Rule-based modeling

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Rule-based modeling is a modeling approach that uses a set of rules that indirectly specifies a mathematical model. The rule-set can either be translated into a model such as Markov chains or differential equations, or be treated using tools that directly work on the rule-set in place of a translated model, as the latter is typically much bigger. Rule-based modeling is especially effective in cases where the rule-set is significantly simpler than the model it implies, meaning that the model is a repeated manifestation of a limited number of patterns. An important domain where this is often the case is biochemical models of living organisms. Groups of mutually corresponding substances are subject to mutually corresponding interactions.

For biochemical systems[edit]

Early efforts to use rule-based modeling in simulation of biochemical systems include the stochastic simulation systems StochSim[1]

A widely used tool for rule-based modeling of biochemical networks is BioNetGen [2] It is released under the GNU GPL, version 3. BioNetGen includes a language to describe chemical substances, including the states they can assume and the bindings they can undergo. These rules can be used to create a reaction network model or to perform computer simulations directly on the rule set. The biochemical modeling framework Virtual Cell includes a BioNetGen interpreter.

A close alternative is the Kappa language.[3]

References[edit]

  1. ^ Morton-Firth CJ, Bray D (1998) Predicting temporal fluctuations in an intracellular signalling pathway. J Theor Biol. 1998 192(1):117-28.
  2. ^ BioNetGen
  3. ^ Kappa