In single-cell research, understanding how stochasticity in gene transcription influences phenotypic heterogeneity within isogenic cell populations represents a fundamental question. Fluctuations in gene expression are characterized by statistical metrics such as mRNA distribution patterns and transcriptional noise, where the transcriptional states of critical genes often determine the progression of cell fate transitions. However, traditional deterministic models of transcriptional thresholds fail to fully explain the heterogeneity in cell fate decisions, particularly within complex gene regulatory networks and under environmental stresses. Consequently, developing quantitative frameworks to elucidate how different gene activation mechanisms impact cell fate holds significant importance for advancing our understanding of development, disease pathogenesis, and therapeutic strategies.
Recently, the research team led by Moxun Tang at the Department of Mathematics of Michigan State University and Feng Jiao at the Guangzhou Applied Mathematics Center of Guangzhou University published a study "Quantifying cell fate change under different stochastic gene activation frameworks" in the journal Quantitative Biology . By integrating mathematical modeling with experimental data, the researchers systematically analyzed how transcriptional noise regulates cell fate decisions under three distinct gene activation frameworks: the classical telegraph model, the three-state model, and the cross-talk pathway model.
The research team introduced two key quantitative concepts - the transcriptional threshold and jumping index - to establish the relationship between gene expression levels and cell fate transition probabilities. Through systematic construction and analysis of three stochastic gene expression models (the classical telegraph model, three-state model, and cross-talk pathway model; Figure 1), the study comprehensively investigated how transcriptional noise influences cell fate decisions under different gene activation frameworks. By integrating experimental data from HIV latency and yeast stress response systems, the work revealed some principles governing noise-mediated regulation of cell fate transitions.
Quantitative Biology
Experimental study
Not applicable
Quantifying cell fate change under different stochastic gene activation frameworks
15-Mar-2025