Institute of Quantitative and Theoretical Biology, Heinrich-Heine-Universität Düsseldorf, Universitätsstraße 1, 40225 Düsseldorf, Germany.
Cluster of Excellence on Plant Sciences (CEPLAS), Heinrich-Heine-Universität Düsseldorf, Universitätsstraße 1, 40225 Düsseldorf, Germany.
Biochem Soc Trans. 2021 Aug 27;49(4):1663-1674. doi: 10.1042/BST20200977.
The application of thermodynamics to microbial growth has a long tradition that originated in the middle of the 20th century. This approach reflects the view that self-replication is a thermodynamic process that is not fundamentally different from mechanical thermodynamics. The key distinction is that a free energy gradient is not converted into mechanical (or any other form of) energy but rather into new biomass. As such, microbes can be viewed as energy converters that convert a part of the energy contained in environmental nutrients into chemical energy that drives self-replication. Before the advent of high-throughput sequencing technologies, only the most central metabolic pathways were known. However, precise measurement techniques allowed for the quantification of exchanged extracellular nutrients and heat of growing microbes with their environment. These data, together with the absence of knowledge of metabolic details, drove the development of so-called black-box models, which only consider the observable interactions of a cell with its environment and neglect all details of how exactly inputs are converted into outputs. Now, genome sequencing and genome-scale metabolic models (GEMs) provide us with unprecedented detail about metabolic processes inside the cell. However, mostly due to computational complexity issues, the derived modelling approaches make surprisingly little use of thermodynamic concepts. Here, we review classical black-box models and modern approaches that integrate thermodynamics into GEMs. We also illustrate how the description of microbial growth as an energy converter can help to understand and quantify the trade-off between microbial growth rate and yield.
热力学在微生物生长中的应用有着悠久的传统，可以追溯到 20 世纪中期。这种方法反映了这样一种观点，即自我复制是一个热力学过程，与机械热力学并没有根本的不同。关键的区别在于，自由能梯度不是转化为机械能（或任何其他形式的能量），而是转化为新的生物量。因此，可以将微生物视为能量转换器，将环境营养物质中包含的部分能量转化为驱动自我复制的化学能量。在高通量测序技术出现之前，人们只知道最核心的代谢途径。然而，精确的测量技术允许定量测量生长中的微生物与环境之间交换的外源性营养素和热量。这些数据，再加上对代谢细节一无所知，推动了所谓黑箱模型的发展，这种模型只考虑细胞与环境的可观察到的相互作用，而忽略了输入究竟是如何转化为输出的所有细节。现在，基因组测序和基因组规模代谢模型（GEMs）为我们提供了关于细胞内代谢过程的前所未有的详细信息。然而，主要由于计算复杂性问题，衍生的建模方法很少利用热力学概念。在这里，我们回顾了经典的黑箱模型和将热力学纳入 GEMs 的现代方法。我们还举例说明了将微生物生长描述为能量转换器如何有助于理解和量化微生物生长速率和产率之间的权衡。
