高通量和基因组规模动态建模的黎明:最近的进展和未来的方向
Ilias Toumpe1, Subham Choudhury1, Vassily Hatzimanikatis1
1Laboratory of Computational Systems Biology (LCSB), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne CH-1015, Switzerland.
ACS synthetic biology
|April 22, 2025
概括
动力模型为细胞代谢提供了详细的见解. 最近的进步,包括机器学习集成和新的数据库,克服了过去的局限性,加速了生物学和医学的进步.
科学领域:
- 系统生物学 系统生物学
- 代谢工程是代谢工程.
- 合成生物学 合成生物学
- 医学研究 医学研究
背景情况:
- 动力模型提供了详细的,现实的细胞代谢过程的表现,捕捉动态行为和调节机制.
- 从历史上看,广泛的参数化要求和高计算成本阻碍了在高通量研究中广泛采用动态模型.
研究的目的:
- 审查最近动力建模方面的进展.
- 讨论未来的方向和这些发展的潜在影响.
主要方法:
- 机器学习与机械化代谢模型的整合.
- 开发新的动力参数数据库.
- 应用量身定制的参数化策略.
主要成果:
- 最近的进展正在克服动力模型开发和采用的历史障碍.
- 这些进步正在使更容易获得和更高效的动力模型成为可能.
结论:
- 新兴技术正在重塑运动建模领域.
- 这些发展有望加速系统生物学,代谢工程和医学研究的进展.
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