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相关概念视频

Enzyme Kinetics01:19

Enzyme Kinetics

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Enzymes speed up reactions by lowering the activation energy of the reactants. The speed at which the enzyme turns reactants into products is called the rate of reaction. Several factors impact the rate of reaction, including the number of available reactants. Enzyme kinetics is the study of how an enzyme changes the rate of a reaction.
Scientists typically study enzyme kinetics with a fixed amount of enzyme in the controlled environment of a test tube. When more reactant, or substrate, is...
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Synthetic Biology02:55

Synthetic Biology

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Synthetic biology is an interdisciplinary science that involves using principles from disciplines such as engineering, molecular biology, cell biology, and systems biology. It involves remodeling existing organisms from nature or constructing completely new synthetic organisms for applications such as protein or enzyme production, bioremediation, value-added macromolecule production, and the addition of desirable traits to crops, to name a few.
Golden rice
Golden rice is a genetically modified...
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Introduction to Enzyme Kinetics01:19

Introduction to Enzyme Kinetics

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Enzyme kinetics studies the rates of biochemical reactions. Scientists monitor the reaction rates for a particular enzymatic reaction at various substrate concentrations. Additional trials with inhibitors or other molecules that affect the reaction rate may also be performed.
The experimenter can then plot the initial reaction rate or velocity (Vo) of a given trial against the substrate concentration ([S]) to obtain a graph of the reaction properties. For many enzymatic reactions involving a...
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Catalytically Perfect Enzymes01:07

Catalytically Perfect Enzymes

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The theory of catalytically perfect enzymes was first proposed by W.J. Albery and J. R. Knowles in 1976. These enzymes catalyze biochemical reactions at high-speed. Their catalytic efficiency values range from 108-109 M-1s-1. These enzymes are also called 'diffusion-controlled' as the only rate-limiting step in the catalysis is that of the substrate diffusion into the active site. Examples include triose phosphate isomerase, fumarase, and superoxide dismutase.
 
Most enzymes...
4.9K
Introduction to Mechanisms of Enzyme Catalysis01:13

Introduction to Mechanisms of Enzyme Catalysis

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For many years, scientists thought that enzyme-substrate binding took place in a simple "lock-and-key" fashion. This model stated that the enzyme and substrate fit together perfectly in one instantaneous step. However, current research supports a more refined view scientists call induced fit. The induced-fit model expands upon the lock-and-key model by describing a more dynamic interaction between enzyme and substrate. As the enzyme and substrate come together, their interaction causes...
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Enzyme Inhibition01:30

Enzyme Inhibition

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Inhibitors are molecules that reduce enzyme activity by binding to the enzyme. In a normally functioning cell, enzymes are regulated by a variety of inhibitors. Drugs and other toxins can also inhibit enzymes. Some inhibitors bind to the enzyme’s active site, while others inhibit enzymatic activity by binding to other sites on the protein structure.
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相关实验视频

Updated: Jan 12, 2026

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow
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Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow

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大型语言模型代理作为数据驱动的酶工程中的实验编排者.

Yuan Gao1, Lihao Fu2, Tong Si1

  • 1State Key Laboratory for Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; University of Chinese Academy of Sciences, Beijing 100049, China.

Current opinion in biotechnology
|November 1, 2025
PubMed
概括
此摘要是机器生成的。

大型语言模型 (LLM) 代理通过智能编排实验工作流来简化酶工程. 这些人工智能系统动态生成协议并适应反,加速设计-构建-测试-学习周期.

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科学领域:

  • 生物化学 生物化学
  • 计算生物学 计算生物学
  • 人工智能的人工智能

背景情况:

  • 由于人工智能 (AI),计算酶设计正在迅速发展.
  • 人工智能驱动的酶设计的实验验证面临着重大瓶.
  • 当前的自动化方法缺乏灵活性来管理复杂的实验工作流程.

研究的目的:

  • 检查大型语言模型 (LLM) 代理如何弥合计算酶设计和实验验证之间的差距.
  • 突出LLM代理人在编排复杂的实验工作流程中的作用.
  • 展示LLM剂在加速酶工程中的潜力.

主要方法:

  • 使用由LLMs驱动的多代理框架.
  • 动态生成实验协议并将其转化为机器命令.
  • 协调资源并适应实时实验反,以实现闭环代.

主要成果:

  • LLM 代理使实验工作流程的智能编排成为可能,超越了严格的脚本编写.
  • 使用LLM代理在酶工程中展示了闭环设计-构建-测试-学习循环.
  • 展示了LLM系统处理复杂,上下文依赖的实验决策的能力.

结论:

  • 在计算酶设计中,LLM代理为实验验证瓶提供了强大的解决方案.
  • 这些人工智能系统提供了标准化和灵活性,加速了酶工程.
  • 用LLM驱动的实验编排可以提高科学研究中先进的人工智能能力的可访问性.