Mathematical modeling of the microtubule detyrosination/tyrosination cycle for cell-based drug screening design
Fig 3
Mathematical model predictions of the tyrosination reaction activation in proliferative and neuronal cells explaining failures of compound screening.
(A) Sensitivity analysis of the equilibrium value of TyrDetyr obtained for different coefficients of variation of the kinetic parameter Vm2 in the computational model CDTP, indicating a tolerance of five hundred percent for the parameter Vm2 before TyrDetyr deviates from its equilibrium state by eighty percent. The BIOCHAM command used is: sensitivity(F(G(TyrDetyr = x)), [Vm2], [x -> 10], robustness_coeff_var: c), where c is the robustness coefficient value. (B) Similar sensitivity analysis in the computational model CDTN, indicating a tolerance of five hundred percent for the parameter Vm2 before TyrDetyr deviates from its equilibrium state by fifteen percent. The BIOCHAM command used is: sensitivity(F(G(TyrDetyr = x)), [Vm2], [x -> 0.065386], robustness_coeff_var: c) where c is the robustness coefficient value. (C) Perturbed numerical simulation in the model CDTP. The tyrosination rate constant Vm2 is increased at 20 units of time (min) by a factor ten. The numerical simulation shows that the tyrosination status do not increase. (D) Perturbed numerical simulation in the model CDTN. The tyrosination rate constant Vm2 is increased at time 60 (min) by a factor ten. The numerical simulation shows that tyrosinated species slightly increase but are not greater than detyrosinated species at steady state.