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  • A biological system as considered in systems biology is understood in the form of a network of interactions among individual biochemical species. Complexity of these networks is inherently enormous, even for simple (e.g., procaryotic) organisms. When modeling and analyzing dynamics of these networks, i.e., exploring how the species evolve in time, we have to fight even another level of complexity - the enormous state space. In this paper we deal with a class of biological models that can be described in terms of multi-affine dynamic systems. First, we present a prototype tool for parallel (distributed) analysis of multi-affine systems discretized into rectangles that adapts the approach of Belta et.al. Secondly, we propose heuristics that significantly increase applicability of the approach to large biological models. Effects of different settings of the heuristics is firstly compared on a set of experiments performed on small models. Subsequently, experiments on large models are provided as well.
  • A biological system as considered in systems biology is understood in the form of a network of interactions among individual biochemical species. Complexity of these networks is inherently enormous, even for simple (e.g., procaryotic) organisms. When modeling and analyzing dynamics of these networks, i.e., exploring how the species evolve in time, we have to fight even another level of complexity - the enormous state space. In this paper we deal with a class of biological models that can be described in terms of multi-affine dynamic systems. First, we present a prototype tool for parallel (distributed) analysis of multi-affine systems discretized into rectangles that adapts the approach of Belta et.al. Secondly, we propose heuristics that significantly increase applicability of the approach to large biological models. Effects of different settings of the heuristics is firstly compared on a set of experiments performed on small models. Subsequently, experiments on large models are provided as well. (en)
Title
  • Computational Analysis of Large-Scale Multi-Affine ODE Models
  • Computational Analysis of Large-Scale Multi-Affine ODE Models (en)
skos:prefLabel
  • Computational Analysis of Large-Scale Multi-Affine ODE Models
  • Computational Analysis of Large-Scale Multi-Affine ODE Models (en)
skos:notation
  • RIV/00216224:14330/09:00028617!RIV11-GA0-14330___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(1ET408050503), P(1M0545), P(GP201/09/P497), S, Z(MSM0021622419)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 307935
http://linked.open...ai/riv/idVysledku
  • RIV/00216224:14330/09:00028617
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • biological networks; parallel model checking; dynamics systems; rectangular abstraction (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [F527E9EE512F]
http://linked.open...v/mistoKonaniAkce
  • Trento
http://linked.open...i/riv/mistoVydani
  • Los Alamitos (California)
http://linked.open...i/riv/nazevZdroje
  • International Workshop on High Performance Computational Systems Biology
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Barnat, Jiří
  • Brim, Luboš
  • Fabriková, Jana
  • Černá, Ivana
  • Šafránek, David
  • Dražan, Sven
http://linked.open...vavai/riv/typAkce
http://linked.open...ain/vavai/riv/wos
  • 000275038300011
http://linked.open.../riv/zahajeniAkce
http://linked.open...n/vavai/riv/zamer
number of pages
http://purl.org/ne...btex#hasPublisher
  • IEEE Computer Society
https://schema.org/isbn
  • 978-0-7695-3809-9
http://localhost/t...ganizacniJednotka
  • 14330
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