Name
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 chemical species and, subsequently, any new species that are found as products of reactions to generate a list of reactions and a list of the chemical species that participate in these reactions, i.e., a reaction network, which can be translated into a mathematical model. (c) 2005 Wiley Periodicals, Inc. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1002/cplx.20074 | COMPLEXITY |
Keywords | Field | DocType |
local rules, automatic model generation, networks, signal transduction, combinatorial complexity, systems biology | Rule-based modeling,Computer science,Systems biology,Combinatorial complexity,Computational model,Software,Artificial intelligence,User interface,Machine learning | Journal |
Volume | Issue | ISSN |
10 | 4 | 1076-2787 |
Citations | PageRank | References |
31 | 2.23 | 19 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| James R Faeder | 1 | 409 | 31.02 |
| Michael L. Blinov | 2 | 193 | 18.13 |
| Byron Goldstein | 3 | 119 | 8.89 |
| William S. Hlavacek | 4 | 277 | 24.15 |