Online Course
Dynamical Modeling Methods for Systems Biology
Icahn School of Medicine at Mount Sinai via Coursera
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Biology Courses
- Provider Coursera
- Cost Free Online Course (Audit)
- Session Upcoming
- Language English
- Certificate Paid Certificate Available
- Effort 8-10 hours a week
- Duration 7 weeks long
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Overview
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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.
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.
Syllabus
Introduction | Computing with MATLAB
-The description goes here
Introduction to Dynamical Systems
-The description goes here
Bistability in Biochemical Signaling Models
-The description goes here
Computational Modeling of the Cell Cycle
-The description goes here
Modeling Electrical Signaling
-The description goes here
Modeling with Partial Differential Equations
-The description goes here
Stochastic Modeling
-The description goes here
-The description goes here
Introduction to Dynamical Systems
-The description goes here
Bistability in Biochemical Signaling Models
-The description goes here
Computational Modeling of the Cell Cycle
-The description goes here
Modeling Electrical Signaling
-The description goes here
Modeling with Partial Differential Equations
-The description goes here
Stochastic Modeling
-The description goes here
Taught by
Eric Sobie
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Marhum
is taking this course right now, spending 2 hours a week on it and found the course difficulty to be easy.
i didn't understand about this course but i interest to enhance my english ability, because i'm looking for an overseas scholarship needing english certificate as require
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Anonymous
Anonymous
completed this course.
Best online class ever! Lot of fun accesible also to a biologist.
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