J Biol Phys. 2026 Jan 29;52(1):6. doi: 10.1007/s10867-026-09701-4.
ABSTRACT
Regulatory T cells (Treg) and T helper 17 cells (Th17), both derived from naïve T cells, play pivotal roles in modulating immune responses, and their dynamic balance is critical for maintaining immune homeostasis. Existing studies predominantly focus on the regulatory mechanisms of individual cell types and lack a systematic analysis of how multiparametric interactions and stochastic perturbations jointly influence cell-fate equilibrium. In this study, we investigate the gene regulatory network of Treg and Th17 cells in two major aspects: (i) elucidating the dynamical features of the network and (ii) examining the regulatory effects of Gaussian white noise on the balance between the two lineages. By integrating systems dynamics, non-equilibrium mechanics, and stochastic process theory, we propose a unified modeling framework that incorporates Gaussian white noise to simulate stochastic perturbations in gene expression, thereby establishing a mapping between parameter sets and cellular phenotypes and quantifying the regulatory weights of key factors. Our results demonstrate that parameters such as extracellular TGF-β input, foxp3 mRNA synthesis rate, and Stat3 protein degradation rate significantly modulate the differentiation balance between Treg and Th17 cells. Furthermore, within a certain range, stronger Gaussian white noise promotes the differentiation of naïve T cells toward the Th17 lineage, thereby enhancing immune responsiveness. This finding aligns with prior experimental evidence demonstrating that stochastic noise can amplify immune response efficacy. This framework uniquely couples static and dynamic perturbations, revealing stochasticity’s role in cell-fate decisions and offering both a quantitative tool for studying Th17-Treg balance and a generalizable approach for other differentiation systems.
PMID:41606283 | DOI:10.1007/s10867-026-09701-4
