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

Theory of Change Review: A Report Commissioned by Comic Relief

Comic Relief does three things. It raises much needed cash, it then allocates that cash to projects in the United States and in the poorest countries in the world, and it raises awareness of the issues it feels strongly about. This report is one of a series of Comic Relief commissioned learning reports. Some learning reports aim to bring the impact of and learning from some of the work Comic Relief has funded in helping change lives to a wider audience. Other reports aim to draw together learning on key issues from a range of stakeholders to inform Comic Relief's thinking and promote debate in the sector. This report aims to draw together Comic Relief staff and partners' experiences in using theory of change; to identify others in development that are using theory of change and analyse their different approaches and experience; and to capture learning from everyone to promote debate, and to help inform what agencies using or advocating for the use of theory of change do next. This report was commissioned by Comic Relief and written by Cathy James, an independent consultant. The views expressed in this report are those of the author and do not necessarily represent the views of Comic Relief

Graphical models for mediation analysis

Mediation analysis seeks to infer how much of the effect of an exposure on an outcome can be attributed to specific pathways via intermediate variables or mediators. This requires identification of so-called path-specific effects. These express how a change in exposure affects those intermediate variables (along certain pathways), and how the resulting changes in those variables in turn affect the outcome (along subsequent pathways). However, unlike identification of total effects, adjustment for confounding is insufficient for identification of path-specific effects because their magnitude is also determined by the extent to which individuals who experience large exposure effects on the mediator, tend to experience relatively small or large mediator effects on the outcome. This chapter therefore provides an accessible review of identification strategies under general nonparametric structural equation models (with possibly unmeasured variables), which rule out certain such dependencies. In particular, it is shown which path-specific effects can be identified under such models, and how this can be done