Comparing network models of gap gene interaction during Drosophila melanogaster development

Abstract

Early development of Drosophila melanogaster (fruit fly) facilitated by the gap gene network has been shown to be incredibly robust, and the same patterns emerge even when the process is seriously disrupted. In this thesis we plan to investigate this robustness using a previously developed computational framework called Dynamic Signatures Generated by Regulatory Networks (DSGRN). The principal result of this research has been in extending DSGRN to study how tissue-scale behavior arises from network behavior in individual cells, such as gap gene expression along the anterior-posterior (A-P) axis of the Drosophila embryo. Essentially, we extend DSGRN to study cellular systems where each cell contains the same network structure but operates under a parameter regime that changes continuously from cell to cell. We then use this extension to study the robustness of two different models of the gap gene network by looking at the number of paths in each network that can produce the observed gap gene expression. While we found that both networks are capable or replicating the data, we hypothesize that one network is a better fit than the other. This is significant in two ways; finding paths shows us that the spatial data can be replicated using a single network with different parameters along the A-P axis, and that we may be able to use this extension of DSGRN to rank network models.