基于物理和数据驱动的混合细胞生物系统框架:应用到有机体的形态发生
Daniel Camacho-Gomez1, Ioritz Sorzabal-Bellido2, Carlos Ortiz-de-Solorzano2
1Department of Mechanical Engineering, Multiscale in Mechanical and Biological Engineering (M2BE), Aragon Institute of Engineering Research (I3A), University of Zaragoza, Zaragoza, Spain.
iScience
|July 24, 2023
概括
本研究介绍了一种人工智能框架,以了解细胞功能协调. 它揭示了细胞如何根据微环境线索自我组织成模式,从而推进了生物系统模拟.
科学领域:
- 计算生物学 计算生物学
- 细胞生物学 细胞生物学
- 人工智能在生物学中的应用
背景情况:
- 了解细胞功能协调是解读生物组织的关键.
- 当前的模型往往缺乏整合机械相互作用和数据驱动的决策.
研究的目的:
- 提出一种新的人工智能框架,用于模拟和理解空间和时间协调的细胞功能.
- 研究细胞自我组织的原理,以应对微环境条件.
主要方法:
- 开发了一种基于物理和深度学习的混合模型.
- 集成的机械相互作用和细胞功能与数据驱动的决策过程.
- 在3D小鼠胰腺管腺癌 (PDAC) 细胞培养中的图像数据指标上训练了深度学习算法.
主要成果:
- 确定了控制细胞自组织过程激活的基本原则.
- 证明了微环境条件如何影响细胞模式.
- 成功模拟了瘤有机体中的细胞自我组织.
结论:
- 拟议的AI框架增强了模拟细胞水平生物系统的工具.
- 为解开复杂的形态遗传模式提供了一个新的视角.
- 提供了关于细胞行为和组织在特定的微环境的见解.
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