Section: Application Domains

Systems Biology

Systems Biology is a recent topic in SUMO. In systems biology, many continuous variables interact together. Biological systems are thus good representatives for large complex quantitative systems, for which we are developing analysis and management methods. For instance, the biological pathway of apoptosis explains how numerous molecules interact inside a cell, triggered by some outside signal (drug, etc.), eventually leading to the death of the cell by apoptosis. While intrinsically quantitative in nature and in problems, data are usually noisy and problems need not be answered with ultimate precision. It thus seems reasonable to resort to approximations in order to handle the state-space explosion resulting from the high dimensionality of biological systems.

We are developing models and abstraction tools for systems biology. Studying these models suggests new reduction methods, such as considering populations instead of explicitly representing every single element into play (be it cells, molecules, etc): we thus develop algorithms handling a population symbolically, either in a continuous (probability distribution) or a discrete (parametric) way. An intermediate goal is to speed-up the analysis of such systems using abstractions, and a long term goal is to develop top-down model-checking methods that can be run on these abstractions.