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Modeling the dynamic behavior of biochemical regulatory networks.
Tyson, John J; Laomettachit, Teeraphan; Kraikivski, Pavel.
Afiliación
  • Tyson JJ; Department of Biological Sciences, Virginia Tech, 5088 Derring Hall, Blacksburg VA 24061, USA; Division of Systems Biology, Academy of Integrated Science, Virginia Tech, Blacksburg VA 24061, USA. Electronic address: tyson@vt.edu.
  • Laomettachit T; Bioinformatics and Systems Biology Program, King Mongkut's University of Technology Thonburi, Bang Khun Thian, Bangkok 10150, Thailand.
  • Kraikivski P; Department of Biological Sciences, Virginia Tech, 5088 Derring Hall, Blacksburg VA 24061, USA; Division of Systems Biology, Academy of Integrated Science, Virginia Tech, Blacksburg VA 24061, USA.
J Theor Biol ; 462: 514-527, 2019 02 07.
Article en En | MEDLINE | ID: mdl-30502409
Strategies for modeling the complex dynamical behavior of gene/protein regulatory networks have evolved over the last 50 years as both the knowledge of these molecular control systems and the power of computing resources have increased. Here, we review a number of common modeling approaches, including Boolean (logical) models, systems of piecewise-linear or fully non-linear ordinary differential equations, and stochastic models (including hybrid deterministic/stochastic approaches). We discuss the pro's and con's of each approach, to help novice modelers choose a modeling strategy suitable to their problem, based on the type and bounty of available experimental information. We illustrate different modeling strategies in terms of some abstract network motifs, and in the specific context of cell cycle regulation.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Modelos Biológicos Límite: Animals / Humans Idioma: En Revista: J Theor Biol Año: 2019 Tipo del documento: Article Pais de publicación: Reino Unido

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Modelos Biológicos Límite: Animals / Humans Idioma: En Revista: J Theor Biol Año: 2019 Tipo del documento: Article Pais de publicación: Reino Unido