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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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The mammalian target of rapamycin  (mTOR) is a serine/threonine kinase that regulates growth, proliferation, and cell survival in response to hormones, growth factors, or nutrient availability. This kinase exists in two structurally and functionally distinct forms: mTOR complex 1  (mTORC1) and mTOR complex 2  (mTORC2). The first form (mTORC1) is composed of a rapamycin-sensitive Raptor and proline-rich Akt substrate, PRAS40. In contrast,  mTORC2 consists of a...
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Updated: Jun 2, 2025

Author Spotlight: Advancing Alzheimer's Research &#8211; Exploring Early Detection and Multi-Omics Approaches
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模拟细胞状态动态的进步:整合omics数据和预测技术.

Sungwon Jung1,2

  • 1Department of Genome Medicine and Science, Gachon University College of Medicine, Incheon, Republic of Korea.

Animal cells and systems
|January 14, 2025
PubMed
概括
此摘要是机器生成的。

细胞状态的动态建模对于理解生物学至关重要. 这篇评论涵盖了网络和深度学习模型等方法,集成omics数据来预测细胞行为和推进精准医学.

关键词:
细胞状态动态 细胞状态动态细胞表型建模 细胞表型建模细胞重新编程的细胞重编程.疾病进展建模模型.

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科学领域:

  • 计算生物学 计算生物学
  • 系统生物学 系统生物学
  • 基因组学就是基因组学.

背景情况:

  • 细胞状态动态是生物过程的基础,如分化和疾病.
  • 了解这些动态需要先进的计算方法.

研究的目的:

  • 为细胞状态提供当前动态建模方法的全面审查.
  • 突出将omics数据与这些模型的整合用于预测见解.
  • 讨论基因功能,治疗设计和发育生物学中的应用.

主要方法:

  • 动态和静态生物分子网络模型的审查.
  • 探索用于细胞状态建模的深度学习方法.
  • 整合策略与奥米克数据 (例如,转录组学,单细胞RNA测序).

主要成果:

  • 各种建模技术可以捕捉和预测细胞行为和转变.
  • 建模有助于预测基因淘汰效应和设计干预措施.
  • 这些模型对于模拟器官发育等复杂的生物系统是有价值的.

结论:

  • 选择合适的建模策略取决于系统的复杂性,可扩展性和分辨率.
  • 建模方面的进步对于开发强大,可解释的工具至关重要.
  • 改进的模型将增强对细胞动态的理解和操纵,推进治疗策略和精准医学.