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Register a new userStochastic equations for a self-regulating gene
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Authors
Martin Jansen
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Expression of cellular genes is regulated by binding of transcription factors to their promoter, either activating or inhibiting transcription of a gene. Particularly interesting is the case when the expressed protein regulates its own transcription. In this paper the features of this self-regulating process are investigated. In the here presented model the gene can be in two states. Either a protein is bound to its promoter or not. The steady state distributions of protein during and at the end of both states are analyzed. Moreover a powerful numerical method based on the corresponding master equation to compute the protein distribution in the steady state is presented and compared to an already existing method. Additionally the special case of self-regulation, in which protein can only be produced, if one of these proteins is bound to the promoter region, is analyzed. Furthermore a self-regulating gene is compared to a similar gene, which also has two states and produces the same amount of proteins but is not regulated by its protein-product.
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Decisions in the cell that lead to its ultimate fate are important for cellular functions such as proliferation, growth, differentiation, development and death. Understanding this decision process is imperative for advancements in the treatment of diseases such as cancer. It is clear that underlying gene regulatory networks and surrounding environments of the cells are crucial for function. The self-repressor is a very abundant gene regulatory motif, and is often believed to have only one cell fate. In this study, we elucidate the effects of microenvironments mimicking the epigenetic effects on cell fates through the introduction of inducers capable of binding to a self-repressing gene product (protein), thus regulating the associated gene. This alters the effective regulatory binding speed of the self-repressor regulatory protein to its destination DNA without changing the gene itself. The steady state observations and real time monitoring of the self-repressor expression dynamics reveal the emergence of the two cell fates, The simulations are consistent with the experimental findings. We provide physical and quantitative explanations for the origin of the two phenotypic cell fates. We find that two cell fates, rather than a single fate, and their associated switching dynamics emerge from a change in effective gene regulation strengths. The switching time scale is quantified. Our results reveal a new mechanism for the emergence of multiple cell fates. This provides an origin for the heterogeneity often observed among cell states, while illustrating the influence of microenvironments on cell fates and their decision-making processes without genetic changes
Gene transcription is a stochastic process mostly occurring in bursts. Regulation of transcription arises from the interaction of transcription factors (TFs) with the promoter of the gene. The TFs, such as activators and repressors can interact with the promoter in a competitive or non-competitive way. Some experimental observations suggest that the mean expression and noise strength can be regulated at the transcription level. A Few theories are developed based on these experimental observations. Here we re-establish that experimental results with the help of our exact analytical calculations for a stochastic model with non-competitive transcriptional regulatory architecture and find out some properties of Noise strength (like sub-Poissonian fano factor) and mean expression as we found in a two state model earlier. Along with those aforesaid properties we also observe some anomalous characteristics in noise strength of mRNA and in variance of protein at lower activator concentrations.
Biochemical reaction networks frequently consist of species evolving on multiple timescales. Stochastic simulations of such networks are often computationally challenging and therefore various methods have been developed to obtain sensible stochastic approximations on the timescale of interest. One of the rigorous and popular approaches is the multiscale approximation method for continuous time Markov processes. In this approach, by scaling species abundances and reaction rates, a family of processes parameterized by a scaling parameter is defined. The limiting process of this family is then used to approximate the original process. However, we find that such approximations become inaccurate when combinations of species with disparate abundances either constitute conservation laws or form virtual slow auxiliary species. To obtain more accurate approximation in such cases, we propose here an appropriate modification of the original method.
Living cells must control the reading out or expression of information encoded in their genomes, and this regulation often is mediated by transcription factors--proteins that bind to DNA and either enhance or repress the expression of nearby genes. But the expression of transcription factor proteins is itself regulated, and many transcription factors regulate their own expression in addition to responding to other input signals. Here we analyze the simplest of such self-regulatory circuits, asking how parameters can be chosen to optimize information transmission from inputs to outputs in the steady state. Some nonzero level of self-regulation is almost always optimal, with self-activation dominant when transcription factor concentrations are low and self-repression dominant when concentrations are high. In steady state the optimal self-activation is never strong enough to induce bistability, although there is a limit in which the optimal parameters are very close to the critical point.
The X-valuation adjustment (XVA) problem, which is a recent topic in mathematical finance, is considered and analyzed. First, the basic properties of backward stochastic differential equations (BSDEs) with a random horizon in a progressively enlarged filtration are reviewed. Next, the pricing/hedging problem for defaultable over-the-counter (OTC) derivative securities is described using such BSDEs. An explicit sufficient condition is given to ensure the non-existence of an arbitrage opportunity for both the seller and buyer of the derivative securities. Furthermore, an explicit pricing formula is presented in which XVA is interpreted as approximated correction terms of the theoretical fair price.
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