一个可微分的吉尔斯皮算法,用于模拟化学动力学,参数估计和设计合成生物电路
1Department of Physics, Boston University, Boston, Massachusetts 02215, USA.
bioRxiv : the preprint server for biology
|July 19, 2024
概括
我们介绍了使用深度学习分析复杂化学反应的可微分吉尔斯皮算法 (DGA). 这种方法准确地学习动力学参数,并为系统生物学应用设计生化网络.
科学领域:
- 系统生物学 系统生物学
- 计算化学的计算化学
- 机器学习 机器学习
背景情况:
- 吉尔斯皮算法是模拟化学反应网络的标准.
- 深度学习为分析复杂系统提供了新的方法.
- 目前的方法缺乏基于梯度的优化差分能力.
研究的目的:
- 开发吉尔斯皮算法 (DGA) 的可微分变体.
- 为了实现基于梯度的动力参数和网络设计的学习.
- 将DGA应用于系统和合成生物学中的随机模型.
主要方法:
- 利用深度学习来用平滑函数对吉尔斯皮算法的不连续运算进行近似计算.
- 实施反向传播用于梯度计算.
- 将DGA应用于随机基因促进器模型.
主要成果:
- 成功地从实验mRNA表达数据中学习了大肠杆菌*促进体的运动参数.
- 设计了具有特定输入-输出关系的不平衡促进器架构.
- 证明了DGA在参数学习和网络设计中的准确性和速度.
结论:
- 可微分的吉尔斯皮算法 (DGA) 为分析随机化学动力学提供了一个强大的新工具.
- DGA促进了基于梯度的优化,用于参数推断和合成生物学设计.
- 这种方法在系统生物学和相关领域具有广泛的应用.
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