Metabolic fluxes and their control mechanisms are fundamental in cellular metabolism, offering insights for the study of biological systems and biotechnological applications. However, quantitative and predictive understanding of controlling biochemical reactions in microbial cell factories, especially at the system level, is limited. In this work, we present ARCTICA, a computational framework that integrates constraint-based modelling with machine learning tools to address this challenge. Using the model cyanobacterium sp. PCC 6803 as chassis, we demonstrate that ARCTICA effectively simulates global-scale metabolic flux control. Key findings are that (i) the photosynthetic bioproduction is mainly governed by enzymes within the Calvin–Benson–Bassham (CBB) cycle, rather than by those involve in the biosynthesis of the end-product, (ii) the catalytic capacity of the CBB cycle limits the photosynthetic activity and downstream pathways and (iii) ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO) is a major, but not the most, limiting step within the CBB cycle. Predicted metabolic reactions qualitatively align with prior experimental observations, validating our modelling approach. ARCTICA serves as a valuable pipeline for understanding cellular physiology and predicting rate-limiting steps in genome-scale metabolic networks, and thus provides guidance for bioengineering of cyanobacteria.

中文翻译：

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机器学习预测蓝藻的全系统代谢通量控制

代谢通量及其控制机制是细胞代谢的基础，为生物系统和生物技术应用的研究提供了见解。然而，对控制微生物细胞工厂生化反应的定量和预测性理解，特别是在系统水平上，是有限的。在这项工作中，我们提出了 ARCTICA，这是一个计算框架，它将基于约束的建模与机器学习工具相集成，以应对这一挑战。使用模型蓝细菌。以 PCC 6803 作为底盘，我们证明 ARCTICA 有效地模拟了全球范围的代谢通量控制。主要发现是（i）光合生物生产主要由卡尔文-本森-巴沙姆（CBB）循环中的酶控制，而不是由参与最终产物生物合成的酶控制，（ii）CBB 的催化能力CBB 循环限制了光合活性和下游途径，并且 (iii) 1,5-二磷酸核酮糖羧化酶/加氧酶 (RuBisCO) 是 CBB 循环中的主要但不是最重要的限制步骤。预测的代谢反应与之前的实验观察定性地一致，验证了我们的建模方法。ARCTICA 是了解细胞生理学和预测基因组规模代谢网络中限速步骤的宝贵管道，从而为蓝细菌的生物工程提供指导。