Connections matter: Boolean modeling of gene regulatory networks

Biological systems often form complex networks of interaction. For example, external signals are detected and internalized by a signal transduction network. Or, the expression of genes is regulated by interactions with other genes and gene products. Recent experimental advances uncovered the qualitative structure of many gene control networks, creating a surge of interest in the quantitative description of gene regulation. In this talk I will focus on the segment polarity genes of the fruit fly Drosophila melanogaster, and the network of interactions determining the stability of their expression. I will present a Boolean representation of this network that assumes that genes and proteins are either ON or OFF, and their interactions can be formulated as logical functions. This simple model is able to reproduce the observed spatial patterns of the segment polarity genes, as well as the patterns obtained in gene mutation experiments. In addition, the Boolean representation allows us to determine the possible steady state patterns, and to identify the initial conditions that lead to certain steady states. I propose this type of modeling as a first, qualitative step in understanding complex networks. The success of a Boolean representation strongly suggests that the topology of the network is correctly taken into account, and a more quantitative approach can be used.

The speaker is a candidate for a joint faculty position in departments of Mathematics and Physics & Astronomy.