通过对基于物理模型的贝叶斯优化来逆向工程形态生成
Nilay Kumar1, Mayesha Sahir Mim1,2, Alexander Dowling1
1Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, IN, 46556, USA.
NPJ systems biology and applications
|May 7, 2024
概括
我们开发了一个贝叶斯优化框架,通过校准基于物理的模型来预测器官形状. 这种方法成功地推断出细胞力分布,有助于理解发育过程和疾病.
科学领域:
- 发展生物学 发展生物学
- 系统生物学 系统生物学
- 生物物理学的生物物理.
- 计算生物学 计算生物学
背景情况:
- 器官形状是由复杂的形态遗传程序决定的,涉及细胞信号和机械力量.
- 预测器官形状需要精确的基于物理的亚细胞力分布模型,但从数据中对参数进行校准是具有挑战性的.
- 了解这些机制对于从发育生物学到癌症研究等领域至关重要.
研究的目的:
- 开发一个贝叶斯优化框架来解决对器官形态生成进行物理模型校准的反向问题.
- 确定最佳的细胞力分布,从而预测实验观察到的器官形状.
- 应用这个框架来研究Drosophila翅膀形象性盘中的上皮过程.
主要方法:
- 利用贝叶斯优化框架与高斯过程回归 (GPR) 集成用于机器学习.
- 采用GPR来学习形态遗传程序与最终器官形状之间的映射功能.
- 校准并测试了Drosophila翅膀影像盘数据的框架,包括与原酶的扰动.
主要成果:
- 该框架成功推断了基础参数的变化,以匹配模拟和实验成像数据.
- 确定了模仿野生类型形状的独特参数集,并使全球灵敏度分析成为可能.
- 发现Piezo是一种机械敏感的离子通道,通过调节actomyosin收缩性和细胞外矩阵 (ECM) 弹性来影响折叠形成.
结论:
- 开发的计算管道有效地通过推断细胞力分布来逆向工程形态遗传机制.
- 动菌素收缩性和基底ECM刚性是Drosophila翅膀形象盘中曲形状的关键调节者.
- 这种工作流可用于研究器官系统和实时控制多细胞系统.
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