Li Sheng, Liu Qiong, Wang Erkang, Wang Jin
State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, Jilin 130022, China.
Department of Chemistry and of Physics and astronomy, State University of New York at Stony Brook, Stony Brook, New York 11794-3400, USA.
Biophys Rev (Melville). 2023 Sep 13;4(3):031401. doi: 10.1063/5.0157759. eCollection 2023 Sep.
Cellular responses to pheromone in yeast can range from gene expression to morphological and physiological changes. While signaling pathways are well studied, the cell fate decision-making during cellular polar growth is still unclear. Quantifying these cellular behaviors and revealing the underlying physical mechanism remain a significant challenge. Here, we employed a hidden Markov chain model to quantify the dynamics of cellular morphological systems based on our experimentally observed time series. The resulting statistics generated a stability landscape for state attractors. By quantifying rotational fluxes as the non-equilibrium driving force that tends to disrupt the current attractor state, the dynamical origin of non-equilibrium phase transition from four cell morphological fates to a single dominant fate was identified. We revealed that higher chemical voltage differences induced by a high dose of pheromone resulted in higher chemical currents, which will trigger a greater net input and, thus, more degrees of the detailed balance breaking. By quantifying the thermodynamic cost of maintaining morphological state stability, we demonstrated that the flux-related entropy production rate provides a thermodynamic origin for the phase transition in non-equilibrium morphologies. Furthermore, we confirmed that the time irreversibility in time series provides a practical way to predict the non-equilibrium phase transition.
酵母细胞对信息素的反应范围可以从基因表达到形态和生理变化。虽然信号通路已得到充分研究，但细胞在极性生长过程中的命运决策仍不清楚。量化这些细胞行为并揭示其潜在的物理机制仍然是一项重大挑战。在这里，我们基于实验观察到的时间序列，采用隐马尔可夫链模型来量化细胞形态系统的动力学。由此产生的统计数据为状态吸引子生成了一个稳定性景观。通过将旋转通量量化为倾向于破坏当前吸引子状态的非平衡驱动力，确定了从四种细胞形态命运到单一主导命运的非平衡相变的动力学起源。我们发现，高剂量信息素诱导的更高化学电压差会导致更高的化学电流，这将触发更大的净输入，从而导致更多程度的详细平衡破坏。通过量化维持形态状态稳定性的热力学成本，我们证明了与通量相关的熵产生率为非平衡形态中的相变提供了热力学起源。此外，我们证实时间序列中的时间不可逆性为预测非平衡相变提供了一种实用方法。
