生成型机器学习产生动态模型,准确地描述细胞内代谢状态
Subham Choudhury1, Bharath Narayanan1,2, Michael Moret1,3
1Laboratory of Computational Systems Biology, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
概括
我们开发了RENAISSANCE,这是一种机器学习框架,用于创建细胞代谢的精确动态模型. 该工具整合了omics数据以确定代谢状态和估计运动参数,推进代谢研究.
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:
- 欧米克数据集提供了细胞洞察力,但破译代谢状态是具有挑战性的.
- 动力模型整合了欧米克数据,但参数化是一个主要障碍.
- 准确的运动模型对于理解细胞生理学和新陈代谢至关重要.
研究的目的:
- 为了介绍RENAISSANCE,一个用于高效动力模型参数化的生成机器学习框架.
- 为了使用各种数据准确地描述细胞内代谢状态.
- 为了减少参数不确定性和提高大规模代谢模型的准确性.
主要方法:
- 开发了一个生成型机器学习框架 (RENAISSANCE).
- 整合了各种各样的omics数据,介质组成,物理化学数据和专家知识.
- 应用框架来模拟大肠杆菌中的代谢状态.
主要成果:
- 文艺复兴高效参数化大规模运动模型.
- 该框架准确地描述了细胞内代谢状态.
- 它估计了缺失的动力参数,并协调了稀疏的实验数据,减少了不确定性并提高了准确性.
结论:
- RENAISSANCE准确地模拟细胞代谢状态,并估计运动参数.
- 该框架整合了多学科数据和专家知识,用于强大的代谢建模.
- 这种工具将促进对健康和生物技术中的代谢变异的研究.
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