Bistability, or more generally multistability, is an important recurring theme
in biological systems. In particular, the discovery of bistability in signal pathways of genetic
networks, prompts strong interest in understanding both the design and function of
these networks. Therefore, modelling these systems is crucial to understand their behaviors,
and also to analyze and identify characteristics that would otherwise be di cult to
realize. Although di erent classes of models have been used to study bistable dynamics,
there is a lag in the development of models for bistable systems starting from experimental
data. This is due to the lack of detailed knowledge of biochemical reactions and
kinetic rates.
In this work, we propose a procedure to develop, starting from observed dynamics,
Metabolic P models for multistable processes. As a case study, a mathematical model
of the Schl ogel's dynamics, which represents an example of a chemical reaction system
that exhibits bistability, is inferred starting from observed stochastic bistable dynamics.
Since, recent experiments indicate that noise plays an important role in the switching of
bistable systems, the success of this work suggests that this approach is a very promising
one for studying dynamics and role of noise in biological systems, such as, for example,
genetic regulatory networks
