Abstract
Frameworks such as BioNetGen, Kappa and Simmune use “reaction rules” to specify biochemical interactions compactly, where each rule specifies a mechanism such as binding or phosphorylation and its structural requirements. Current rule-based models of signaling pathways have tens to hundreds of rules, and these numbers are expected to increase as more molecule types and pathways are added. Visual representations are critical for conveying rule-based models, but current approaches to show rules and interactions between rules scale poorly with model size. Also, inferring design motifs that emerge from biochemical interactions is an open problem, so current approaches to visualize model architecture rely on manual interpretation of the model. Here, we present three new visualization tools that constitute an automated visualization framework for rule-based models: (i) a compact rule visualization that efficiently displays each rule, (ii) the atom-rule graph that conveys regulatory interactions in the model as a bipartite network, and (iii) a tunable compression pipeline that incorporates expert knowledge and produces compact diagrams of model architecture when applied to the atom-rule graph. The compressed graphs convey network motifs and architectural features useful for understanding both small and large rule-based models, as we show by application to specific examples. Our tools also produce more readable diagrams than current approaches, as we show by comparing visualizations of 27 published models using standard graph metrics. We provide an implementation in the open source and freely available BioNetGen framework, but the underlying methods are general and can be applied to rule-based models from the Kappa and Simmune frameworks also. We expect that these tools will promote communication and analysis of rule-based models and their eventual integration into comprehensive whole-cell models.
Highlights
Rule-based frameworks such as BioNetGen [1,2,3], Kappa [4,5,6] and Simmune [7,8] have been used to build detailed kinetic models of signaling pathways (e.g., FcεRI [9,10,11], TCR [12], EGFR [13,14], and p53 [15])
Signaling in living cells is mediated through a complex network of chemical interactions
To build compact pathway diagrams that convey function, we provide a pipeline for reducing the complexity of the model AR graph (Fig 4A) while preserving relevant regulatory features
Summary
Rule-based frameworks such as BioNetGen [1,2,3], Kappa [4,5,6] and Simmune [7,8] have been used to build detailed kinetic models of signaling pathways (e.g., FcεRI [9,10,11], TCR [12], EGFR [13,14], and p53 [15]). Whether rule-based or otherwise, are difficult to understand or communicate without good visualization methods. The size of rule-based model that can be simulated far exceeds the size of model for which useful visualizations can be constructed automatically. Other than using manual approaches, we do not have an effective approach to build compact pathway diagrams to communicate the model. Solving the automated diagramming problem is necessary to make the leap from opaque machine-readable model descriptions that can only be understood through manual annotation to transparent models that can be understood and explored by the wider community
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