Inferring models of gene expression dynamics.
We study the problem of identifying genetic networks in which expression dynamics are modeled by a differential equation that uses logical rules to specify time derivatives. We make three main contributions. First, we describe computationally efficient procedures for identifying the structure and dynamics of such networks from expression time series. Second, we derive predictions for the expected amount of data needed to identify randomly generated networks. Third, if expression values are available for only some of the genes, we show that the structure of the network for these "visible" genes can be identified and that the size and overall complexity of the network can be estimated. We validate these procedures and predictions using simulation experiments based on randomly generated networks with up to 30,000 genes and 17 distinct regulators per gene and on a network that models floral morphogenesis in Arabidopsis thaliana.
McGill Centre for Bioinformatics, McGill University, 3775 University St. Montreal, Quebec, Canada H3A 2B4. perkins@mcb.mcgill.ca
Theodore J
TJ
Perkins
Mike
M
Hallett
Leon
L
Glass
D017360:Arabidopsis / Q000235:genetics; D019295:Computational Biology; D005786:Gene Expression Regulation; D018507:Gene Expression Regulation, Developmental; D018506:Gene Expression Regulation, Plant; D008957:Models, Genetic; D009024:Morphogenesis / Q000235:genetics; D013997:Time Factors