Metabolic reaction rates (fluxes) play a crucial role in comprehending cellular phenotypes and are essential in areas such as metabolic engineering, biotechnology, and biomedical research. The state-of-the-art technique for estimating fluxes is metabolic flux analysis using isotopic labelling (13C-MFA), which uses a dataset-model combination to determine the fluxes. Bayesian statistical methods are gaining popularity in the field of life sciences, but the use of 13C-MFA is still dominated by conventional best-fit approaches. The slow take-up of Bayesian approaches is, at least partly, due to the unfamiliarity of Bayesian methods to metabolic engineering researchers. To address this unfamiliarity, we here outline similarities and differences between the two approaches and highlight particular advantages of the Bayesian way of flux analysis. With a real-life example, re-analysing a moderately informative labelling dataset of E. coli, we identify situations in which Bayesian methods are advantageous and more informative, pointing to potential pitfalls of current 13C-MFA evaluation approaches. We propose the use of Bayesian model averaging (BMA) for flux inference as a means of overcoming the problem of model uncertainty through its tendency to assign low probabilities to both, models that are unsupported by data, and models that are overly complex. In this capacity, BMA resembles a tempered Ockham's razor. With the tempered razor as a guide, BMA-based 13C-MFA alleviates the problem of model selection uncertainty and is thereby capable of becoming a game changer for metabolic engineering by uncovering new insights and inspiring novel approaches.

中文翻译：

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重新思考 13C 代谢通量分析 – 通量推断的贝叶斯方式


代谢反应速率（通量）在理解细胞表型方面起着至关重要的作用，在代谢工程、生物技术和生物医学研究等领域至关重要。估计通量的最新技术是使用同位素标记 （13C-MFA） 的代谢通量分析，它使用数据集-模型组合来确定通量。贝叶斯统计方法在生命科学领域越来越受欢迎，但 13C-MFA 的使用仍然以传统的最佳拟合方法为主。贝叶斯方法的缓慢采用至少部分是由于代谢工程研究人员对贝叶斯方法的不熟悉。为了解决这种不熟悉的问题，我们在这里概述了两种方法之间的异同，并强调了贝叶斯通量分析方法的特殊优势。通过一个真实的例子，重新分析大肠杆菌的中等信息量标记数据集，我们确定了贝叶斯方法有利且信息量更大的情况，指出了当前 13C-MFA 评估方法的潜在陷阱。我们建议使用贝叶斯模型平均 （BMA） 进行通量推断，作为克服模型不确定性问题的一种手段，因为它倾向于为没有数据支持的模型和过于复杂的模型分配低概率。在这个能力上，BMA 就像一把经过回火的奥卡姆剃须刀。以回火剃须刀为指导，基于 BMA 的 13C-MFA 缓解了模型选择不确定性的问题，从而能够通过发现新见解和激发新方法成为代谢工程的游戏规则改变者。