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Reveal, A General Reverse Engineering Algorithm For Inference Of Genetic Network Architectures (1998)
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BibTeX
@MISC{Liang98reveal,a,
author = {Shoudan Liang and Stefanie Fuhrman and Roland Somogyi},
title = {Reveal, A General Reverse Engineering Algorithm For Inference Of Genetic Network Architectures},
year = {1998}
}
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Abstract
Given the immanent gene expression mapping covering whole genomes during development, health and disease, we seek computational methods to maximize functional inference from such large data sets. Is it possible, in principle, to completely infer a complex regulatory network architecture from input/output patterns of its variables? We investigated this possibility using binary models of genetic networks. Trajectories, or state transition tables of Boolean nets, resemble time series of gene expression. By systematically analyzing the mutual information between input states and output states, one is able to infer the sets of input elements controlling each element or gene in the network. This process is unequivocal and exact for complete state transition tables. We implemented this REVerse Engineering ALgorithm (REVEAL) in a C program, and found the problem to be tractable within the conditions tested so far. For n=50 (elements) and k=3 (inputs per element), the analysis of incomplete state transition tables (100 state transition pairs out of a possible 10 15) reliably produced the original rule and wiring sets. While this study is limited to synchronous Boolean
Keyphrases
input output gene expression original rule complete state transition table state transition table incomplete state transition table binary model output state large data set complex regulatory network architecture input output pattern functional inference computational method immanent gene expression synchronous boolean genetic network state transition input element reverse engineering algorithm boolean net mutual information whole genome resemble time series input state
