arXivDaily arXiv每日学术速递 周一至周五更新
2606.16803 2026-06-19 q-bio.MN q-bio.SC 交叉投稿

Cell Division Changes Fate Decisions in a Genetic Toggle Switch

细胞分裂改变遗传开关中的命运决定

Charli Austin, Nikola Popovic, Ramon Grima

AI总结 本研究通过分析布尔型遗传开关模型,发现细胞分裂可将相同初始条件的轨迹导向不同稳定态,并定义了忽略分裂时命运预测错误的区域,表明分裂可重塑多稳态调控网络的命运边界。

Comments 16 pages;7 figures. Includes new Figure A.2 comparing the separatrices of the classical and Boolean toggle switches, with and without cell division. Two Appendices (previously H and I in the previous version) integrated into Appendix E for clarity

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AI中文摘要

基因调控网络通过多稳态动力学控制细胞命运决定。遗传开关是此类行为的经典模型;然而,细胞分裂对其动力学的影响仍知之甚少。我们推导了有无分裂的简化布尔型开关的解析分界线。我们证明,分裂可以将具有相同初始条件的轨迹重定向到相反的稳定态,并定义了一个不一致区域,在该区域中,如果忽略分裂,则命运预测错误。我们的结果表明,分裂可以从根本上重塑多稳态调控网络中的命运边界。

英文摘要

Gene regulatory networks govern cellular fate decisions through multistable dynamics. The genetic toggle switch is a canonical model of such behaviour; yet, the impact of cell division on its dynamics remains poorly understood. We derive analytical separatrices for a simplified Boolean toggle switch with and without division. We show that division can redirect trajectories with identical initial conditions to opposing stable states, and we define a region of disagreement where fate decisions are predicted incorrectly if division is neglected. Our results imply that division can fundamentally reshape fate boundaries in multistable regulatory networks.

2512.02908 2026-06-19 q-bio.MN q-bio.QM q-bio.SC 版本更新

Imperfect molecular detection can renormalize apparent kinetic rates in stochastic gene regulatory networks

不完美的分子检测可以重整化随机基因调控网络中的表观动力学速率

Iryna Zabaikina, Ramon Grima

AI总结 研究不完美分子检测对基因调控网络随机动力学的影响,发现捕获效应在某些条件下可重整化动力学速率,为解释噪声单细胞测量提供系统基础。

Comments 28 pages, 6 figures. Changes include Table I, demonstrating accurate renormalization even for mean protein copy numbers of only a few tens of molecules, and Fig. 6, summarizing all models, reaction schemes, assumptions, rate rescalings, and validity regimes. The conclusion was expanded to discuss practical applications

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AI中文摘要

单细胞实验中的不完美分子检测引入了技术噪声,掩盖了基因调控网络的真实随机动力学。虽然分子捕获的二项模型提供了不完美检测的原理性描述,但迄今为止仅针对未明确考虑调控的简单基因表达模型进行了分析。在这里,我们将捕获的二项模型扩展到一般基因调控网络,以理解不完美捕获如何重塑观察到的分子计数的时间相关统计量。我们的结果揭示了捕获效应何时对应于一部分动力学速率的重整化,以及何时不能被吸收为有效速率,从而为解释有噪声的单细胞测量提供了系统基础。特别地,我们表明速率重整化取决于模型中调控细节的水平。对于基于启动子状态转换的隐式调控模型,只要基因产物合成不触发启动子状态变化(例如没有启动子近端暂停或暂停短暂),就会发生重整化。对于具有显式转录因子结合的模型,同样的条件成立,同时需要足够高的转录因子丰度,实际上每个细胞只需几十个分子。在这些情况下,技术噪声降低了合成基因产物的表观平均爆发大小,并加速了转录因子结合反应的表观速率。这种加速随着参与启动子转换的蛋白质种类和/或分子数量的增加而增强。这些效应对任意连接性的基因调控网络都成立,并且在时间依赖的动力学速率下仍然有效。

英文摘要

Imperfect molecular detection in single-cell experiments introduces technical noise that obscures the true stochastic dynamics of gene regulatory networks. While binomial models of molecular capture provide a principled description of imperfect detection, they have so far been analyzed only for simple gene-expression models that do not explicitly account for regulation. Here, we extend binomial models of capture to general gene regulatory networks to understand how imperfect capture reshapes the observed time-dependent statistics of molecular counts. Our results reveal when capture effects correspond to a renormalization of a subset of the kinetic rates and when they cannot be absorbed into effective rates, providing a systematic basis for interpreting noisy single-cell measurements. In particular, we show that rate renormalization depends on the level of regulatory detail in the model. For implicit regulatory models based on promoter state transitions, it arises whenever gene product synthesis does not trigger a promoter state change, as in the absence of promoter-proximal pausing or when pausing is short-lived. For models with explicit transcription factor binding, the same condition holds, together with sufficiently high transcription factor abundance, which in practice requires only a few tens of molecules per cell. In these cases, technical noise reduces the apparent mean burst size of synthesized gene products and accelerates the apparent rates of transcription factor binding reactions. This acceleration becomes stronger as the number of protein species and/or molecules involved in promoter switching increases. These effects hold for gene regulatory networks of arbitrary connectivity and remain valid under time-dependent kinetic rates.