Title: New insights in Approximate Bayesian Computation algorithms for network reverse-engineering
Authors: Nicolas Jung - Universite de Strasbourg (France)
Frederic Bertrand - Universite de Strasbourg (France)
Myriam Maumy-Bertrand - Universite de Strasbourg (France) [presenting]
Khadija Musayeva - Universite de Strasbourg (France)
Abstract: Elucidating gene regulatory network is an important step towards understanding the normal cell physiology and complex pathological phenotype. Reverse-engineering consists in using gene expression over time or over different experimental conditions to discover the structure of the gene network in a targeted cellular process. The fact that gene expression data are usually noisy, highly correlated, and have high dimensionality explains the need for specic statistical methods to reverse engineer the underlying network. Among known methods, Approximate Bayesian Computation (ABC) algorithms have not been thoroughly studied for network inference. Due to the computational overhead their application is also limited to a small number of genes. We have developed a new multi-level ABC approach that has less computational cost. At the first level, the method captures the global properties of the network, such as scale-freeness and clustering coecients, whereas the second level is targeted to capture local properties, including the probability of each couple of genes being linked. Our approach is evaluated on longitudinal expression data in Escherichia coli.