贝叶斯流:贝叶斯的方法来量化代谢流和基因组规模的不确定性
Tyler W H Backman1,2, Christina Schenk1,3,4, Tijana Radivojevic1,2,4
1Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America.
PLoS computational biology
|November 10, 2023
概括
贝叶斯推理 (BayFlux) 通过采样可能流量的完整分布,量化不确定性来改进代谢流量分析. 这种方法揭示了基因组规模模型提供比传统核心模型更精确的流量数据.
科学领域:
- 系统生物学 系统生物学
- 代谢工程是代谢工程.
- 计算生物学 计算生物学
背景情况:
- 代谢流对于理解细胞功能至关重要.
- 13C代谢流量分析 (13C MFA) 是测量这些流量的黄金标准.
- 传统的13C MFA方法与非线性拟合和不确定性量化作斗争.
研究的目的:
- 开发一个贝叶斯推理方法 (BayFlux) 进行全面的代谢流量空间采样.
- 准确量化代谢流量计算中的不确定性.
- 为了比较基因组规模模型与小核心模型的流量分布.
主要方法:
- 使用贝叶斯推理进行流量空间采样 (BayFlux).
- 应用13C标记实验和细胞外交换流.
- 开发了P-13C MOMA和P-13C ROOM用于基因淘汰预测与不确定性量化.
主要成果:
- 湾流 (BayFlux) 识别了与实验数据相容的流的完整分布.
- 与核心模型相比,基因组规模模型产生更窄的流量分布 (减少不确定性).
- 新的方法通过量化不确定性来改善基因淘汰预测.
结论:
- BayFlux为代谢流量分析和不确定性量化提供了一个强大的方法.
- 模型的完整性显著影响13C MFA结果,需要谨慎.
- 新的预测方法增强了代谢模型的生物见解.
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