Download Logic Synthesis for Genetic Diseases: Modeling Disease by Pey-Chang Kent Lin PDF

By Pey-Chang Kent Lin

This publication brings to undergo a physique of common sense synthesis strategies, with a view to give a contribution to the research and regulate of Boolean Networks (BN) for modeling genetic ailments corresponding to melanoma. The authors offer a number of VLSI good judgment thoughts to version the genetic affliction habit as a BN, with robust implicit enumeration strategies. insurance additionally comprises thoughts from VLSI checking out to regulate a defective BN, remodeling its habit to a fit BN, possibly supporting in efforts to discover the easiest applicants for therapy of genetic illnesses.

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Additional resources for Logic Synthesis for Genetic Diseases: Modeling Disease Behavior Using Boolean Networks

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However, there may exist many possible predictors for any gene, based on the attractor cycle data. Furthermore, only certain combinations of predictors may form a valid predictor set, due to biological constraints. The issue addressed in this chapter is how to efficiently and deterministically select the predictors that form the predictor set. We have implemented a Boolean satisfiability (SAT) based algorithm for the inference of gene predictor sets. Satisfiability is a decision problem of determining whether the variables in a Boolean formula (expressed in Conjunctive Normal Form or CNF) can be assigned to make the formula evaluate to true.

In this chapter, we implement a SAT-based algorithm to determine the gene predictor set from steady state gene expression data (attractor states). Using the attractor states as input, the states are ordered into attractor cycles. For each attractor cycle ordering, all possible predictors are enumerated and a conjunctive normal form (CNF) expression is generated which encodes these predictors and their biological constraints. Each CNF is solved using a SAT solver to find candidate predictor sets.

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