This course gives an introduction to how to create genetic circuit models. These models leverage chemical reactions represented using the Systems Biology Markup Language (SBML). The second module introduces methods to simulate these models using ordinary differential equation (ODE) methods. The third module teach stochastic simulation methods. The fourth module introduces several variations of the stochastic simulation algorithm. Finally, the fifth module introduces genetic technology method that leverage computational analysis for selecting parts and verifying their performance.
Engineering Genetic Circuits: Modeling and Analysis
Engineering Genetic Circuits: Modeling and Analysis
本课程是 Engineering Genetic Circuits 专项课程 的一部分
位教师：Chris Myers
访问权限由 New York State Department of Labor 提供
您将学到什么
Design and analyze models of genetic circuits.
Simulate genetic circuit models using ODE simulation methods.
Simulate genetic circuit models using stochastic simulation methods.
Utilize genetic technology mappers to select parts for genetic designs.
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作业
16 项作业
授课语言：英语（English）
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本课程是 Engineering Genetic Circuits 专项课程 专项课程的一部分
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该课程共有5个模块
This week will describe the basics of modeling biological systems using chemical reactions, how these models can be represented using the Systems Biology Markup Language (SBML) standard, and how these models can be constructed using software tools such as iBioSim.
涵盖的内容
23个视频8篇阅读材料2个作业1次同伴评审
This module will introduce the theory and methods for the analysis of genetic circuit models using ordinary differential equations (ODEs). In particular, it will describe the classical chemical kinetic model, numerical methods for ODE simulation of these models, and techniques to analyze these ODE models qualitatively.
涵盖的内容
13个视频3篇阅读材料3个作业1次同伴评审
This module will introduce stochastic analysis methods for genetic circuits. In particular, it will introduce the stochastic chemical kinetics model, Gillespie's Stochastic Simulation Algorithm (SSA) to analyze these models, and various alternative stochastic analysis methods. Finally, the module will conclude with some additional topics: the Chemical Langevin Equation, stochastic Petri nets, the phage lambda model, and spatial Gillespie methods.
涵盖的内容
20个视频4篇阅读材料4个作业1次同伴评审
This module presents several variations on the SSA algorithm to solve particular analysis problems. In particular, the hierarchical SSA (hSSA) methods enable the analysis of large models, the weighted SSA (wSSA) methods allow for the analysis of rare events, and the incremental SSA (iSSA) methods enable the determination of typical behaviors.
涵盖的内容
18个视频4篇阅读材料3个作业
This module presents various ways that modeling can be utilized in genetic circuit design to select parts for optimal performance.
涵盖的内容
21个视频4篇阅读材料4个作业
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课程 是 University of Colorado Boulder提供的以下学位课程的一部分。如果您被录取并注册，您已完成的课程可计入您的学位学习，您的学习进度也可随之转移。
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University of Colorado Boulder
University of Colorado Boulder
University of Colorado Boulder
University of Colorado Boulder