Jasmin Fisher, Microsoft Research Cambridge, jasmin.fisher 'AT' microsoft.com

The Executable Pathway to Biological Networks

Abstract:

Computational modelling of biological systems is becoming increasingly important in efforts to better understand complex biological behaviours. Executable Biology is a pioneering approach focused on the design of executable computer programs that mimic biological phenomena. While traditional mechanistic models in biology are usually described by diagrams(giving a fairly static picture of cellular processes), executable biology seeks to translate such static diagrams into dynamic models using formal computational methods that were originally designed for the construction and analysis of complex man-made systems (e.g., computers and computer programs). In this talk, I will illustrate the usefulness of this framework to model signalling pathways using the following examples: (1) our modelling work of the EGFR/Notch signalling crosstalk during the process of cells fate determination in C. elegans vulval development. This model brings forward intricate timing considerations in the operation of these signals, which were also validated experimentally; (2) a more recent model describing metabolic disturbance in fat tissue with relation to diabetes and obesity. Constructing this model and running it against the experimental observations has highlighted two key nodes in the processes of early obesity: the transcription factor Mlxipl, and the metabolic intermediate acetyl CoA. The model suggests that these act synergistically to affect fatty acid production, which is likely to be a key intermediate phase along the obesity timeline. These provide focus points for further biological study; and (3) a detailed analysis of a molecular model describing the EGFR pathway, leading to a more abstract view of the different modules of this network. Our analysis suggests that the pathway contains regions of functional redundancy in the upstream modules. Downstream modules, like Ras and ERK, have fewer redundancies, and strong inhibition of specific reactions in those modules greatly attenuates signal response. We have also identified positive feedback loops whose role is to prolong the active state of key components, and negative feedback loops that help promote signal adaptation and stabilization.

Hidde de Jong, INRIA Grenoble - Rhône-Alpes, Hidde.de-Jong 'AT' inria.fr

Qualitative Modeling and Simulation of Bacterial Regulatory Networks

Abstract:

The adaptation of microorganisms to their environment is controlled at the molecular level by large and complex networks of biochemical reactions involving genes, RNAs, proteins, metabolites, and small signalling molecules. In theory, it is possible to write down mathematical models of these networks, and study these by means of classical analysis and simulation tools. In practice, this is not easy to achieve though, as quantitative data on kinetic parameters are usually absent for most systems of biological interest. Moreover, the models consist of a large number of variables, are strongly nonlinear and include different time-scales, which make them difficult to handle both mathematically and computationally.

We have developed methods for the reduction and approximation of kinetic models of bacterial regulatory networks to simplified, so-called piecewise-linear differential equation models. The qualitative dynamics of the piecewise-linear models can be studied using discrete abstractions from hybrid systems theory. This enables the application of model-checking tools to the formal verification of dynamic properties of the regulatory networks. The above approach has been implemented in the publicly-available computer tool Genetic Network Analyzer (GNA) and has been used to analyze a variety of bacterial regulatory networks.

I will illustrate the application of GNA by means of the network of global transcription regulators controlling the adaptation of the bacterium Escherichia coli to environmental stress conditions. Even though E. coli is one of the best studied model organisms, it is currently little understood how a stress signal is sensed and propagated through the network of global regulators, and leads the cell to respond in an adequate way. Qualitative modeling and simulation of the network of global regulators has allowed us to identify essential features of the transition between exponential and stationary phase of the bacteria and to make new predictions on the dynamic behavior following a carbon upshift.

Grzegorz Rozenberg, University of Leiden, rozenber 'AT' liacs.nl (cancelled)

Modeling interactions between biochemical reactions

Abstract:

For a biochemical reaction to take place, the following two conditions have to be satisfied: all the reactants must be present and none of the inhibiting conditions may be present. Hence, on this level of abstraction, a biochemical reaction is driven by two regulation mechanisms: facilitation and inhibition. Thus, the interactions between individual reactions take place through their influence on each other, and, again, this influence is based on facilitation and inhibition.

Reaction systems is a formal framework for investigation of biochemical reactions based on the facilitation and inhibition mechanisms. In our lecture we discuss the underlying setup of this framework. We present the basic notions of reaction systems and their dynamic behavior, pointing out that it is methodologically different from (often orthogonal to) corresponding notions in standard models considered in theoretical computer science. We motivate these differences by explaining our basic assumptions which stem from organic chemistry of living cells. Also, we present some typical, biochemistry related notions and research problems as well as demonstrate some typical results.

The lecture is of a tutorial character and self-contained.