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

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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.
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Gene expression can be regulated at almost every step from gene to protein. Transcription is the step that is most commonly regulated. This involves the binding of proteins to short regulatory sequences on the DNA. This association can either promote or inhibit the transcription of a gene associated with the respective sequence.
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A gene is the fundamental unit of heredity. Every individual has two copies of each gene, one inherited from each parent. Although most people contain the same genes, there is a small fraction that is slightly different amongst people. A gene with a small difference in its sequence of DNA bases forms different alleles, contributing to different phenotypes.
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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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A gene is a stretch of DNA that serves as the blueprint for functional RNAs and proteins. Since DNA is comprised  of nucleotides and proteins are comprised of amino acids, a mediator is required to convert the information encoded in DNA into proteins. This mediator is the messenger RNA (mRNA). mRNA copies the blueprint from DNA by a process called transcription. In eukaryotes, transcription occurs in the nucleus by complementary base-pairing with the DNA template. The mRNA is then...
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The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors—these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5' capping, splicing, and the...
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使用队列理论解决随机基因表达模型:一个教程回顾

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  • 1School of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom.

Biophysical journal
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概括
此摘要是机器生成的。

这项研究引入了队列理论作为一种分析随机基因表达模型的新方法. 这种方法为以前无法解决的复杂模型提供了新的分析解决方案.

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科学领域:

  • * 数学生物学数学生物学
  • * 计算生物学 * 计算生物学
  • * 分子系统生物学 * 分子系统生物学

背景情况:

  • * 随机模型对于理解基因表达变异性至关重要.
  • *化学主方程是标准框架,通常用分析方法解决.
  • *基因表达的随机性影响细胞功能和决策.

研究的目的:

  • * 介绍和审查排队理论作为静态基因表达模型的替代方法.
  • * 为了证明无限服务器队列的应用到单细胞随机生物学.
  • * 为复杂的基因表达模型提供分析解决方案.

主要方法:

  • * 对生物系统应用的队列理论原则的审查.
  • * 解释六种类型的无限服务器队列在单细胞基因表达的背景下.
  • * 对于静态和非静态分布和动量的分析表达式的推导.
  • * 对基因表达噪声的Fano因子的边界计算.

主要成果:

  • * 排队理论为分析随机基因表达提供了一个新的框架.
  • * mRNA和蛋白质数量 (分布和时刻) 的分析表达式得出.
  • * 建立了Fano因子的边界,提供了对噪声特征的见解.
  • *该方法适用于使用标准方法难以处理的复杂模型.

结论:

  • * 排队理论为随机基因表达分析提供了一个强大而未被充分利用的工具.
  • * 这种方法有可能解决分析上具有挑战性的复杂模型.
  • * 该框架有助于更深入地了解单细胞中的分子噪声.