Mitigating biomass composition uncertainties in flux balance analysis using ensemble representations.
Mitigating biomass composition uncertainties in flux balance analysis using ensemble representations.
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DOI：
10.1016/j.csbj.2023.07.025
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发表时间：
2023
影响因子：
6
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中科院分区：
文献类型：
--
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生物量方程是基因组尺度代谢模型（GEMs）的关键组成部分：它被用作通量平衡分析（FBA）中事实上的目标函数。该方程根据在特定条件下测量的大分子和单体组成，说明了细胞生长所需的所有已知生物质前体的数量。然而，经常有报道称，细胞的大分子组成可能在不同的环境条件下发生变化，因此在多种条件下，在FBA中使用相同的单一生物量方程是值得怀疑的。本文首先研究了大肠杆菌、酿酒酵母和灰环虫三种代表性宿主生物在不同环境/遗传变异下大分子组成的定性和定量变化。虽然RNA、蛋白质和脂质组成等大分子构建块变化显著，但核苷酸和氨基酸等基本生物质单体单位的变化并不明显。我们还观察到，通过FBA预测通量对大分子组成非常敏感，而对单体组成则不敏感。基于这些观察，我们提出了生物量方程在FBA中的集合表示，以解释细胞成分的自然变化。生物量的这种集合表示更好地预测了通过合成代谢反应的通量，因为它允许细胞的生物合成需求的灵活性。目前的研究清楚地强调了生物量方程的某些组成部分确实在不同的条件下变化，并且通过考虑这种自然变化，生物量方程在FBA中的集合表示可以避免可能由计算机模拟产生的不准确性。评估了三种模式物种大分子组成的自然变化。进行敏感性分析，探讨生物量组成对FBA的影响。鉴定出在表型预测中最敏感的蛋白质和脂质。提出了一种新的方法FBAwEB来减轻生物量方程的不确定性。
The biomass equation is a critical component in genome-scale metabolic models (GEMs): it is used as the de facto objective function in flux balance analysis (FBA). This equation accounts for the quantities of all known biomass precursors that are required for cell growth based on the macromolecular and monomer compositions measured at certain conditions. However, it is often reported that the macromolecular composition of cells could change across different environmental conditions and thus the use of the same single biomass equation in FBA, under multiple conditions, is questionable. Herein, we first investigated the qualitative and quantitative variations of macromolecular compositions of three representative host organisms, Escherichia coli, Saccharomyces cerevisiae and Cricetulus griseus, across different environmental/genetic variations. While macromolecular building blocks such as RNA, protein, and lipid composition vary notably, changes in fundamental biomass monomer units such as nucleotides and amino acids are not appreciable. We also observed that flux predictions through FBA is quite sensitive to macromolecular compositions but not the monomer compositions. Based on these observations, we propose ensemble representations of biomass equation in FBA to account for the natural variation of cellular constituents. Such ensemble representations of biomass better predicted the flux through anabolic reactions as it allows for the flexibility in the biosynthetic demands of the cells. The current study clearly highlights that certain component of the biomass equation indeed vary across different conditions, and the ensemble representation of biomass equation in FBA by accounting for such natural variations could avoid inaccuracies that may arise from in silico simulations. Evaluated natural variations in macromolecular compositions in three model species. Conducted sensitivity analysis to explore impact of biomass compositions on FBA. Identified proteins and lipids to be most sensitive in phenotype predictions. Proposed new approach FBAwEB to mitigate the uncertainty in biomass equations.
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