Research On The Measurement Method Of Random Feature In Gene Expression

Gene expression accompanies life from start to finish,reduced to two steps:transcription and translation.In fact,gene expression is a very complex process.With the development of science and technology,researchers have proved through experiments that this process is discontinuous and random,which involves the conversion and staining between gene promoter activity and inactivity.Recombination,histone modification and other reactions.There are many biochemical reaction steps involved in each reaction in the cell,which leads to the discontinuity of gene expression,and the probability distribution of the waiting time between reactions no longer obeys the exponential distribution(no memory),so the reaction is A non-Markovian discrete stochastic process is caused by the modulation of memory molecules between them.The researchers have now successfully investigated this process using generalized chemical master equation models,but they have primarily studied stochastic models of burst gene expression.However,these models do not take into account the regulatory role of feedback networks in gene expression,and how the interaction between feedback regulation and memory molecules affects gene expression is unclear.In this dissertation,we mainly analyze the dynamic effects of feedback regulation and memory molecules on gene expression.A generalized stochastic chemical master equation model with positive feedback and negative feedback regulation is established,which is regulated by memory molecular parameters k and feedback regulation parameters β.This dissertation deduces the analytical expressions of the gene expression products(proteins)in the two models under steady-state conditions,and uses the generalized chemical master equation,the probability distribution of proteins and noise to measure random features such as memory molecules and feedback regulation.The experimental results show that memory molecules compete with positive feedback in suppressing protein noise.For generalized stochastic models with negative feedback,memory molecules can strengthen the suppression of this noise,and these results suggest that memory molecules and feedback have the same role in affecting gene expression.The specific work of this dissertation is as follows:1.In order to fully understand the biochemical reaction network under the nonMarkov chain,this dissertation studies the gene expression model of the nonMarkovian process.The generalized chemical master equation model with positive feedback control is analyzed through the chemical continuous time random walk theory.In the response network of gene expression,multiple internal reactions are involved between each reaction,which is considered to be regulated by memory molecules,so the waiting time between each reaction is non-exponential distribution.Assuming that the production and degradation processes of proteins obey the Erlang probability distribution,the analytical expression of the protein probability distribution of the gene expression model is derived.In a steady state,the generalized stochastic model with positive feedback regulation increases with the increase of the parameters k of the memory molecule,the peak value of the probability distribution of the protein increases,but the probability of protein production in large quantities decreases with the increase of k,and the memory molecule can reduce the protein Probability of mass generation.2.After obtaining the probability distribution of the protein with positive feedback regulation through the generalized chemical master equation,the noise of the protein stationary distribution will be further analyzed,and the analytical formula of the noise intensity(Fano factor)of the protein will be deduced.In equilibrium,the Fano factor of the protein decreases with increasing memory molecular parameters k.In addition,considering the regulatory effect of negative feedback on protein production,it was found that negative feedback usually suppresses protein noise during gene expression.Then,we deeply understood the interaction between memory molecules and feedback regulation,and found that memory molecules would further strengthen the strength of this negative feedback inhibition;while in the positive feedback model,there is an interaction between positive feedback and memory molecules.