Transcriptional stochasticity in gene expression

• Verfasst von: Lipniacki, Tomasz [VerfasserIn]
• Verfasst von: Marciniak-Czochra, Anna [VerfasserIn]
• Titel: Transcriptional stochasticity in gene expression
• Verf.angabe: Tomasz Lipniacki, Pawel Paszek, Anna Marciniak-Czochra, Allan R. Brasier, Marek Kimmel
• Jahr: 2006
• Jahr des Originals: 2005
• Umfang: 20 S.
• Fussnoten: Available online 21 July 2005 ; Gesehen am 20.07.2018
• Titel Quelle: Enthalten in: Journal of theoretical biology
• Ort Quelle: Amsterdam : Elsevier Ltd., 1961
• Jahr Quelle: 2006
• Band/Heft Quelle: 238(2006), 2, Seite 348-367
• ISSN Quelle: 1095-8541
• DOI: doi:10.1016/j.jtbi.2005.05.032
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Gene regulation
• Sach-SW: Probability density function
• Sach-SW: Stochasticity
• Sach-SW: Transcription
• Sach-SW: Transport-type equations
• K10plus-PPN: 1577808916

Abstract

Due to the small number of copies of molecular species involved, such as DNA, mRNA and regulatory proteins, gene expression is a stochastic phenomenon. In eukaryotic cells, the stochastic effects primarily originate in regulation of gene activity. Transcription can be initiated by a single transcription factor binding to a specific regulatory site in the target gene. Stochasticity of transcription factor binding and dissociation is then amplified by transcription and translation, since target gene activation results in a burst of mRNA molecules, and each mRNA copy serves as a template for translating numerous protein molecules. In the present paper, we explore a mathematical approach to stochastic modeling. In this approach, the ordinary differential equations with a stochastic component for mRNA and protein levels in a single cells yield a system of first-order partial differential equations (PDEs) for two-dimensional probability density functions (pdf). We consider the following examples: Regulation of a single auto-repressing gene, and regulation of a system of two mutual repressors and of an activator-repressor system. The resulting PDEs are approximated by a system of many ordinary equations, which are then numerically solved.