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A New Formulation of Gene Networks

Most difficulties associated with implementing gene therapy, which could prevent the onset of hereditary diseases in genetically susceptible individuals, are due to the complexity of the underlying regulatory networks. The forms of nonlinear interactions between the hundreds of genes, proteins, and metabolites in these networks are not known with sufficient accuracy to compute changes necessary to move their steady states. An alternative approach to analyzing a biological process is to limit consideration to genes on the network. Steady state solutions of these influence networks can be obtained from DNA microarray experiments on mutants; in principle, effective interaction between their nodes can be inferred from many such solutions. However, since they contain a large number of nodes, the computation of influence networks requires a prohibitively large set of microarray experiments. Furthermore, since error rates associated with gene expression measurements are large, the inversions required to deduce a network increase the uncertainty to a level that makes verifiable predictions impossible. Here, we propose a reduction of influence networks that retains the ability to address several critical questions on the underlying system. The construction relies on the identification of a small group of genes that can be used to affect changes in other nodes of the network. The reduced network, which we refer to as the effective empirical subnetwork (EES), can be computed using results from a small number of microarray experiments on genetically perturbed systems. It can be used to make predictions on the expression profiles of other mutants, and to compute how to implement pre-specified changes in the steady state of the underlying biological process. These assertions are verified in a synthetic influence network. We also use previously published experimental data to compute the EES associated with an oxygen deprivation network of E.coli, and use it to predict gene expression levels on a double mutant. The predictions are significantly different from the experimental results for less than 30% of genes.