Xuan Zhan

Intrinsic noise in post-transcriptional gene regulation by small non-coding RNA

Small non-coding RNA (sRNA) plays very important role in the post transcriptional regulation in various organisms. In complex regulatory networks, highly significant relative fluctuations in RNAs copy numbers can not be neglected due to very small copy number of individual RNA molecules. Here we consider two simple regulation schemes, where one is single target gene regulated by a sRNA and the other is two target mRNAs (mRNA(R) and mRNA(T)) regulated by one sRNA. The Fano factor (a measure of the relative size of the internal fluctuations) formulae of RNA molecules in the post transcriptional regulation are theoretically derived by using of the Langevin theory. For single target gene regulated by a sRNA, it is shown that the intrinsic noise of both mRNA and sRNA approaches the bare Poissonian limit in the regimen of both target RNA silencing and surviving. However, the strong anti-correlation between the fluctuations of two components result in a large intrinsic fluctuations in the level of RNA molecules in the regimen of crossover. For two target mRNAs regulated by one sRNA, in the regimen of crossover, it is found that, with the increasing of transcription rate of target mRNA(T), the maximal intrinsic fluctuation of RNA molecules is shifted from sRNA to target mRNA(R), and then to target mRNA(T). The intrinsic noise intensity of target mRNA(R) is determined by both the transcriptional rate of itself and that of sRNA, and independent of the transcriptional rate of the other target mRNA(T).

Effects of ion channel blocks on electrical activity of stochastic Hodgkin–Huxley neural network under electromagnetic induction

Abstract The effects of channel blocks on the spontaneous spiking activity and neuron networks pattern selection are investigated by using an improved Hodgkin–Huxley (HH) model in which the electromagnetic induction is considered and the magnetic flux is used to describe the influence of electromagnetic field. The discharge behavior of neurons induced by potassium ion and sodium ion channel blocks was analyzed by numerical simulation in the improved HH model. The results suggest that changes in the maximum conductance of potassium channels can cause spontaneous discharge behavior of neurons. The poisoning of potassium ion can be weakened by the electromagnetic radiation in the neural network, and the neural network presents a state of spatial order in the case of spiral waves. It is interesting that the ordered waveform is generated by no-flux boundary condition when the initial states are selected as wedge-shaped type in the network. In addition, potassium channel blocks can promote the discharge of neurons and facilitate the formation of spiral waves in the neural network. By contrast, the electrical activity of neurons is inhibited by sodium channel blocks. The influence of membrane patch size on the electrical activity of single neuron is greater than that on the collective behavior of neural network. This research will enhance understanding of the role of toxins in neuronal firing and collective behavior of real neural systems.

Propagation of firing rate by synchronization in a feed-forward multilayer Hindmarsh–Rose neural network

Abstract The feed-forward neural network is an artificial neural network, which is used extensively in deep learning models, wherein synaptic weight and characteristic time play very important role in information moves. In this paper, based on a feed-forward multilayer (ten layers) Hindmarsh–Rose (HR) neural network, the effects of synaptic weight and characteristic time on the signal propagation are investigated under the cases of continuous and transient external stimulated current, respectively. In the presence of continuous external stimulated current triggering the discharge of neurons, it is found that a random input signal driven by Gaussian white noise can be transmitted from input layer to next layers, and the propagation of weak spike train is gradually disappeared in the following layers when the synaptic weight is small. However, by choosing the appropriate values of synaptic weight and characteristic time, the mean firing rate of neurons in output layer is increased and the synchronization of neural firing in the following layers can be triggered. In the presence of transient (a short period) stimulated current triggering the discharge of neurons on the input layer, the firing rate of neurons cannot be transmitted from the input layer to the following layers with a small synaptic weight. Moreover, with the increasing of the synaptic weight, the mean firing rates of neurons in the following layers are higher than that in input layer, and the neurons in the following layers can be excited.