Biosystems and Biomaterials Division, National Institute of Standards and Technology, Gaithersburg, MD, United States of America.
PLoS One. 2020 Mar 11;15(3):e0230076. doi: 10.1371/journal.pone.0230076. eCollection 2020.
The steady state distributions of phenotypic responses within an isogenic population of cells result from both deterministic and stochastic characteristics of biochemical networks. A biochemical network can be characterized by a multidimensional potential landscape based on the distribution of responses and a diffusion matrix of the correlated dynamic fluctuations between N-numbers of intracellular network variables. In this work, we develop a thermodynamic description of biological networks at the level of microscopic interactions between network variables. The Boltzmann H-function defines the rate of free energy dissipation of a network system and provides a framework for determining the heat associated with the nonequilibrium steady state and its network components. The magnitudes of the landscape gradients and the dynamic correlated fluctuations of network variables are experimentally accessible. We describe the use of Fokker-Planck dynamics to calculate housekeeping heat from the experimental data by a method that we refer to as Thermo-FP. The method provides insight into the composition of the network and the relative thermodynamic contributions from network components. We surmise that these thermodynamic quantities allow determination of the relative importance of network components to overall network control. We conjecture that there is an upper limit to the rate of dissipative heat produced by a biological system that is associated with system size or modularity, and we show that the dissipative heat has a lower bound.
同一种细胞系的表型反应的稳态分布是由生化网络的确定性和随机性特征共同决定的。基于反应分布和 N 个细胞内网络变量之间相关动态波动的扩散矩阵,可以用多维势能景观来描述生化网络。在这项工作中,我们在网络变量之间的微观相互作用层面上,对生物网络进行了热力学描述。玻尔兹曼 H 函数定义了网络系统的自由能耗散率,并为确定与非平衡稳态及其网络组成部分相关的热量提供了一个框架。景观梯度和网络变量的动态相关波动的幅度是可以通过实验获得的。我们描述了如何使用福克-普朗克动力学,通过我们称之为 Thermo-FP 的方法,从实验数据中计算出维持生命所需的热量。该方法深入了解了网络的组成以及网络组件的相对热力学贡献。我们推测,这些热力学量可以确定网络组件对整体网络控制的相对重要性。我们推测,生物系统产生的耗散热量存在一个上限,这个上限与系统的大小或模块化有关,并且我们还表明,耗散热量存在一个下限。