Approximate Bayesian approaches for reverse engineering biological networks
Rau , Andrea (INRA , Jouy-En-Josas (France). UMR 1313 Génétique Animale et Biologie Intégrative) | Jaffrézic , Florence (INRA , Jouy-En-Josas (France). UMR 1313 Génétique Animale et Biologie Intégrative) | Foulley , Jean Louis (INRA , Jouy-En-Josas (France). UMR 1313 Génétique Animale et Biologie Intégrative) | Doerge , R. W. (Purdue University, West Lafayette, IN(Etats-Unis). Department of Statistics)
Genes are known to interact with one another through proteins by regulating the rate at which gene transcription takes place. As such, identifying these gene-to-gene interactions is essential to improving our knowledge of how complex biological systems work. In recent years, a growing body of work has focused on methods for reverse-engineering these so-called gene regula-tory networks from time-course gene expression data. However, reconstruction of these networks is often complicated by the large number of genes potentially involved in a given network and the limited number of time points and biological replicates typically measured. Bayesian methods are particularly well-suited for dealing with problems of this nature, as they provide a systematic way to deal with different sources of variation and allow for a measure of uncertainty in parameter esti-mates through posterior distributions, rather than point estimates. Our current work examines the application of approximate Bayesian methodology for the purpose of reverse engineering regula-tory networks from time-course gene expression data. We demonstrate the advantages of our pro-posed approximate Bayesian approaches by comparing their performance on a well-characterized pathway in Escherichia coli.
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