Reverse engineering a gene network using an asynchronous parallel evolution strategy

Here, we present synchronous and asynchronous versions of the piES algorithm, and apply them to a real reverse engineering problem: inferring parameters in the gap gene network. We find that the asynchronous piES exhibits very little communication overhead, and shows significant speed-up for up to 50 nodes: the piES running on 50 nodes is nearly 10 times faster than the best serial algorithm. We compare the asynchronous piES to pLSA on the same test problem, measuring the time required to reach particular levels of residual error, and show that it shows much faster convergence than pLSA across all optimisation conditions tested.Our results demonstrate that the piES is consistently faster and more reliable than the pLSA algorithm on this problem, and scales better with increasing numbers of nodes. In addition, the piES is especially well suited to further improvements and adaptations: Firstly, the algorithm's fast initial descent speed and high reliability make it a good candidate for being used as part of a global/local search hybrid algorithm. Secondly, it has the potential to be used as part of a hierarchical evolutionary algorithm, which takes advantage of modern multi-core computing architectures.The driving aim of systems biology is to understand complex regulatory systems. A powerful tool for this is reverse engineering, a top-down approach in which we use data to infer parameter values for a model of an entire system. This differs from the traditional bottom-up approach of building up the larger picture through individually measured simple interactions. Many methods have been developed for reverse engineering of gene regulatory networks, most of which are based on expression data from gene expression microarrays. However, most of these approaches do not consider temporal or spatial aspects of gene expression. Examples of this are methods that infer regulatory modules from expression data across different experimental conditions [1,2], or methods based on (sta