An Algorithm for Subgraph Isomorphism
Journal of the ACM (JACM)
Efficient Graph Rewriting and Its Implementation
Efficient Graph Rewriting and Its Implementation
Theoretical Computer Science - Special issue: Computational systems biology
Rule-based modeling of biochemical networks: Research Articles
Complexity - Understanding Complex Systems: Part II
Theoretical Computer Science
A Language for Biochemical Systems
CMSB '08 Proceedings of the 6th International Conference on Computational Methods in Systems Biology
CMSB '08 Proceedings of the 6th International Conference on Computational Methods in Systems Biology
Scalable simulation of cellular signaling networks
APLAS'07 Proceedings of the 5th Asian conference on Programming languages and systems
Statistical model checking: an overview
RV'10 Proceedings of the First international conference on Runtime verification
A Syntactic Abstraction for Rule-Based Languages with Binding
Electronic Notes in Theoretical Computer Science (ENTCS)
Graph theory for rule-based modeling of biochemical networks
Transactions on Computational Systems Biology VII
A language for biochemical systems: design and formal specification
Transactions on Computational Systems Biology XII
Simulation of kohn's molecular interaction maps through translation into stochastic CLS+
PSI'09 Proceedings of the 7th international Andrei Ershov Memorial conference on Perspectives of Systems Informatics
Rule-based modelling of cellular signalling
CONCUR'07 Proceedings of the 18th international conference on Concurrency Theory
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The process by which a cell senses and responds to its environment, as in signal transduction, is often mediated by a network of protein-protein interactions, in which proteins combine to form complexes and undergo post-translational modifications, which regulate their enzymatic and binding activities. A typical signaling protein contains multiple sites of protein interaction and modification and may contain catalytic domains. As a result, interactions of signaling proteins have the potential to generate a combinatorially large number of complexes and modified states, and representing signal-transduction networks can be challenging. Representation, in the form of a diagram or model, usually involves a tradeoff between comprehensibility and precision: comprehensible representations tend to be ambiguous or incomplete, whereas precise representations, such as a long list of chemical species and reactions in a network, tend to be incomprehensible. Here, we develop conventions for representing signal-transduction networks that are both comprehensible and precise. Labeled nodes represent components of proteins and their states, and edges represent bonds between components. Binding and enzymatic reactions are described by reaction rules, in which left graphs define the properties of reactants and right graphs define the products that result from transformations of reactants. The reaction rules can be evaluated to derive a mathematical model.