Abstract
BackgroundReverse engineering in systems biology entails inference of gene regulatory networks from observational data. This data typically include gene expression measurements of wild type and mutant cells in response to a given stimulus. It has been shown that when more than one type of experiment is used in the network inference process the accuracy is higher. Therefore the development of generally applicable and effective methodologies that embed multiple sources of information in a single computational framework is a worthwhile objective.ResultsThis 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.ConclusionsThe 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.
Highlights
Reverse engineering in systems biology entails inference of gene regulatory networks from observational data
We considered the possibility that multi-objective optimisation (MOO) might be used to integrate two different networkinference procedures, for example applied to independent replicates of a time-course experiment
Different procedures may be used in combination using an ensemble approach; in this paper we describe the results of combining MOO-tp after inactivation (Tr) with MOO-Tc (Figure 1, MOO-Tens) and MOO-Sr with MOO-Sc (Figure 1, MOO-Sens)
Summary
Reverse engineering in systems biology entails inference of gene regulatory networks from observational data. 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. 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. For an extensive overview of these methodologies see: [11,12]
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