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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.
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 di
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
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
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. B
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