What can we learn when fitting a complex gene expression model to a simple telegraph model?
焦锋 教授
广州大学 数学与信息科学学院
Host： 段金桥教授
Date： 2023年6月27日 (周二)
Time： 10:30-11:30 A.M.
Venue： Tencent Meeting ID：746-929-903
国际创新创业社区A5栋1806会议室
Abstract：
In experiments, the distributions of mRNA or protein numbers in single cells are often fitted to the random telegraph model which includes synthesis and degradation of mRNA or protein, and switching of the gene between active and inactive states. While commonly used, this model does not describe how fluctuations are influenced by crucial biological mechanisms such as feedback regulation, non-exponential gene inactivation durations, and multiple gene activation pathways. Here we investigate the dynamical properties of four complex gene expression models by fitting their steady-state mRNA or protein number distributions to the simple telegraph model. We show that despite the underlying complex biological mechanisms, the telegraph model with three effective rate parameters can accurately capture the steady-state gene product distributions, as well as the conditional distributions in the active gene state, of the complex models. Some effective rate parameters are reliable and can reflect realistic dynamic behaviors of the complex models, while others may deviate significantly from their real values in the complex models. The effective parameters can be also applied to characterize the capability for a complex model to exhibit multimodality. Using additional information such as single-cell data at multiple time points, we provide an effective method of distinguishing the complex models from the telegraph model. Furthermore, using measurements under varying experimental conditions, we show that fitting the mRNA or protein number distributions to the telegraph model may even reveal the underlying gene regulation mechanisms of the complex models. The effectiveness of these methods is confirmed by analysis of single-cell data for E. coli and mammalian cells.
Biography：
焦锋，教授，博导，广州大学数学与信息科学学院副院长，近年来围绕随机基因表达的数学刻画开展了生物数学方法研究及其在分子生物学层面的应用，相关工作在《Bioinformatics》，《Biophysical J.》，《PLoS Comput. Biol.》，《SIAM J. Appl. Math.》等刊物发表。先后主持国家自然科学基金，广东省自然科学基金，获评湖南省自然科学奖二等奖、秦元勋青年数学奖。
