Gene Network Inference
from Large-Scale
Gene Expression Data

The traditional approach to research in Molecular Biology has been an inherently local one, examining and collecting data on a single gene, a single protein or a single reaction at a time. This is, of course, the classical reductionist stance: to understand the whole, one must first understand the parts. Over the years, this approach has led to remarkable achievements, allowing us to make highly accurate biochemical models of such favorites as bacteriophage Lambda.

However, with the advent of the "Age of Genomics" an entirely new class of data is emerging. Can we really expect to construct a detailed biochemical model of, say, an entire yeast cell with some 6000 genes (only about 1000 of which were defined before sequencing started, and about 50% of which are clearly related to other known genes), by analyzing each gene and determining all the binding and reaction constants one by one? Likewise, from the perspective of drug target identification for human disease, we cannot realistically hope to characterize all the relevant molecular interactions one-by-one as a requirement for building a predictive disease model.

There is a need for methods that can handle this data in a global fashion, and that can analyze such large systems at some intermediate level, without going all the way down to the exact biochemical reactions. At the very least, such an analysis could help guide the traditional pharmacological and biochemical approaches towards those genes most worthy of attention among the thousands of newly discovered genes. Ideally, a sufficiently predictive and explanatory model at an intermediate level could obviate the need for an exact understanding of the system at the biochemical level.

What sort of data is available?

Data requirements for gene network inference

Additive regulation models


UPCOMING:

Gene network inference using recurrent neural networks

Data sources

Bibliography


Publications:

Other useful papers and links:

© Copyright 1997 by Patrik D'haeseleer, patrik at cs dot unm dot edu
c/o Computer Science Department, University of New Mexico, Albuquerque, NM, 87131

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