随机基因表达及基因网络中信号的调节分析

Stochastic gene expression and its regulation are the core problems of molecular biology as well as important branches in the present study of life science. In recent years, it has been increasingly recognized that stochastic mechanisms play a key role in the dynamics of biological systems. Signal transduction networks are one example where the stochastic mechanisms are of particular importance, which would produce fluctuations in the numbers of mRNAs and proteins, constituting the phenotypic heterogeneity within a genetically homogeneous population of cells. In this program, we will systematically develop and improve mathematical models of stochastic gene expression to study how environmental signals and the intrinsic cellular contexts are combined to regulate stochastic gene transcription. To characterize the dynamical behavior of stochastic gene expression, we use several quantities: the mean numbers of mRNAs and proteins produced in individual cells, their noise and noise strength, transcriptional efficiency, probability distribution function. By mathematical analysis, numerical simulation, we will understand deeply how the signal transduction networks regulate gene expression. The work will help us understand the mechanism and origination of gene expression noise, the impact of noise on the function of innate immune system. The study of noise in gene expression will provide novel insights into how cells obtain and answer signals to remain viable in stochastic environments. This line of research is likely to further our understanding and treatment of human disease.

随机基因表达及其调控是分子生物学的核心问题，是当前生命科学研究的重要分支。近年来，人们逐渐认识到随机机制在生物系统中起着至关重要的作用。例如在信号传导网络中，随机机制尤为重要，它可使在基因相同的细胞群中合成的mRNA和蛋白质数量不同，并构成它们的表型不均一。为研究环境信号及在细胞内的一系列反应如何共同调节随机基因表达的，我们将系统建立和完善基因表达的数学模型。为刻画随机基因表达的动力学行为，我们将引入若干变量：单个细胞内生成的mRNA和蛋白质的平均数量、噪声与噪声强度、转录效率、概率分布函数等。通过数学分析、数值模拟等方法，我们可深入了解信号传导网络对基因表达的综合调控。这项研究可以帮助我们了解基因表达噪声产生的机制、根源及在先天免疫系统中的功能。对噪声的研究可使我们认识到在随机环境中细胞是如何接受、应对信号并保持自行生产发育的，同时这项研究可能进一步推动我们对人类疾病的理解与治疗。

本项目围绕随机基因转录的网络模型展开研究，通过整合实验数据，结合分析、统计、计算机模拟等方法，得到了多种信号相互作用下mRNA的表达水平及噪声的解析表达式，定性及定量地刻画了基因转录的随机行为，探讨了信号网络对免疫基因的随机调控。我们探讨了可诱导基因在多种信号作用下的转录动态行为，利用微分方程理论、随机过程等方法讨论了mRNA数量的概率分布及其对应的动态特征和参数区域，得到了两状态转录模型对应的mRNA的分布类型，通过动力系统和概率论等方法探索了基因转录噪声的调控机制。..此外，我们协助广州大学生物数学研究团队举办了三次大型的国际学术会议(两次生物数学国际学术会议，第九届应用动力系统最新进展国际学术会议)，一次全国会议(第十四届全国泛函微分方程学术会议)，两次生物数学暑期班，以及多次专题讲习班，与美国、加拿大、澳大利亚、荷兰、芬兰及香港地区的数学家及生物学家进行了深入的交流，建立了合作关系。