Interval Methods

Abstract

We describe a modern approach to parameter estimation, based on set-valued computations combined with a branch and bound step. This allows us to examine entire sets of parameters, and thus to exhaust the global search within a finite number of steps. In addition, we show that the method can be accelerated by set-valued constraint propagation, which allows great improvement of its efficiency. To illustrate the applicability of our method we apply it to some networks of biochemical reactions modeled by a generic class of ODEs called Generalized Mass Action Models (GMAs).