Rule‐based modeling of biochemical networks

Abstract

We present a method for generating a biochemical reaction network from a description of the interactions of components of biomolecules. The interactions are specified in the form of reaction rules, each of which defines a class of reaction associated with a type of interaction. Reactants within a class have shared properties, which are specified in the rule defining the class. A rule also provides a rate law, which governs each reaction in a class, and a template for transforming reactants into products. A set of reaction rules can be applied to a seed set of

Stochastic effects and bistability in T cell receptor signaling

“…We also encoded the model in the BioNetGen™ language (Faeder et al, 2005a;Blinov et al, 2006), which enables automatic building (and solution) of ODEs based on specified reaction rules that serve as generators of chemical reactions. This formal model specification can be used for future work as the BioNetGen™ software allows for simulations of systems of thousands of reactions (Blinov et al, 2004;Faeder et al, 2005b). We found that the stochastic simulations performed using BioNetGen™ are more than 10 times faster than in MATLAB.…”

Constraint-Based Simulation of Biological Systems Described by Molecular Interaction Maps

“…Constraints are interpreted first- 6 The combinatorial interpretation considers these reactions as happening uniformly, i.e. with the same rate whatever the context.…”

“…A big problem with this approach is the cost of the notation: in some cases, the effort required for a complete specification can be overwhelming; this is true especially for bio-regulatory networks, due to the central role played by multimolecular complexes and protein modifications [10,11,6]. In fact, even a small set of proteins can potentially generate a big number of different complexes, of which only a few different kinds may be present at a certain time.…”

“…Moreover, the combinatorial interpretation represents implicitly a large number of molecules: a single complexation arrow usually handles, on both sides, a big set of reactants. Consider, for example, the arrow connecting A to B in Figure 1(a); in the combinatorial interpretation it represents four reactions, namely the complexation of A with B, pB, B : C and pB : C. 6 In principle, it is always possible to create an explicit MIM with the same behaviors of a combinatorial MIM, introducing more reaction arrows and contingencies. 7 Explicit MIMs can be easily translated into a set of ODEs or into an explicit stochastic model for computer simulation, see [10].…”

Reachability Analysis of Biological Signalling Pathways by Abstract Interpretation

“…We model these networks in structured concurrent languages, such as κ [2,3,4], or the closely related BioNetGen language [5,6]. Models being based on rules, not flat reactions, they incorporate bona fide biological knowledge, and are easier to discuss and update.…”