Bridging qualitative and quantitative modeling of complex systems with FuzzX

The modeling of complex systems often has to deal with the presence of features emerging at multiple scales of complexity, and the availability of data in both qualitative and quantitative form. Correspondingly, many mathematical formalisms were developed to define either quantitative or qualitative models of such systems. Bridging the gap between these two worlds would allow to exploit the advantages provided by both approaches: however, to date the attempts in this direction were limited. A novel, general-purpose computational framework, named FuzzX, is here presented to address this limitation. FuzzX enables the analysis of hybrid models consisting in a quantitative (or mechanistic) and a qualitative module, reciprocally controlling each other’s behavior. FuzzX leverages quantitative information about the system by means of a mechanistic module. At the same time, it describes the behavior of not fully characterized system components by exploiting fuzzy logic to define a qualitative module. FuzzX is here applied for the analysis of a hybrid model of a complex biochemical system, characterized by the presence of several feedback regulations. The results show that FuzzX can reproduce known emergent system behaviors, in normal and perturbed conditions.