Data of the PhD thesis "Escherichia coli metabolism under dynamic conditions: The tales of substrate hunting"

Dynamic environmental conditions govern microbial metabolism and affect cellular growth. Many applications in biotechnology require cultivating microorganisms in large-scale bioreactors. These environments are commonly characterized by physicochemical gradients, due to imperfect mixing and have been the cause of reduced performance of cell factories in industry. The aim of this thesis was to unravel and understand the effects of repetitive substrate fluctuations on the cellular behaviour of Escherichia coli K12 MG1655, using experimental and modelling approaches. The datasets derived from this research characterize E.coli cultivations (performed in 1.2 L lab bioreactors) under two different growth conditions: carbon-limited steady-state and repetitive cycles of substrate gradients. The data include: physiological rates, quantitative metabolite concentrations, metabolic flux balance analysis, 13C labelling enrichment metabolite profiles, shot-gun proteomic data, as well as dynamic kinetic modelling approaches.

动态环境条件可调控微生物代谢，并影响细胞生长。生物技术领域的诸多应用均需在大规模生物反应器中培养微生物。受限于混合不完全的工况，这类环境普遍存在理化梯度，而这正是工业生产中细胞工厂性能衰减的核心诱因。本研究旨在通过实验与建模手段，解析并阐明底物重复波动对大肠杆菌（Escherichia coli）K12 MG1655细胞行为的影响。本研究产出的数据集表征了在1.2升实验室规模生物反应器中开展的大肠杆菌培养实验，涵盖两种不同生长条件：碳限制稳态培养与底物梯度重复循环培养。数据集包含以下内容：生理速率参数、定量代谢物浓度数据、代谢流平衡分析（metabolic flux balance analysis）结果、13C标记富集代谢物谱、鸟枪法蛋白质组学（shot-gun proteomic）数据，以及动态动力学建模（dynamic kinetic modelling）分析结果。