Computer-Aided Drug Discovery for Pathway and Genetic Diseases
Anil Aswani, Claire Tomlin

Citation
Anil Aswani, Claire Tomlin. "Computer-Aided Drug Discovery for Pathway and Genetic Diseases". Unpublished article, 2010.

Abstract
Selecting drug targets in pathway and genetic diseases (e.g., cancer) is a difficult problem facing the medical field and pharmaceutical industry. Because of the complex interconnections and feedback found in biological pathways, it is difficult to understand the potential effects of targeting certain portions of the network. The pharmaceutical industry has avoided novel targets for drugs, largely because of the increased risk in developing such treatments. This necessitates the need for systems biology methods which can help mitigate some of the risks of identifying novel targets and also suggest further experiments to validate them. The primary goal of this paper is to introduce a mathematical framework for solving such problems, that is amenable to computational or mathematical study. The secondary goal is to suggest methods for solving problems posed in this framework. One of these methods is a heuristic which is designed to allow its computations to scale up to much bigger examples and pathways than presented here.

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Citation formats

• HTML

  Anil Aswani, Claire Tomlin. <a
  href="http://chess.eecs.berkeley.edu/pubs/784.html"
  ><i>Computer-Aided Drug Discovery for Pathway and
  Genetic Diseases</i></a>, Unpublished article, 
  2010.

• Plain text

  Anil Aswani, Claire Tomlin. "Computer-Aided Drug
  Discovery for Pathway and Genetic Diseases".
  Unpublished article,  2010.

• BibTeX

  @unpublished{AswaniTomlin10_ComputerAidedDrugDiscoveryForPathwayGeneticDiseases,
      author = {Anil Aswani and Claire Tomlin},
      title = {Computer-Aided Drug Discovery for Pathway and
                Genetic Diseases},
      year = {2010},
      abstract = {Selecting drug targets in pathway and genetic
                diseases (e.g., cancer) is a difficult problem
                facing the medical field and pharmaceutical
                industry. Because of the complex interconnections
                and feedback found in biological pathways, it is
                difficult to understand the potential effects of
                targeting certain portions of the network. The
                pharmaceutical industry has avoided novel targets
                for drugs, largely because of the increased risk
                in developing such treatments. This necessitates
                the need for systems biology methods which can
                help mitigate some of the risks of identifying
                novel targets and also suggest further experiments
                to validate them. The primary goal of this paper
                is to introduce a mathematical framework for
                solving such problems, that is amenable to
                computational or mathematical study. The secondary
                goal is to suggest methods for solving problems
                posed in this framework. One of these methods is a
                heuristic which is designed to allow its
                computations to scale up to much bigger examples
                and pathways than presented here.},
      URL = {http://chess.eecs.berkeley.edu/pubs/784.html}
  }
  

Posted by Christopher Brooks on 24 Nov 2010.
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