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.
@cite_29 provide an overview of graphical modeling software tools for representing or simulating reaction-based models, and detailing usability and perceptual issues that they can introduce. They find that creating graphical languages with many glyphs reduces ambiguity, but at the cost of introducing visual clutter and making the visual layout unappealing to users. They advocate for a minimalist approach that presents only the visual elements necessary for a particular analysis. Our tool also uses a smaller visual language for representing rules and clusters of rules.
@cite_29
1519642193 2132881639 1901568548 2011257065
While information visualization tools support the representation of abstract data, their ability to enhance one’s understanding of complex relationships can be hindered by a limited set of predefined charts. To enable novel visualization over multiple variables, we propose a declarative language for specifying informational graphics from first principles. The language maps properties of generic objects to graphical representations based on scaled interpretations of data values. An iterative approach to constraint solving that involves user advice enables the optimization of graphic layouts. The flexibility and expressiveness of a powerful but relatively easy to use grammar supports the expression of visualizations ranging from the simple to the complex. The goal of the research described in this paper is to develop an application-independent presentation tool that automatically designs effective graphical presentations (such as bar charts, scatter plots, and connected graphs) of relational information. Two problems are raised by this goal: The codification of graphic design criteria in a form that can be used by the presentation tool, and the generation of a wide variety of designs so that the presentation tool can accommodate a wide variety of information. The approach described in this paper is based on the view that graphical presentations are sentences of graphical languages. The graphic design issues are codified as expressiveness and effectiveness criteria for graphical languages. Expressiveness criteria determine whether a graphical language can express the desired information. Effectiveness criteria determine whether a graphical language exploits the capabilities of the output medium and the human visual system. A wide variety of designs can be systematically generated by using a composition algebra that composes a small set of primitive graphical languages. Artificial intelligence techniques are used to implement a prototype presentation tool called APT (A Presentation Tool), which is based on the composition algebra and the graphic design criteria. We describe a software model checking tool founded on game semantics, highlight the underpinning theoretical results and discuss several case studies. The tool is based on an interpretation algorithm defined compositionally on syntax and thus can also handle open programs. Moreover, the models it produces are equationally fully abstract. These features are essential in the modeling and verification of software components such as modules and turn out to lead to very compact models of programs. Modeling biological systems to understand their mechanistic behavior is an important activity in molecular systems biology. Mathematical modeling typically requires deep mathematical or computing knowledge, and this limits the spread of modeling tools among biologists. Graphical modeling languages have been introduced to minimize this limit. Here, we survey the main graphical formalisms (supported by software tools) available to model biological systems with a primary focus on their usability, within the framework of modeling reaction pathways with two-dimensional (2D) (possibly nested) compartments. Considering the main characteristics of the surveyed formalisms, we synthesise a new proposal (Style) and report the results of an online survey conducted among biologists to assess usability of available graphical formalisms. We consider this proposal a guideline developed from what we learned in the survey, which can inform development of graphical formalisms to model reaction pathways in 2D space.