(Wednesday, May 29, 2002)
School of Mathematics
University of Minnesota
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.