整合一个定制的循环神经网络与贝叶斯实验设计,以优化微生物社区功能
Jaron C Thompson1,2, Victor M Zavala1, Ophelia S Venturelli1,2,3
1Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, Wisconsin, United States of America.
PLoS computational biology
|September 29, 2023
概括
我们开发了一个新的机器学习模型来预测微生物组的功能. 这种物理约束的循环神经网络通过指导实验以改善代谢物生产和降解来优化微生物社区工程.
科学领域:
- 微生物组研究的研究.
- 计算生物学是一种计算生物学.
- 合成生物学 合成生物学
背景情况:
- 微生物组执行有价值的功能,如代谢物生产和降解,对健康,农业和环境应用至关重要.
- 工程微生物群落需要准确的计算模型来预测物种相互作用和环境因素.
- 由于复杂的相互作用和数据驱动的方法,现有的模型面临挑战,这可能会产生不切实际的预测.
研究的目的:
- 为优化微生物组功能开发一种新的计算框架.
- 创建一个物理约束的机器学习模型,确保实现现实的预测.
- 设计一种实验策略,有效指导针对特定微生物功能的数据收集.
主要方法:
- 开发一个物理约束的循环神经网络 (RNN) 模型.
- 一个闭环的整合,贝叶斯的实验设计算法.
- 在生物反应器案例研究中应用框架,以优化运行条件.
主要成果:
- 物理限制的RNN在预测物种丰度和代谢物度方面超过了现有的机器学习方法.
- 贝叶斯的实验设计算法有效地导航了一个大的设计空间,以确定最佳的操作条件.
- 该框架证明了针对性微生物社区功能的成功优化.
结论:
- 拟议的物理约束RNN为建模复杂微生物组动态提供了灵活而准确的方法.
- 综合贝叶斯实验设计加速了微生物社区功能的优化.
- 这种方法为推进微生物社区工程及其应用提供了一个强大的工具.
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