Conditioning-Based Modeling of Contextual Genomic Regulation

Abstract

A more complete understanding of the alterations in cellular regulatory and control mechanisms that occur in the various forms of cancer has been one of the central targets of the genomic and proteomic methods that allow surveys of the abundance and/or state of cellular macromolecules. This preference is driven both by the intractability of cancer to generic therapies, assumed to be due to the highly varied molecular etiologies observed in cancer, and by the opportunity to discern and dissect the regulatory and control interactions presented by the highly diverse assortment of perturbations of regulation and control that arise in cancer. Exploiting the opportunities for inference on the regulatory and control connections offered by these revealing system perturbations is fraught with the practical problems that arise from the way biological systems operate. Two classes of regulatory action in biological systems are particularly inimical to inference, convergent regulation, where a variety of regulatory actions result in a common set of control responses (crosstalk), and divergent regulation, where a single regulatory action produces entirely different sets of control responses, depending on cellular context (conditioning). We have constructed a coarse mathematical model of the propagation of regulatory influence in such distributed, context-sensitive regulatory networks that allows a quantitative estimation of the amount of crosstalk and conditioning associated with a candidate regulatory gene taken from a set of genes that have been profiled over a series of samples where the candidate's activity varies.

Gene Networks Inference through One Genetic Algorithm Per Gene

“…The functions with very small probabilities can simulate perturbations (external stimuli) or changes between biological contexts [8], [11].…”

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“…One of them is the coefficient of non-linear determination (CoD), which considers the error committed by a subset in classifying the target value (Bayesian error) [9,10,11]. The CoD of the target Y given the knowledge of X = (X 1 , ..., X n ) is given by:…”

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“…In such a PBN, the governing BN remains fixed for an interval of time until the occurrence of some (random) event, perhaps corresponding to an external stimulus. Taking the perspective that a switch corresponds to a change in context for the cell, these more general PBNs are referred to as context-sensitive probabilistic Boolean networks (cPBNs) [4,5]. A subsequent change to the cPBN structure is to replace its constituent BNs with BNs with perturbation.…”