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The Executable Pathway to Biological Networks

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If you have a question about this talk, please contact Laura Blackburn.

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.

This talk is part of the Seminars on Quantitative Biology @ CRUK Cambridge Institute series.

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