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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/dynamical-modeling
课程评论：没有评论
课程名称：生物系统的动态建模方法 课程概述： 该课程介绍了现代生物系统研究中使用的动态建模技术。通过案例驱动的方法教授当代数学建模技术，适合高级本科生和初学研究生。课程内容包括生物学背景的讲解，以及经典数学模型与近年来生物过程的新表现形式的发展。这门课程将帮助计划在实验室中使用实验技术，并利用计算建模深入理解实验的学生。对那些打算进行生物系统建模的原创研究的学生来说，这也是一个有价值的入门概览。 本课程侧重于用于生物系统研究的动态建模技术。这些技术基于生物机制，利用这些模型进行的模拟能够生成可实验验证的预测，这些预测往往为生物过程提供了新的见解。教授的方法主要分为以下几个类别：1）基于常微分方程的模型，2）基于偏微分方程的模型，以及3）随机模型。 课程大纲： 第一部分：介绍 | MATLAB计算 描述：在此部分中，将介绍课程内容及使用MATLAB进行计算的基础。 第二部分：动态系统导论 描述：解释动态系统的基本概念及其在生物学中的应用。 第三部分：生化信号模型中的双稳态 描述：探讨生化信号模型中的双稳态现象。 第四部分：细胞周期的计算建模 描述：利用计算方法模型化和分析细胞周期的动态。 第五部分：电信号建模 描述：讨论如何建模生物体内的电信号传递。 第六部分：偏微分方程建模 描述：介绍如何使用偏微分方程进行生物过程的建模。 第七部分：随机建模 描述：讲解随机模型在生物系统建模中的应用与重要性。
Part: 1
Title:Introduction | Computing with MATLAB
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Part: 2
Title:Introduction to Dynamical Systems
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Part: 3
Title:Bistability in Biochemical Signaling Models
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Part: 4
Title:Computational Modeling of the Cell Cycle
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Part: 5
Title:Modeling Electrical Signaling
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Part: 6
Title:Modeling with Partial Differential Equations
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Part: 7
Title:Stochastic Modeling
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An introduction to dynamical modeling techniques used in contemporary Systems Biology research. We take a case-based approach to teach contemporary mathematical modeling techniques. The course is appropriate for advanced undergraduates and beginning graduate students. Lectures provide biological background and describe the development of both classical mathematical models and more recent representations of biological processes. The course will be useful for students who plan to use experimental techniques as their approach in the laboratory and employ computational modeling as a tool to draw deeper understanding of experiments. The course should also be valuable as an introductory overview for students planning to conduct original research in modeling biological systems. This course focuses on dynamical modeling techniques used in Systems Biology research. These techniques are based on biological mechanisms, and simulations with these models generate predictions that can subsequently be tested experimentally. These testable predictions frequently provide novel insight into biological processes. The approaches taught here can be grouped into the following categories: 1) ordinary differential equation-based models, 2) partial differential equation-based models, and 3) stochastic models.
