In-depth understanding of molecular networks promises much needed insight into complex diseases, novel drug targets, and integration of diagnostics with treatments for individualized therapies. Recent progress in molecular biology, measurement technologies and computational performance is making it possible to systematically investigate these regulatory networks. On the one hand, high-throughput methods are providing us with global gene activity profiles characterizing the output of genetic networks. On the other hand, the challenge now lies in applying advanced data mining and modeling methods for discovering novel functional connections between genes and their integration into pathways and networks. While clustering co-expression profiles may aid in the exploration of shared regulatory inputs and functional pathways, we must take the next step of identifying direct causal relationships, i.e. who is regulating whom and how. There are several approaches to this problem of network reverse engineering, ranging from discrete, to continuous linear and non-linear models. Opportunities are now arising for taking a major leap forward in datamining for the discovery of highly valuable predictive relationships, in silico exploration of global behavior in network models, and systematic experimental design to provide the biological facts for network inference.
Joint work by Roland Somogyi, Alan Ableson, Max Kotlyar, Hai Pham, Steve Misener, and Evan Steeg.
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