Vincent Danos

We are pleased to announce the appointment of Vincent Danos to a Chair in Computational Systems Biology.

Vincent has pursued various lines of research during his career, from mathematical logic and the semantics of programming languages, to probabilistic and agent-based models, and comes to us from a position as Directeur de Recherches at the CNRS. He has spent the past year visiting the Harvard Medical School, and working in a start-up company trying to bring agent based techniques to bear on the representation of cellular signalling networks.

At Edinburgh he plans to lead the development of an efficient bottom-up simulation platform for cellular signalling, that will enable the rapid generation of cellular insight—including causal information—without requiring significant modeling or quantitative capability from the user.

Rule-based Modeling of Cellular Signalling

Public Lecture

Speaker: Vincent Danos
Title: Rule-based Modeling of Cellular Signalling
Date: Thursday 20th September
Time: 17:00-18:00
Place: e-Science Institute, 13-15 South College Street, Edinburgh

Modelling is becoming a necessity in studying biological signalling pathways, because the combinatorial complexity of such systems rapidly overwhelms intuitive and qualitative forms of reasoning. Yet, this same combinatorial explosion makes the traditional modelling paradigm based on systems of differential equations impractical. In contrast, agent-based or concurrent languages, such as kappa describe biological interactions in terms of rules, thereby avoiding the combinatorial explosion besetting differential equations. Rules are expressed in an intuitive graphical form that transparently represents biological knowledge. In this way, rules become a natural unit of model building, modification, and discussion. We illustrate this with a sizeable example obtained from refactoring two models of EGF receptor signalling that are based on differential equations. An exciting aspect of the agent-based approach is that it naturally lends itself to the identification and analysis of the causal structures that deeply shape the dynamical, and perhaps even evolutionary, characteristics of complex distributed biological systems. In particular, one can adapt the notions of causality and conflict, familiar from concurrency theory, to kappa, our representation language of choice. Using the EGF receptor model as an example, we show how causality enables the formalization of the colloquial concept of pathway and, perhaps more surprisingly, how conflict can be used to dissect the signalling dynamics to obtain a qualitative handle on the range of system behaviours. By taming the combinatorial explosion, and exposing the causal structures and key kinetic junctures in a model, agent- and rule-based representations hold promise for making modelling more powerful, more perspicuous, and of appeal to a wider audience.