Kang Chris, McElroy Madelynn, Voulgarakis Nikolaos K
Department of Mathematics and Statistics, Washington State University, Pullman, WA 99164, USA.
Voiland School of Chemical Engineering and Bioengineering, Washington State University, Pullman, WA 99164, USA.
Entropy (Basel). 2023 Jan 27;25(2):235. doi: 10.3390/e25020235.
Early embryonic development involves forming all specialized cells from a fluid-like mass of identical stem cells. The differentiation process consists of a series of symmetry-breaking events, starting from a high-symmetry state (stem cells) to a low-symmetry state (specialized cells). This scenario closely resembles phase transitions in statistical mechanics. To theoretically study this hypothesis, we model embryonic stem cell (ESC) populations through a coupled Boolean network (BN) model. The interaction is applied using a multilayer Ising model that considers paracrine and autocrine signaling, along with external interventions. It is demonstrated that cell-to-cell variability can be interpreted as a mixture of steady-state probability distributions. Simulations have revealed that such models can undergo a series of first- and second-order phase transitions as a function of the system parameters that describe gene expression noise and interaction strengths. These phase transitions result in spontaneous symmetry-breaking events that generate new types of cells characterized by various steady-state distributions. Coupled BNs have also been shown to self-organize in states that allow spontaneous cell differentiation.
早期胚胎发育涉及从一团类似液体的相同干细胞形成所有特化细胞。分化过程由一系列打破对称性的事件组成，从高对称状态（干细胞）开始，到低对称状态（特化细胞）。这种情况与统计力学中的相变非常相似。为了从理论上研究这一假设，我们通过耦合布尔网络（BN）模型对胚胎干细胞（ESC）群体进行建模。相互作用通过多层伊辛模型来应用，该模型考虑了旁分泌和自分泌信号以及外部干预。结果表明，细胞间的变异性可以解释为稳态概率分布的混合。模拟结果显示，此类模型可以根据描述基因表达噪声和相互作用强度的系统参数经历一系列一阶和二阶相变。这些相变导致自发的对称性破缺事件，产生以各种稳态分布为特征的新型细胞。耦合布尔网络也已被证明能自组织成允许自发细胞分化的状态。
