Rules for Modeling Signal-Transduction Systems

Abstract

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.

Lose and gain: impacts of ERK5 and JNK cascades on each other

“…If we assume that the total " x ¼x t ð Þ þ x t ð Þis constant for all time t, the following differential equation models a signaling step (Hlavacek et al 2006)…”

Formal Cell Biology in Biocham

“…We use the rule-based syntax of the biochemical abstract machine Biocham [9,10] which is similar to (and compatible with) the Systems Biology Markup Language (SBML) [2] nowadays supported by a majority of modeling tools [11,12]. Then we present the different semantics of Biocham models which correspond to different abstraction levels: namely the differential, stochastic, discrete and boolean semantics [13,14,15].…”

Rule-Based Modeling of Biochemical Systems with BioNetGen

“…Before beginning our discussion of BioNetGen, we will briefly recap features of signaltransduction systems that motivate a rule-based modeling approach and the general idea of rulebased modeling. For more thorough reviews of these topics, see (7,8).…”