✦ LIBER ✦

Inferring models of gene expression dynamics

✍ Scribed by Theodore J Perkins; Mike Hallett; Leon Glass

Publisher: Elsevier Science
Year: 2004
Tongue: English
Weight: 395 KB
Volume: 230
Category: Article
ISSN: 0022-5193
DOI: 10.1016/j.jtbi.2004.05.022

No coin nor oath required. For personal study only.

✦ Synopsis

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.

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