A computational framework for gene regulatory network inference that combines multiple methods and datasets

This paper presents a new method for network inference, which uses multi-objective optimisation (MOO) to integrate multiple inference methods and experiments. We illustrate the potential of the methodology by combining ODE and correlation-based network inference procedures as well as time course and gene inactivation experiments. Here we show that our methodology is effective for a wide spectrum of data sets and method integration strategies.The approach we present in this paper is flexible and can be used in any scenario that benefits from integration of multiple sources of information and modelling procedures in the inference process. Moreover, the application of this method to two case studies representative of bacteria and vertebrate systems has shown potential in identifying key regulators of important biological processes.In the last ten years the development of functional genomics technologies has provided us with the ability to generate quantitative data representing the molecular state of cells and tissues at a genome level [1,2]. These datasets can be in the form of a time series representing the dynamics of gene expression profiles (e.g. mRNA, proteins and metabolites) in response to a given stimulus, such as an environmental perturbation, the effect of a growth factor or an experimentally induced gene deletion. Despite the relatively large amount of information, predicting underlying regulatory networks from observational data is still not trivial and is a matter of intense research [3].A number of reverse-engineering approaches have been proposed. Some of these are designed to infer networks from a compendium of perturbation experiments while others are able to use time course data to develop dynamical models of gene interaction. Bayesian networks have been among the first to be applied to biological problems [4]. They work by inferring probabilistic relationships between variables, can use either time course or steady state data and allow integration o