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生化信号通路中的随机参数估计
Stochastic Parameter Estimation in Biochemical Signalling Pathways |
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| 课程网址: |
http://videolectures.net/pmnp07_papadopoulos_speb/
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| 主讲教师: |
George Papadopoulos
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| 开课单位: |
曼彻斯特大学
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| 开课时间: |
2007-09-07
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| 课程语种: |
英语
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| 中文简介: |
在对生化网络进行建模时，通常使用诸如通用ODE模型结构之类的定性信息来进行参数估计，同时保留基本模型结构，最能代表控制细胞的生化过程。然而，当来自每个参与物种的可用分子的数量非常小（小拷贝数）时，情况并非如此，因为有必要引入复杂的随机建模技术，利用化学主方程来模拟系统状态（物种浓度）的轨迹。M〔7〕。基因表达本质上是随机的，因此基因调控和信号转导网络也有类似的行为。最重要的是，在酵母、小鼠和人类细胞中检测到的大量基因表达数据集遵循一个由许多低丰度转录物扭曲的帕累托样分布模型，涵盖了大量真核细胞[2]。因此，很明显，随机建模策略应该是结构，以适应系统的特定需求….
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| 课程简介: |
It is common when modelling biochemical networks to use qualitative information such as the general ODE model structure so as to proceed in parameter estimation while at the same time retaining the basic model structure the best represents the biochemical process governing the cell. This is not the case however when the population of the available molecules from each of the participating species is very small (small copy number) deeming necessary the introduction of complex stochastic modelling techniques that make use of chemical master equations to simulate the trajectories of the states (species concentration) of the system [7]. Gene expression is stochastic by nature [7][5] and as a consequence gene regulatory and signal transduction networks follow a similar behaviour. Most importantly, a large number of gene expression data sets examined in yeast, mouse and human cells follow a Pareto-like distribution model skewed by many low-abundance transcripts, covering a large variety of eukaryotic cells [2]. It is therefore apparent that a stochastic modelling strategy should be structure so as to accommodate the specific needs of the system....
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| 关 键 词: |
生物化学网络; 微分方程; 参数估计; 建模技术
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| 课程来源: |
视频讲座网
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| 最后编审: |
2020-06-02：张荧(课程编辑志愿者)
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| 阅读次数: |
39
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