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相关概念视频

Interactions Between Signaling Pathways01:19

Interactions Between Signaling Pathways

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Signaling cascades usually lack linearity. Multiple pathways interact and regulate one another, allowing cells to integrate and respond to diverse environmental stimuli.
Convergence and divergence, and cross-talk between signaling pathways
Two distinct signaling pathways can converge on a single functional unit, which may either be a single protein or a complex of proteins. The response is either functionally distinct or synergistic between the two pathways but different from the response...
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Amplifying Signals via Enzymatic Cascade01:22

Amplifying Signals via Enzymatic Cascade

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When a ligand binds to a cell-surface receptor, the receptor's intracellular domain changes shape, which may either activate its enzyme function or allow its binding to other molecules. The initial signal is amplified by most signal transduction pathways. This means that a single ligand molecule can activate multiple molecules of a downstream target. Proteins that relay a signal are most commonly phosphorylated at one or more sites, activating or inactivating the protein. Kinases catalyze...
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Diversity in Cell Signaling Responses01:22

Diversity in Cell Signaling Responses

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The physiological function of a cell and cellular communication are outcomes of a range of extrinsic signals, intracellular signaling pathways, and cellular responses. No two cell types express the same repertoire of signaling components. Receptors are highly selective for their cognate ligands, but once activated, they can alter multiple cellular processes such as DNA transcription, protein synthesis, and metabolic activity. 
Graded and Abrupt Responses
Some signaling systems generate...
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相关实验视频

Updated: Jul 9, 2025

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

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通过多重实验和机器学习实现路径设计.

Aashutosh Girish Boob1, Junyu Chen2, Huimin Zhao3

  • 1Department of Chemical and Biomolecular Engineering, University of Illinois Urbana-Champaign, Urbana, IL, 61801, United States; Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL, 61801, United States; DOE Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois Urbana-Champaign, Urbana, Illinois 61801, United States.

Metabolic engineering
|December 1, 2023
PubMed
概括
此摘要是机器生成的。

本综述探讨了先进的途径工程策略,包括多重实验和机器学习,以克服微生物生产有价值分子的局限性. 这些方法加快了最佳生物合成途径的发现,以提高产量和成本效益.

关键词:
组合性路径优化的优化机器学习是机器学习.代谢工程是代谢工程.路径设计路径设计.

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科学领域:

  • 代谢工程和合成生物学用于可持续的化学生产.

背景情况:

  • 自然的代谢多样性为有价值的分子合成提供了途径.
  • 宿主转移的挑战包括中间积累和竞争途径,阻碍产量.
  • 传统的优化方法是缓慢的,劳动密集型的,并且经常产生低于最佳的结果.

研究的目的:

  • 审查用于增强分子生产的途径工程方面的进展.
  • 突出多重实验和机器学习在优化路径中的作用.
  • 讨论可持续微生物生产化学品的工具和策略.

主要方法:

  • 对路径工程技术的最新文献的审查.
  • 专注于多重实验,探索酶表达场景.
  • 机器学习的应用用于识别最佳路径配置.

主要成果:

  • 多重体实验和机器学习克服了传统方法的局限性.
  • 这些方法使得更广泛的设计空间探索可用于路径优化.
  • 发现最佳途径配置以提高分子产量的可能性增加.

结论:

  • 多重体实验和机器学习显著改善了传统方法.
  • 这些方法对于克服代谢通路工程的局限性至关重要.
  • 它们增强了有效的微生物细胞工厂的发现,以实现可持续的化学合成.