FBA-PRCC. 基于约束的模型中偏秩相关系数 (PRCC) 全局敏感性分析 (GSA) 的应用。
FBA-PRCC. Partial Rank Correlation Coefficient (PRCC) Global Sensitivity Analysis (GSA) in Application to Constraint-Based Models.
机构信息
Okinawa Institute of Science and Technology Graduate University, Okinawa 904-0495, Japan.
Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China.
出版信息
Biomolecules. 2023 Mar 9;13(3):500. doi: 10.3390/biom13030500.
BACKGROUND
Whole-genome models (GEMs) have become a versatile tool for systems biology, biotechnology, and medicine. GEMs created by automatic and semi-automatic approaches contain a lot of redundant reactions. At the same time, the nonlinearity of the model makes it difficult to evaluate the significance of the reaction for cell growth or metabolite production.
METHODS
We propose a new way to apply the global sensitivity analysis (GSA) to GEMs in a straightforward parallelizable fashion.
RESULTS
We have shown that Partial Rank Correlation Coefficient (PRCC) captures key steps in the metabolic network despite the network distance from the product synthesis reaction.
CONCLUSIONS
FBA-PRCC is a fast, interpretable, and reliable metric to identify the sign and magnitude of the reaction contribution to various cellular functions.
背景
全基因组模型（GEM）已经成为系统生物学、生物技术和医学的通用工具。通过自动和半自动方法创建的 GEM 包含大量冗余反应。同时，模型的非线性使得难以评估反应对细胞生长或代谢产物产生的意义。
方法
我们提出了一种新的方法，可以直接并行地将全局敏感性分析（GSA）应用于 GEM。
结果
我们表明，偏秩相关系数（PRCC）尽管与产物合成反应的网络距离较远，但仍能捕捉到代谢网络中的关键步骤。
结论
FBA-PRCC 是一种快速、可解释和可靠的指标，可以识别反应对各种细胞功能的贡献的符号和大小。
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