Ebenhöh Oliver, Ebeling Josha, Meyer Ronja, Pohlkotte Fabian, Nies Tim
Institute of Quantitative and Theoretical Biology, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany.
Cluster of Excellence on Plant Sciences, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany.
Life (Basel). 2024 Feb 9;14(2):247. doi: 10.3390/life14020247.
The biotechnological exploitation of microorganisms enables the use of metabolism for the production of economically valuable substances, such as drugs or food. It is, thus, unsurprising that the investigation of microbial metabolism and its regulation has been an active research field for many decades. As a result, several theories and techniques were developed that allow for the prediction of metabolic fluxes and yields as biotechnologically relevant output parameters. One important approach is to derive macrochemical equations that describe the overall metabolic conversion of an organism and basically treat microbial metabolism as a black box. The opposite approach is to include all known metabolic reactions of an organism to assemble a genome-scale metabolic model. Interestingly, both approaches are rather successful at characterizing and predicting the expected product yield. Over the years, macrochemical equations especially have been extensively characterized in terms of their thermodynamic properties. However, a common challenge when characterizing microbial metabolism by a single equation is to split this equation into two, describing the two modes of metabolism, anabolism and catabolism. Here, we present strategies to systematically identify separate equations for anabolism and catabolism. Based on metabolic models, we systematically identify all theoretically possible catabolic routes and determine their thermodynamic efficiency. We then show how anabolic routes can be derived, and we use these to approximate biomass yield. Finally, we challenge the view of metabolism as a linear energy converter, in which the free energy gradient of catabolism drives the anabolic reactions.
微生物的生物技术开发使得利用新陈代谢来生产具有经济价值的物质成为可能,比如药物或食物。因此,几十年来,对微生物新陈代谢及其调控的研究一直是一个活跃的研究领域,这并不奇怪。结果,人们开发了几种理论和技术,可用于预测作为生物技术相关输出参数的代谢通量和产量。一种重要的方法是推导宏观化学方程式,这些方程式描述了生物体的整体代谢转化过程,并且基本上将微生物新陈代谢视为一个黑箱。相反的方法是纳入生物体所有已知的代谢反应,以构建一个基因组规模的代谢模型。有趣的是,这两种方法在表征和预测预期产物产量方面都相当成功。多年来,宏观化学方程式尤其在其热力学性质方面得到了广泛的表征。然而,用一个单一方程式来表征微生物新陈代谢时,一个常见的挑战是将这个方程式拆分为两个,分别描述新陈代谢的两种模式,即合成代谢和分解代谢。在此,我们提出了系统地识别合成代谢和分解代谢各自方程式的策略。基于代谢模型,我们系统地识别出所有理论上可能的分解代谢途径,并确定它们的热力学效率。然后,我们展示如何推导合成代谢途径,并利用这些途径来估算生物量产量。最后,我们对将新陈代谢视为线性能量转换器的观点提出质疑,在这种观点中,分解代谢的自由能梯度驱动着合成代谢反应。