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

InfoVis experience enhancement through mediated interaction

Information visualization is an experience in which both the aesthetic representations and interaction are part. Such an experience can be augmented through close consideration of its major components. Interaction is crucial to the experience, yet it has seldom been adequately explored in the field. We claim that direct mediated interaction can augment such an experience. This paper discusses the reasons behind such a claim and proposes a mediated interactive manipulation scheme based on the notion of directness. It also describes the ways in which such a claim will be validated. The Literature Knowledge Domain (LKD) is used as the concrete domain around which the discussions will be held

Visual Causality: Investigating Graph Layouts for Understanding Causal Processes

Causal diagrams provide a graphical formalism indicating how statistical models can be used to study causal processes. Despite the extensive research on the efficacy of aesthetic graphic layouts, the causal inference domain has not benefited from the results of this research. In this paper, we investigate the performance of graph visualisations for supporting users’ understanding of causal graphs. Two studies were conducted to compare graph visualisations for understanding causation and identifying confounding variables in a causal graph. The first study results suggest that while adjacency matrix layouts are better for understanding direct causation, node-link diagrams are better for understanding mediated causation along causal paths. The second study revealed that node-link layouts, and in particular layouts created by a radial algorithm, are more effective for identifying confounder and collider variables

Cranial morphometrics of the dire wolf, Canis dirus, at Rancho La Brea: temporal variability and its links to nutrient stress and climate

The tar pits of Rancho La Brea are a unique window onto the biology and ecology of the terminal Pleistocene in southern California. In this study we capitalize on recent advances in understanding of La Brea tar pit chronology to perform the first morphometric study of crania of the dire wolf, Canis dirus, over time. We first present new data on tooth fracture and wear from pits dated older than heretofore analyzed, and demonstrate that fracture and wear events, and the increased competition and heightened carcass utilization they are thought to represent, were of varying intensity across the sampled time intervals. Skull size, and by extension body size, is shown to differ significantly among pits at La Brea, with the strongest single observation being reduction in body size at the last glacial maximum. Skull size variation is shown to be a result of both ontogenetic and evolutionary factors, neither of which is congruent with a temporal version of Bergmann’s rule. Skull shape difference among the pits is also significant, with shape variability attributable to both neotenic effects in populations with high breakage and wear, and evolutionary changes possibly due to climate change. Testing of this hypothesis requires better accuracy and precision in La Brea carbon data, a program that is within the reach of current AMS dating technology