Intrinsic fluctuation and susceptibility in somatic cell reprogramming process

Based on the coherent feedforward transcription regulation loops in somatic cell reprogramming process, a stochastic kinetic model is proposed to study the intrinsic fluctuations in the somatic cell reprogramming. The Fano factor formulas of key genes expression level in the coherent feedforward transcription regulation loops are derived by using of Langevin theory. It is found that the internal fluctuations of gene expression levels mainly depend on itself activation ratio and degradation ratio. When the self-activation ratio(or self-degradation ratio) is increased, the Fano factor increases reaches a maximum and then decreases. The susceptibility is used to measure the sensitivity of steady-state response to the variation in systemic parameters. It is found that with the increase of the self-activation ratio(or self-degradation ratio), the susceptibility of steady-state increases at first, it reaches a maximum, and it then decreases. The magnitude of the maximum is increased with the increase of activated ratio by the upstream transcription factor.

Synchronization transition of a modular neural network containing subnetworks of different scales

Time delay and coupling strength are important factors that affect the synchronization of neural networks.In this study,a modular neural network containing subnetworks of different scales was constructed using the Hodgkin–Huxley(HH)neural model;i.e.,a small-scale random network was unidirectionally connected to a large-scale small-world network through chemical synapses.Time delays were found to induce multiple synchronization transitions in the network.An increase in coupling strength also promoted synchronization of the network when the time delay was an integer multiple of the firing period of a single neuron.Considering that time delays at different locations in a modular network may have different effects,we explored the influence of time delays within each subnetwork and between two subnetworks on the synchronization of modular networks.We found that when the subnetworks were well synchronized internally,an increase in the time delay within both subnetworks induced multiple synchronization transitions of their own.In addition,the synchronization state of the small-scale network affected the synchronization of the large-scale network.It was surprising to find that an increase in the time delay between the two subnetworks caused the synchronization factor of the modular network to vary periodically,but it had essentially no effect on the synchronization within the receiving subnetwork.By analyzing the phase difference between the two subnetworks,we found that the mechanism of the periodic variation of the synchronization factor of the modular network was the periodic variation of the phase difference.Finally,the generality of the results was demonstrated by investigating modular networks at different scales.

Effects of potassium channel blockage on inverse stochastic resonance in Hodgkin-Huxley neural systems

Inverse stochastic resonance(ISR)is a phenomenon in which the firing activity of a neuron is inhibited at a certain noise level.In this paper,the effects of potassium channel blockage on ISR in single Hodgkin-Huxley neurons and in small-world networks were investigated.For the single neuron,the ion channel noise-induced ISR phenomenon can occur only in a certain small range of potassium channel blockage ratio.Bifurcation analysis showed that this small range is the bistable region regulated by the external bias current.For small-world networks,the effect of non-homogeneous network blockage on ISR was investigated.The network blockage ratio was used to represent the proportion of potassium-channel-blocked neurons to total network neurons.It is found that an increase in network blockage ratio at small coupling strengths results in shorter ISR duration.When the coupling strength is increased,the ISR is more significant in the case of a large network blockage ratio.The ISR phenomenon is determined by the network blockage ratio,the coupling strength,and the ion channel noise.Our results will provide new perspectives on the observation of ISR in neuroscience experiments.