Rza BASHIROV, Nimet AKÇAY, Adil ŞEYTANOĞLU, Mani MEHRAEI, Recep DURANAY

Exploiting stochastic Petri nets with fuzzy parameters to predict efficient drug combinations for Spinal Muscular Atrophy

Randomness and uncertainty are two major problems one faces while modeling nonlinear dynamics ofmolecular systems. Stochastic and fuzzy methods are used to cope with these problems, but there is no consensus amongresearchers regarding which method should be used when. This is because the areas of applications of these methodsare overlapping with differences in opinions. In the present work, we demonstrate how to use stochastic Petri nets withfuzzy parameters to manage random timing of biomolecular events and deal with the uncertainty of reaction rates inbiological networks. The approach is demonstrated through a case study of simulation-based prediction of efficient drugcombinations for spinal muscular atrophy, for which we obtained very promising results. The feasibility of the approach isassessed through statistical analysis of deterministic, pure stochastic and fuzzy stochastic simulation results. Statisticalanalysis reveals that all three models produce significantly different results which, when coupled with the fact that fuzzystochastic model provides the closest approximation of underlying biological network, successfully coping not only withrandomness but also uncertainty, suggests that fuzzy stochastic model is the most appropriate choice for the present casestudy. The proposed approach can be adapted or extended to other biological networks.

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