Exam Parameter estimation for Boolean models of biological networks - arXiv:0908.3037v1[q-bio.MN 21 Aug 2009 Parameter estimation for Boolean models of

Exam Parameter estimation for Boolean models of biological networks

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Unformatted text preview: arXiv:0908.3037v1 [q-bio.MN] 21 Aug 2009 Parameter estimation for Boolean models of biological networks Elena Dimitrova a , Luis David Garc´ ıa-Puente b,h , Franziska Hinkelmann c,d , Abdul S. Jarrah c,d , Reinhard Laubenbacher * ,c,d,h , Brandilyn Stigler e,h , Michael Stillman g , Paola Vera-Licona f a Department of Mathematical Sciences, Clemson University, Clemson, SC 29634-0975, USA b Department of Mathematics and Statistics, Sam Houston State University, Huntsville, TX 77341-2206, USA c Department of Mathematics, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061-0123, USA d Virginia Bioinformatics Institute, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061-0477, USA e Mathematics Department, Southern Methodist University, Dallas, TX 75275-0156, USA f DIMACS Center, Rutgers University, Piscataway, NJ 08854-8018, USA g Mathematics Department, Cornell University, Ithaca, NY 14853-4201, USA h Statistical and Applied Mathematical Sciences Institute, Research Triangle Park, NC 27709-4006, USA Abstract Boolean networks have long been used as models of molecular networks and play an in- creasingly important role in systems biology. This paper describes a software package, Polynome , offered as a web service, that helps users construct Boolean network models based on experimental data and biological input. The key feature is a discrete analog of parameter estimation for continuous models. With only experimental data as input, the software can be used as a tool for reverse-engineering of Boolean network models from experimental time course data. Key words: 2000 MSC: Primary 92-08, 92B05; Secondary 13P10 1. Introduction During the last decade finite dynamical systems, that is, discrete dynamical systems with a finite phase space, have been used increasingly in systems biology to model a va- riety of biochemical networks, such as metabolic, gene regulatory, and signal transduction networks. In many cases, the available data quantity and quality is not sufficient to build * Corresponding author Email addresses: [email protected] (Elena Dimitrova), [email protected] (Luis David Garc´ ıa-Puente), [email protected] (Franziska Hinkelmann), [email protected] (Abdul S. Jarrah), [email protected] (Reinhard Laubenbacher), [email protected] (Brandilyn Stigler), [email protected] (Michael Stillman), [email protected] (Paola Vera-Licona) 1 Partially supported by SAMSI New Researcher Fellowship. Preprint submitted to Theoretical Computer Science August 21, 2009 detailed quantitative models such as systems of ordinary differential equations, which re- quire many parameters that are frequently unknown. In addition, discrete models tend to be more intuitive and more easily accessible to life scientists. Boolean networks and the more general so-called logical models are the main types of finite dynamical systems that have been used successfully in modeling biological networks....
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  • Fall '14
  • LuisDavidGarcia-Puente
  • Logic, Statistics, Boolean function, Gene regulatory network, Polynôme

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