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相关概念视频

Turnover Number and Catalytic Efficiency01:19

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The turnover number of an enzyme is the maximum number of substrate molecules it can transform per unit time. Turnover numbers for most enzymes range from 1 to 1000 molecules per second. Catalase has the known highest turnover number, capable of converting up to 2.8×106 molecules of hydrogen peroxide into water and oxygen per second. Lysozyme has the lowest known turnover number of half a molecule per second.
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Catalytically Perfect Enzymes01:07

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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...
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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Determination of Michaelis Constant and Maximum Elimination Rate01:20

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The Michaelis constant (KM) and the theoretical maximum process rate (Vmax) are vital parameters in the Michaelis-Menten equation, central to many biochemical reactions. They provide essential insights into enzyme kinetics and drug metabolism.
These parameters can be estimated by analyzing plasma concentration data post-drug administration. A notable example of this application is phenytoin, a drug with capacity-limited kinetics. It's recommended that phenytoin should be administered at two...
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Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

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Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
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Operon Model01:23

Operon Model

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The operon model represents a fundamental mechanism of gene regulation in prokaryotes, enabling coordinated expression of genes involved in related metabolic or functional pathways. Operons consist of structural genes, a promoter, and an operator, with transcription regulated by repressors, activators, and small effector molecules.Structure and Function of OperonsAn operon is a cluster of structural genes transcribed together under the control of a single promoter. The promoter region...
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相关实验视频

Updated: Jul 23, 2025

Procedure for Adaptive Laboratory Evolution of Microorganisms Using a Chemostat
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敏感性分析和适应性突变策略差异进化算法,以优化代谢模型中酶的周转数.

Xingcun Fan1, Lingfeng Cao1, Xuefeng Yan1,2

  • 1Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China.

Biotechnology and bioengineering
|July 14, 2023
PubMed
概括

在基因组规模代谢网络模型 (GSMMs) 中优化酶约束可以改善代谢预测. 适应差异进化算法有效地改进了关键酶参数,以提高准确性.

关键词:
这种植物是Saccharomyces cerevisiae.适应性突变战略的适应性突变策略酶受约束的基因组规模代谢网络模型.灵敏度分析是一种灵敏度分析.

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科学领域:

  • 代谢工程是代谢工程.
  • 计算生物学 计算生物学
  • 系统生物学 系统生物学

背景情况:

  • 基因组规模的代谢网络模型 (GSMMs) 对于理解细胞代谢至关重要.
  • 结合酶约束,特别是酶周转数,显著提高了GSMM的预测能力.
  • 对酶约束的准确参数化对于可靠的代谢预测至关重要.

研究的目的:

  • 开发和评估一种用于受酶约束的GSMM的新型参数优化方法.
  • 确定影响特定增长率预测的关键酶参数.
  • 通过系统的优化来提高GSMM的准确性和预测能力.

主要方法:

  • 灵敏度分析以确定有影响力的酶参数.
  • 差异进化 (DE) 算法,具有适应性突变策略,用于参数优化.
  • 在Saccharomyces cerevisiae代谢模型 (ecYeast8.3.4) 上应用和评估该方法.

主要成果:

  • 敏感性分析将参数分为最敏感,高度敏感和非敏感的组.
  • 适应式DE算法成功优化了高度敏感的参数,改善了模型预测.
  • 建议的策略包括保留所有参数或仅优化高度敏感的参数.

结论:

  • 使用自适应DE的酶受约束的GSMM参数优化是有效的.
  • 将优化重点放在高度敏感的参数上,为改善代谢模型提供了一个有效的策略.
  • 开发的方法提高了GSMM用于代谢工程和合成生物学应用的预测准确性.