Using Simulations to Explore Genetic Networks

University Of Washington, Seattle WA

Investigators

Abstract

This project continues development of computer methods for translating experimentally charted maps of genetic interactions into realistic, detailed, formal mathematical models that allow biologists to explore whether and how genes, interacting in regulatory networks, lay down striped spatial patterns of proteins which prefigure the segmented body plan that later develops. Approximately 1200 person-years of experimental research, in many labs worldwide, have culminated in a detailed understanding of how the so-called segment polarity genes cross regulate each other in early fruit fly embryos. This project synthesizes those data into a systems-level model that quantifies how the interactions between these genes, characterized principally in fruit flies, but thought to be shared by all higher animals, act synergistically to endow this network with astonishing robustness. That is, the systems level model predicts that the network can continue to perform its task correctly even when the strengths of all the interactions between its genes change greatly. The project entails refinement of that case study, and creation of a new model of another gene network (the neurogenic network). The project will ascertain whether this robustness, found in the first network so modeled occurs also in other experimentally well studied networks. It will use computer modeling to ascertain how such robustness could have evolved. The research will include experiments on real fruit fly embryos to find out whether the robustness, which the computer model predicts the gene network could have, actually exists in real embryos. Do, and how do, gene networks in living embryos contend with genetic and environmental variation? Do embryos actually experience significant amounts of "developmental noise", and if so, how do developmental mechanisms either exploit or insulate themselves from such variation? This cross-disciplinary project will try to answer such questions using a blend of experimental developmental biology, computer-implemented 3-D analysis of laser scanning confocal micrographic data, implementation of detailed realistic mathematical models, computer algorithms for solving differential equation systems and searching/visualizing their associated high-dimensional parameter spaces.