Mathias John, post-doc équipe Biocomputing, IRI
Cell-biological systems are complex interaction networks of biochemical components that are often significantly impacted by stochastic and spatial considerations. To study cell-biological systems, formal modeling provides a method to overcome the limits of experimental observation in the wet-lab by moving to the abstract world of the computer. Therefore, the system under study is formally described in some modeling language and it is the expressiveness of this language that in turn defines the limits of the abstract world.
This talk is to show my ideas of considering spatial aspects in stochastic models of cell-biological systems. First, it shall be discussed what are spatial aspects of interest when studying cellular interaction networks and what are the different abstraction levels to look at them. Second, approaches shall be provided to model these spatial aspects at different abstraction levels and it shall be scatched how the expressiveness of languages can limit modelers in this task. Therefore, I will first present a rough overview on the iterative process of formal modeling and provide the concept of stochastic model languages with their syntax and their semantics in terms of continuous time Markov chains. Then, a case study at the hand of small examples shall show to what extend spatial aspects of cell-biological systems can be modeled at different abstraction levels.
The modeling language of choice for my talk is React(C) that was recently developed by the BioComputing group. A main feature of React(C) is that it is more natural to the domain as other languages, since it is rule-based, i.e. interactions of biochemical components can be described in terms of chemical reaction rules. At the same time, React(C) is very expressive as it provides reaction constraints, extending the possibilities of modelers to consider spatial aspects.
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