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场景，均需在大规模生物反应器中培养微生物。由于混合不完全，这类环境通常存在物理化学梯度，这也是工业中细胞工厂生产性能下降的诱因之一。本研究借助实验与建模手段，旨在解析并阐明底物反复波动对大肠杆菌（Escherichia coli）K12 MG1655细胞行为的影响。本研究产生的数据集，表征了在1.2升实验室生物反应器中开展的大肠杆菌（E.coli）培养实验，涉及两种不同生长条件：碳限制稳态培养与底物梯度反复循环培养。所获数据涵盖：生理速率、定量代谢物浓度、代谢流平衡分析结果、¹³C标记富集代谢物谱、鸟枪蛋白质组学数据，以及动态动力学建模方法。