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

By Pey-Chang Kent Lin

ISBN-10: 1461494281

ISBN-13: 9781461494287

ISBN-10: 146149429X

ISBN-13: 9781461494294

This ebook brings to endure a physique of good judgment synthesis innovations, with a purpose to give a contribution to the research and keep an eye on of Boolean Networks (BN) for modeling genetic ailments akin to melanoma. The authors supply numerous VLSI common sense suggestions to version the genetic disorder habit as a BN, with strong implicit enumeration concepts. assurance additionally contains ideas from VLSI checking out to manage a defective BN, remodeling its habit to a fit BN, in all probability helping in efforts to discover the easiest applicants for remedy of genetic diseases.

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Extra info for Logic Synthesis for Genetic Diseases: Modeling Disease Behavior Using Boolean Networks

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The last constraint (S3 ) requires that each gene must be used as a predictor for at least one other gene in the predictor set. A gene that is not used in any predictor does not perform any regulation function and could be removed from the GRN. S3 ensures that this does not occur. To ensure that gene gi is used in at least one predictor, we form clauses ci3 which include all predictors that use gene gi as input. To specify that gene gi must be used, we also include a single variable clause (xi ) to ci3 .

298(14), 1682– 1684 (2007) 5. Teutsch, M. : The evaluation of genomic applications in practice and prevention (EGAPP) initiative:methods of the EGAPP working group. Genet. Med. 11(1), 3–14 (2009) 6. Guelzim, N. : Topological and causal structure of the yeast transcriptional regulatory network. Nat. Genet. 31, 60–63 (2002) 7. : Coefficient of determination in nonlinear signal processing. Signal Process. 80(10), 2219–2235 (2000) 8. : Inferring connectivity of genetic regulatory networks using information-theoretic criteria.

In the context of genomics, SAT has been applied to the analysis of GRNs.

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Logic Synthesis for Genetic Diseases: Modeling Disease Behavior Using Boolean Networks by Pey-Chang Kent Lin


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