In recent years, there has been a considerable amount of interest in the area of Genomic Signal Processing, which is the engineering discipline that studies the processing of genomic signals. Since regulatory decisions within the cell utilize numerous inputs, analytical tools are necessary to model the multivariate influences on decision-making produced by complex genetic networks. Signal processing approaches such as detection, prediction and classification have been used in the recent past to construct genetic regulatory networks capable of modeling genetic behavior. To accommodate the large amount of uncertainty associated with this kind of modeling, many of the networks proposed are probabilistic. One of the objectives of network modeling is to use the network to design different intervention approaches for affecting the time evolution of the gene activity profile of the network. More specifically, one is interested in intervening to help the network avoid undesirable states such as those associated with a disease. This paper provides a tutorial survey of the intervention approaches developed so far in the literature for probabilistic gene networks (probabilistic Boolean networks) and outlines some of the open challenges that remain.
Keywords: Gene regulatory network, markov chain, steady-state distribution, optimal control, dynamic programming, context sensitive networks
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