Dynamic Influence Networks for Rule-Based Models

Abstract

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.

The Kappa platform for rule-based modeling

“…7a ). A detailed account of the animation and its interpretation is provided elsewhere ( Forbes et al , 2018 ). The point to note here is the drastic difference in the influence structure of the system between the two time points.…”

NeuroCave: A web-based immersive visualization platform for exploring connectome datasets

“…Future work will give users the option to make use of popular community detection algorithms, such as Louvain (Blondel, Guillaume, Lambiotte, & Lefebvre, 2011 ) and Infomap (Rosvall & Bergstrom, 2008 ), which were found to perform well across a range of benchmarks (Lancichinetti & Fortunato, 2009 ). We also will adapt our system to support temporally varying dynamic datasets (Crossley et al, 2016 ; Forbes et al, 2018 ; Ma, Forbes, Llano, Berger-Wolf, & Kenyon, 2016 ; Purgato, Santambrogio, Berger-Wolf, & Forbes, 2017 ), and investigate how NeuroCave can facilitate comparisons between structural and functional connectomes in order to reveal the complex mappings between them (Bullmore & Sporns, 2009 ; C. Honey et al, 2009 ; C. J. Honey, Kötter, Breakspear, & Sporns, 2007 ). Another future goal is to quantitatively assess the impact of VR mode on analysis tasks and empirically investigate the current NeuroCave workflow, which encourages moving between VR and desktop modes.…”

RuleVis: Constructing Patterns and Rules for Rule-Based Models

“…Just as molecules undergo chemical reactions that cause their bonds to change, rearranging the atoms, a Kappa rule defines how a pattern can change its internal state or be transformed in the presence of other patterns [3]. Given appropriate sets of rules, the Kappa graph rewriting engine is able to generate coherent oscillatory behaviors that emulate complex biological behaviors [16]. More explicitly, a pattern is a site graph where multiple types of the same agent are allowed to occur, but each site can have at most only one edge [10,11].…”