讲座题目：A computational model for analyzing stochasticity in gene transcription
主 讲 人：孙启文 副教授（广州大学）
In single cells, message RNA molecules fluctuate heavily even within a homogeneous environment. Fluctuation in gene transcription is considered to be a main source of phenotypic heterogeneity. Highly variable mRNA distributions are primarily attributed to randomly switching between periods of active (ON) and inactive (OFF) gene transcription. In most studies, the waiting-time distributions for gene spending in its ON- or OFF-state are typically modeled as exponential distributions. However, increasing data indicate that a gene's lifetimes at ON- or OFF-state are non-exponential distributions. By combining the Kolmogorov forward equations with the alternating renewal processes, we present a novel method to compute the average transcription level and its noise. Taking the lifetimes of the OFF and the ON states having the Erlang distribution, for instance, we derive the mean transcription level and the noise in mRNA copy numbers at a steady state. The computation approach we constructed in this paper to calculate the mean and the noise of mRNA transcripts can be used to treat a series of transcription cycles that are non-lattice.
孙启文，男，博士，广州大学副教授，主要从事生物数学分子生物学的研究工作。在Journal of Mathematical Biology, Journal of Theoretical Biology, PLoS Computational Biology等刊物发表论文多篇。博士学位论文《基因转录多路径模型及转录噪声的调节分析》入选2013年广东省优秀博士学位论文。