Understanding how genes in a single cell respond to dynamically changing signals has been a central question in stochastic gene transcription research. Recent studies have generated massive steady-state or snapshot mRNA distribution data of individual cells, and inferred a large spectrum of kinetic transcription parameters under varying conditions.  However, there have been few algorithms to convert these static data into the temporal variation of kinetic rates. Real-time imaging has been developed to monitor stochastic transcription processes at the single-cell level, but the immense technicality has prevented its application to most endogenous loci in mammalian cells. In this article, we introduced a stochastic gene transcription model with variable kinetic rates induced by unstable cellular conditions. We approximated the transcription dynamics using easily obtained steady-state formulas in the model. We tested the approximation against experimental data in both prokaryotic and eukaryotic cells and further solidified the conditions that guarantee the robustness of the method. The method can be easily implemented to provide convenient tools for quantifying dynamic kinetics and mechanisms underlying the widespread static transcription data, and may shed a light on circumventing the limitation of current bursting data on transcriptional real-time imaging.

焦锋，副教授，广州大学数学与信息科学学院副院长，从事常微分方程定性理论及其应用研究。近年来围绕随机基因表达的数学刻画开展了生物数学的方法研究及其在分子生物学层面的应用。目前已在《Biophysical J.》，《PLoS Comput. Biol.》，《SIAM J. Appl. Math.》，《Phys. Rev. E》，《J. Differential Equations》等SCI刊物发表论文20余篇，SCI被引1500余次；先后主持国家自然科学基金面上项目和青年基金，广东省自然科学基金面上项目和青年项目；参与国家级重点、重大研究平台项目，是教育部创新团队的核心成员；获湖南省自然科学奖二等奖（第三）。