We introduce the Dynamic Influence Network (DIN), a novel visual analytics technique for representing and analyzing rule-based models of protein-protein interaction networks. Rule-based modeling has proved instrumental in developing biological models that are concise, comprehensible, easily extensible, and that mitigate the combinatorial complexity of multi-state and multi-component biological molecules. Our technique visualizes the dynamics of these rules as they evolve over time. Using the data produced by KaSim, an open source stochastic simulator of rule-based models written in the Kappa language, DINs provide a node-link diagram that represents the influence that each rule has on the other rules. That is, rather than representing individual biological components or types, we instead represent the rules about them (as nodes) and the current influence of these rules (as links). Using our interactive DIN-Viz software tool, researchers are able to query this dynamic network to find meaningful patterns about biological processes, and to identify salient aspects of complex rule-based models. To evaluate the effectiveness of our approach, we investigate a simulation of a circadian clock model that illustrates the oscillatory behavior of the KaiC protein phosphorylation cycle.
's @cite_49 provides a framework for editing and exploring rule-based systems of intracellular biochemistry, such as . In this framework, the primary visualization provides an interactive contact map in which molecules are rendered as large gray nodes and domain states are positioned inside these nodes. Rules are represented as links between specific sites within the nodes, and nested isocontours are used to define a compartment hierarchy of elements related to particular components of the cell. A secondary visualization shows the relations between the reaction rules that describe the behavior of a system. That is, similar to our approach, the influence graph shows if a rule activates or inhibits another rule. However, our approach further emphasizes how these influences can change over time, facilitating the analysis of the dynamics of the system.
@cite_49
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Background Rule-based modeling (RBM) is a powerful and increasingly popular approach to modeling cell signaling networks. However, novel visual tools are needed in order to make RBM accessible to a broad range of users, to make specification of models less error prone, and to improve workflows.