GENAVOS: A New Tool for Modelling and Analyzing Cancer Gene Regulatory Networks Using Delayed Nonlinear Variable Order Fractional System

Gene regulatory networks (GRN) are one of the etiologies associated with cancer. Their dysregulation can be cancer formation and asymmetric cellular functions in stem cells, leading to disease persistence resistance treatment. Systems that model complex dynamics these along adapting partially known real omics data closer reality may useful understand mechanisms underlying neoplastic phenomena. In this paper, for first time, modelling GRNs is performed using delayed nonlinear variable order fractional (VOF) systems state space by a new tool called GENAVOS. Although uses gene expression time series identify optimize system parameters, it also models possible epigenetic signals, results show VOF have very good flexibility data. We found cells actually larger delay parameter than normal cells. It create weak chaotic, periodic, quasi-periodic oscillations changing parameters. Chaos onset Our findings indicate profound effect time-varying orders on networks, which related type memory. By (possible epigenetics signals) cell system, its behaviour becomes quite similar cell. This work confirms effective role miR-17-92 cluster as an factor cycle.