A Correction to the Review Titled "Rules for Modeling Signal-Transduction Systems" by W. S. Hlavacek et al.

Formalized rules for protein-protein interactions have recently been introduced to represent the binding and enzymatic activities of proteins in cellular signaling. Rules encode an understanding of how a system works in terms of the biomolecules in the system and their possible states and interactions. A set of rules can be as easy to read as a diagrammatic interaction map, but unlike most such maps, rules have precise interpretations. Rules can be processed to automatically generate a mathematical or computational model for a system, which enables explanatory and predictive insights into the system’s behavior. Rules are independent units of a model specification that facilitate model revision. Instead of changing a large number of equations or lines of code, as may be required in the case of a conventional mathematical model, a protein interaction can be introduced or modified simply by adding or changing a single rule that represents the interaction of interest. Rules can be defined and visualized by using graphs, so no specialized training in mathematics or computer science is necessary to create models or to take advantage of the representational precision of rules. Rules can be encoded in a machine-readable format to enable electronic storage and exchange of models, as well as basic knowledge about protein-protein interactions. Here, we review the motivation for rule-based modeling; applications of the approach; and issues that arise in model specification, simulation, and testing. We also discuss rule visualization and exchange and the software available for rule-based modeling. Signaling molecules that control cellular regulation operate in complex networks of molecular interactions within the cell. Many of the individual proteins undergo multiple posttranslational modifications and can thus exist in numerous biochemically distinct states. We explore how mathematical models can cope with such complexity when intuition is insufficient to understand a regulatory scheme. We review approaches to creation of mathematical models of signaling systems with strategies that keep the models from being unwieldy but still allow them to accurately reflect biological systems. We discuss the translation of information about such signaling pathways into a computer-readable language that could allow interoperability of various models. The review has 10 figures and 155 citations and contains Web links to Web sites relevant to the various modeling efforts discussed.