Constructing and visualizing chemical reaction networks from pi-calculus models

Abstract

Abstract. The π-calculus, in particular its stochastic version the stochastic π-calculus, is a common modeling formalism to concisely describe the chemical reactions occurring in biochemical systems. However, it remains largely unexplored how to transform a biochemical model expressed in the stochastic π-calculus back into a set of meaningful reactions. To this end, we present a two step approach of first translating model states to reaction sets and then visualizing sequences of reaction sets, which are obtained from state trajectories, in terms of reaction networks. Our translation from model states to reaction sets is formally defined and shown to be correct, in the sense that it reflects the states and transitions as they are derived from the continuous time Markov chain-semantics of the stochastic π-calculus. Our visualization concept combines high level measures of network complexity with interactive, table-based network visualizations. It directly reflects the structures introduced in the first step and allows modelers to explore the resulting simulation traces by providing both: an overview of a network's evolution and a detail inspection on demand.

The State of the Art in Visualizing Dynamic Multivariate Networks

“…Edges in each time step are encoded between consecutive pairs of parallel axes. John et al [JSS * 13] also employed list views for chemical reaction networks. The reactions are shown on a timeline and user‐selected time steps are represented as bipartite networks using list views.…”

A Taxonomy and Survey of Dynamic Graph Visualization

“…Others : There are various further applications of dynamic graphs and their visualization. In research, for instance, in context of biology, evolving metabolic pathways [RUK*10], simulated chemical reaction networks [JSS*12] and uncertainties therein [VHK*13], or protein interaction networks [BFL12] are studied. Psychology and user interface research may profit from depicting eye gaze data as dynamic graphs recorded in eye‐tracking studies [BBR*14, HEF*13].…”