贝叶斯式SSA:基于结构敏感性分析的贝叶斯式统计模型,用于预测代谢网络中对酶扰动的反应
Shion Hosoda1, Hisashi Iwata2, Takuya Miura2
1Center for Exploratory Research, Research and Development Group, Hitachi, Ltd., Kokubunji-shi, Tokyo, 185-8601, Japan. shion.hosoda.hd@hitachi.com.
BMC bioinformatics
|September 10, 2024
概括
贝叶斯式SSA是一种新的模型,通过将环境数据与结构灵敏度分析 (SSA) 整合起来,改善了化学生物生产中代谢变化的预测. 这有助于设计高效的生物工艺.
科学领域:
- 生物技术和代谢工程 生物技术和代谢工程
- 计算生物学和系统生物学
背景情况:
- 化学生物生产对于脱碳社会至关重要.
- 预测由酶扰动引起的代谢流量变化对于计算设计至关重要.
- 现有的结构敏感性分析 (SSA) 方法可能缺乏足够的网络信息来进行明确的预测.
研究的目的:
- 开发一种新的方法,贝叶斯SSA,将环境信息集成到SSA预测中.
- 通过结合经验数据来解决传统SSA的局限性.
- 提高预测生物生产中对酶干扰的系统反应的准确性.
主要方法:
- 开发了贝叶斯式SSA,这是一个基于SSA的贝叶斯式统计模型.
- 从扰动数据集中提取环境信息.
- 将这些环境信息集成到SSA预测中.
主要成果:
- 成功地将贝叶斯式SSA应用于Escherichia coli中央代谢途径的合成和真实数据集.
- 证明贝叶斯式SSA有效地将环境信息集成到SSA预测中.
- 表明贝叶斯式SSA的后部分布与已知的途径保持一致,增强了简单的出口流.
结论:
- 贝叶斯式SSA增强了生物生产中代谢流变化的预测.
- 预计这一进步将加速化学生物生产过程.
- 贝叶斯学SSA有助于代谢工程和合成生物学的进步.
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