Reverse Engineering the Gap Gene Network of Drosophila melanogaster

A fundamental problem in functional genomics is to determine the structure and dynamics of genetic networks based on expression data. We describe a new strategy for solving this problem and apply it to recently published data on early Drosophila melanogaster development. Our method is orders of magnitude faster than current fitting methods and allows us to fit different types of rules for expressing regulatory relationships. Specifically, we use our approach to fit models using a smooth nonlinear formalism for modeling gene regulation (gene circuits) as well as models using logical rules based on activation and repression thresholds for transcription factors. Our technique also allows us to infer regulatory relationships de novo or to test network structures suggested by the literature. We fit a series of models to test several outstanding questions about gap gene regulation, including regulation of and by hunchback and the role of autoactivation. Based on our modeling results and validation against the experimental literature, we propose a revised network structure for the gap gene system. Interestingly, some relationships in standard textbook models of gap gene regulation appear to be unnecessary for or even inconsistent with the details of gap gene expression during wild-type development.Synopsis: Modeling dynamical systems involves determining which elements of the system interact with which, and what is the nature of the interaction. In the context of modeling gene expression dynamics, this question equates to determining regulatory relationships between genes. Perkins and colleagues present a new computational method for fitting differential equation models of time series data, and apply it to expression data from the well-known segmentation network of Drosophila melanogaster. The method is orders of magnitude faster than other approaches that produce fits of comparable quality, such as Simulated Annealing. The authors show that it is possible to detect interactions de novo as well as to test existing regulatory hypotheses, and they propose a revised network structure for the gap gene system, based on their modeling efforts and on other experimental literature.