7 research outputs found
Dynamic Influence Networks for Rule-based Models
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.Comment: Accepted to TVCG, in pres
Animated Visualization of Causal Relations Through Growing 2D Geometry
Causality visualization is an important tool for many scientific domains that involve complex interactions between multiple entities (examples include parallel and distributed systems in computer science). However, traditional visualization techniques such as Hasse diagrams are not well-suited to large system executions, and users often have difficulties answering even basic questions using them, or have to spend inordinate amounts of time to do so. In this paper we present the Growing Squares and Growing Polygons methods, two sibling visualization techniques that were designed to solve this problem by providing efficient 2D causality visualization through the use of color, texture, and animation. Both techniques have abandoned the traditional linear timeline and instead map the time parameter to the size of geometrical primitives representing the processes; in the Growing Squares case, each process is a color-coded square that receives color influences from other process squares as messages reach it; in the Growing Polygons case, each process is instead an -sided polygon consisting of triangular sectors showing color-coded influences from the other processes. We have performed user studies of both techniques, comparing them with Hasse diagrams, and they have been shown to be significantly more efficient than old techniques, both in terms of objective performance as well as the subjective opinion of the test subjects (the Growing Squares technique is, however, only significantly more efficient for small systems)
Animated Visualization of Causal Relations Through Growing 2D Geometry ∗ Abstract
Causality visualization is an important tool for many scientific domains that involve complex interactions between multiple entities (examples include parallel and distributed systems in computer science). However, traditional visualization techniques such as Hasse diagrams are not well-suited to large system executions, and users often have difficulties answering even basic questions using them, or have to spend inordinate amounts of time to do so. In this paper we present the Growing Squares and Growing Polygons methods, two sibling visualization techniques that were designed to solve this problem by providing efficient 2D causality visualization through the use of color, texture, and animation. Both techniques have abandoned the traditional linear timeline and instead map the time parameter to the size of geometrical primitives representing the processes; in the Growing Squares case, each process is a color-coded square that receives color influences from other process squares as messages reach it; in the Growing Polygons case, each process is instead an n-sided polygon consisting of triangular sectors showing color-coded influences from the other processes. We have performed user studies of both techniques, comparing them with Hasse diagrams, and they have been shown to be significantly more efficient than old techniques, both in terms of objective performance as well as the subjective opinion of the test subjects (the Growing Squares technique is, however, only significantly more efficient for small systems)