Stability And Dynamics Of Gene Regulatory Network In Cell Reprogramming With Stochastic Fluctuations

The technology of cell reprogramming has a profound impact on drug discovery and screening,transplantation therapy,gene therapy and disease research,which also opens the door to regenerative medicine.However,many experimental results have demonstrated that the process of reprogramming is inefficient,and often incomplete.Therefore,how to improve the efficiency of reprogramming and accelerate the process of reprogramming has become one of the frontier subjects.According to the somatic reprogramming process,this work constructs a feedforward gene network motif based on the timeline of induced pluripotency.The dynamics of this model are studied by using the stochastic dynamic theory and the numerical simulation,the results are shown as following:(1)Based on the feedforward gene network motif and the deterministic differential equations,the numerical simulation results show that,with the sufficient amount of time of exogenous OSKM,gene Y(such as SSEA1)expresses around day 2 and gene Z(such as Nanog,Oct4,Sox2)expresses around day 7.These results fit the experimental results well.Thus,the model can explain the nature of the different phases of reprogramming.(2)6 parameters are chosen and tested how they influence the reprogramming process.The results demonstrate that within the cascade motif,increasing the value of activation rate by upstream gene,the time for the expression of Z will be obviously decreased,which means fast gene expression can lead to a faster response.In addition,transgene OSKM plays a key role in initiating reprogramming.(3)Using Routh-Hurwitz conditions,the stability about the steady states of the system is analyzed.Considering the stochastic effects in cell reprogramming,Gaussian white noises are added to the ODE system and an approximate Fokker-Planck equation is calculated.The Fano factor and susceptibility formulas of gene expression around the steady state are calculated by Langevin theory.Numerical simulations(Gillespie method)are also carried out to verify our theoretical results.