New paper accepted on IEEE Transactions on Fuzzy Systems

Some years ago we began exploiting Fuzzy Logic (FL) for the modeling of complex systems. In particular, we used FL to create interpretable models of heterogeneous cellular systems characterized by uncertainty.

In this context, we thought that some parts of the models could be coupled to detailed mechanistic formalisms (e.g., systems of coupled differential equations). However, we quickly realized that bridging the two modeling approaches is extremely difficult: the fusion of the two worlds required the development of a new theory, in particular on the interface between the FL and the mechanistic regimes. This research is the main focus of the Ph.D. student I am co-advising — Simone Spolaor — who worked in the last two years on a new mathematical framework named FuzzX.

The paper about FuzzX, titled “Coupling Mechanistic Approaches and Fuzzy Logic to Model and Simulate Complex Systems”, was accepted today on the prestigious journal IEEE Transactions on Fuzzy Systems. In the results, we show that a FuzzX model of a biochemical model (the Ras/cAMP/PKA pathway) is able to reproduce the emergent behavior (e.g., specific transient, stable oscillations, intrinsic noise) handling the uncertainty of the model and requiring a reduced number of kinetic parameters. Notably, FuzzX simulation requires a fraction of the original computational effort due to stochastic simulation.