贝叶斯优化用于设计多级生物电路的贝叶斯优化
Charlotte Merzbacher1, Oisin Mac Aodha1,2, Diego A Oyarzún1,2,3
1School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, U.K.
ACS synthetic biology
|June 20, 2023
概括
本研究介绍了一种机器学习方法,使用贝叶斯优化来设计合成生物学电路. 它有效地优化了多个规模的复杂生物系统,加速了发现,提高了强度.
科学领域:
- 合成生物学 合成生物学
- 计算生物学 计算生物学
- 生物技术是生物技术.
背景情况:
- 合成生物学使得复杂的分子电路在细胞尺度上的构建成为可能.
- 目前的计算优化方法由于模拟刚性而与多尺度生物系统作斗争.
- 复杂的基因调节,信号和代谢途径需要有效的设计工具.
研究的目的:
- 开发一种高效的机器学习方法,在多个尺度上优化合成生物电路.
- 解决当前计算方法在处理多尺度生物系统中的局限性.
- 为了实现电路架构和参数的联合优化,以改进生物系统设计.
主要方法:
- 利用贝叶斯优化,这是一种优化深度神经网络的机器学习技术.
- 将该方法应用于控制生物合成途径的基因电路,具有非线性和多个尺度.
- 开发了一个在混合整数输入空间中导航非凸的优化问题的策略.
主要成果:
- 在多个时间和度尺度上证明了生物循环的高效优化.
- 成功处理了大型的,多层次的生物系统设计问题.
- 启用参数扫描,通过in silico选来评估电路对干扰的强度.
结论:
- 开发的机器学习方法为设计复杂的合成生物学电路提供了一种可行的方法.
- 这种策略有效地优化了多尺度生物系统,克服了传统模拟方法的局限性.
- 该方法是有效的in silico选工具,加速合成生物学设计的实验实施.
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