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Application of logic synthesis to the understanding and cure of genetic diseases

Published: 03 June 2012 Publication History

Abstract

In the quest to understand and cure genetic diseases such as cancer, the fundamental approach being taken is undergoing a gradual change. It is becoming more acceptable to view these diseases as an engineering problem, and systems engineering approaches are becoming more accepted as a means to tackle genetic diseases. In this light, we believe that logic synthesis techniques can play a very important role. Several techniques from the field of logic synthesis can be adapted to assist in the arguably huge effort of modeling and controlling such diseases. The set of genes that control a particular genetic disease can be modeled as a Finite State Machine (FSM) called the Gene Regulatory Network (GRN). Important problems include (i) inferring the GRN from observed gene expression data from patients and (ii) assuming that such a GRN exists, determining the "best" set of drugs so that the disease is "maximally" cured. In this paper, we report initial results on the application of logic synthesis techniques that we have developed to address both these problems. In the first technique, we present Boolean Satisfiability (SAT) based approaches to infer the logical support of each gene that regulates melanoma, using gene expression data from patients of the disease. From the output of such a tool, biologists can construct targeted experiments to understand the logic functions that regulate a particular gene. The second technique assumes that the GRN is known, and uses a weighted partial Max-SAT formulation to find the set of drugs with the least side-effects, that steer the GRN state towards one that is closest to that of a healthy individual, in the context of colon cancer. Our group is currently exploring the application of several other logic techniques to a variety of related problems in this domain.

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        cover image ACM Conferences
        DAC '12: Proceedings of the 49th Annual Design Automation Conference
        June 2012
        1357 pages
        ISBN:9781450311991
        DOI:10.1145/2228360
        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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        Published: 03 June 2012

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        Author Tags

        1. gene regulation
        2. genomics
        3. logic

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        DAC '12: The 49th Annual Design Automation Conference 2012
        June 3 - 7, 2012
        California, San Francisco

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        • (2014)Data compression via logic synthesis2014 19th Asia and South Pacific Design Automation Conference (ASP-DAC)10.1109/ASPDAC.2014.6742961(628-633)Online publication date: Jan-2014
        • (2013)Determining Gene Function in Boolean Networks using SATLogic Synthesis for Genetic Diseases10.1007/978-1-4614-9429-4_3(39-51)Online publication date: 1-Nov-2013
        • (2013)Predictor Set Inference using SATLogic Synthesis for Genetic Diseases10.1007/978-1-4614-9429-4_2(25-38)Online publication date: 1-Nov-2013
        • (2013)IntroductionLogic Synthesis for Genetic Diseases10.1007/978-1-4614-9429-4_1(1-21)Online publication date: 1-Nov-2013
        • (2012)Determining gene function in boolean networks using boolean satisfiabilityProceedings 2012 IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS)10.1109/GENSIPS.2012.6507757(176-179)Online publication date: Dec-2012

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