Biological modeling of complex systems: a negative selection approach to systems biology

From the construction of predictive models to the development of pharmacological solutions and diagnostic tools

Understanding the causal mechanisms of a disease is the necessary step to find the most adapted therapeutic solutions and effective biomarkers. To this purpose, a complementary approach between heuristic and mathematical modeling is adapted, since, in biology, functional linearity cannot be assumed and the components of a model cannot incorporate solely what is known.

​Heuristic modeling plays the role of an architect (defines the nature, the structure, the functionalities and the contextual constraints of the system under study) whereas mathematical modeling, to be implemented at a later stage, plays the role of an engineer (reveals the dynamics and robustness of the structures within the system while defining the set of parameters sufficient to give rise to similar or very different phenotypes).

Our methodology is a problems solving approach evaluating each step in a process, searching for satisfactory solutions rather than for optimal solutions, using all available qualitative information instead of quantitative information.

Since it strictly relies upon systematic implementation of negative selection of hypotheses, models arising from this procedure contain elements that had hitherto never been described but cannot be refuted by current knowledge and/or available biological data, thereby generating novel understanding.

Concrete paradigms of our approach from research to development will be presented.