer and coworkers, 96 and others distinguish between forward engineering of biochemical networks and reverse engineering of biochemical networks. Forward engineering is when detailed models are constructed with the kinetic rates and concentration of the molecular species are assembled in silico from experiments.…”

Learning Gene Regulatory Networks via Globally Regularized Risk Minimization

“…Yet it is well known that genes that share the same expression pattern are likely to be involved in the same regulatory process, and therefore share the same (or at least a similar) set of regulators [13]. The main question we investigate is how to exploit biologically significant knowledge about co-regulation to improve the inference of the underlying gene regulatory network from expression data.…”