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Dynamical Modeling Methods for Systems Biology

Approx. 19 hours to complete

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Course Summary

Learn how to model and analyze dynamic systems using mathematical tools and computer simulations in this course on Dynamical Modeling.

Key Learning Points

Understand the fundamentals of dynamical systems and their behavior.
Learn how to use mathematical tools such as differential equations and linear algebra in modeling.
Gain experience in computer simulations and numerical methods.
Apply dynamical modeling to real-world problems such as population dynamics and climate change.
Develop critical thinking and problem-solving skills through hands-on exercises and projects.

Job Positions & Salaries of people who have taken this course might have

Data Scientist
- USA: $120,000
- India: ₹1,000,000
- Spain: €40,000
Research Analyst
- USA: $70,000
- India: ₹600,000
- Spain: €25,000
Simulation Engineer
- USA: $90,000
- India: ₹800,000
- Spain: €30,000
Climate Modeler
- USA: $110,000
- India: ₹900,000
- Spain: €35,000

Learning Outcomes

Ability to model and analyze complex dynamic systems.
Proficiency in using mathematical tools and computer simulations for modeling.
Experience in applying dynamical modeling to real-world problems.

Prerequisites or good to have knowledge before taking this course

Basic knowledge of calculus and linear algebra.
Familiarity with programming languages such as MATLAB or Python.

Course Difficulty Level

Intermediate

Course Format

Online self-paced course
Video lectures
Interactive quizzes and exercises
Hands-on projects

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Quantum Mechanics and Quantum Computation

Related Education Paths

Notable People in This Field

Edward Lorenz
James Gleick
Benjamin Good

Related Books

Description

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.

Outline

Introduction | Computing with MATLAB
Lecture 1 - Introduction
Lecture 2 - Computing with MATLAB - Part 1
Lecture 3 - Computing with MATLAB - Part 2
Lecture 4 - Computing with MATLAB - Part 3
Lecture 5 - Computing with MATLAB - Part 4
Syllabus
Supplemental Files
MATLAB Licenses
Lecture Slides
Lecture Slides
Supplementary Files
Just In Time MATLAB Tutorials
Lecture Slides
Supplementary Files
Assignment 1

Introduction to Dynamical Systems
Lecture 6 - Introduction to Dynamical Systems - Part 1
Lecture 7 - Introduction to Dynamical Systems - Part 2
Lecture 8 - Introduction to Dynamical Systems - Part 3
Lecture 9 - Introduction to Dynamical Systems - Part 4
Lecture Slides
Lecture Slides
Lecture Slides
Lecture Slides
Assignment 2

Bistability in Biochemical Signaling Models
Lecture 10 - Bistability in Biochemical Signaling Models - Part 1
Lecture 11 - Bistability in Biochemical Signaling Models - Part 2
Lecture 12 - Bistability in Biochemical Signaling Models - Part 3
Lecture 13 - Bistability in Biochemical Signaling Models - Part 4
Lecture 14 - Bistability in Biochemical Signaling Models - Part 5
Lecture 15 - Bistability in Biochemical Signaling Models - Part 6
Lecture Slides
Lecture Slides
Lecture Slides
Lecture Slides
Assignment 3

Computational Modeling of the Cell Cycle
Lecture 16 - Computational Modeling of the Cell Cycle - Part 1
Lecture 17 - Computational Modeling of the Cell Cycle - Part 2
Lecture 18 - Computational Modeling of the Cell Cycle - Part 3
Lecture 19 - Computational Modeling of the Cell Cycle - Part 4
Lecture Slides
Lecture Slides
Lecture Slides
Assignment 4

Modeling Electrical Signaling
Lecture 20 - Mathematical Models of Action Potentials - Part 1
Lecture 21 - Mathematical Models of Action Potentials - Part 2
Lecture 22 - Mathematical Models of Action Potentials - Part 3
Lecture 23 - Mathematical Models of Action Potentials - Part 4
Lecture 24 - Mathematical Models of Action Potentials - Part 5
Lecture 25 - Mathematical Models of Action Potentials - Part 6
Lecture Slides
Lecture Slides
Lecture Slides
Lecture Slides
Lecture Slides
Assignment 5

Modeling with Partial Differential Equations
Lecture 26 - Modeling with Partial Differential Equations - Part 1
Lecture 27 - Modeling with Partial Differential Equations - Part 2
Lecture 28 - Modeling with Partial Differential Equations - Part 3
Lecture Slides
Lecture Slides

Stochastic Modeling
Lecture 29 - Stochastic Modeling - Part 1
Lecture 30 - Stochastic Modeling - Part 2
Lecture Slides

Summary of User Reviews

Learn Dynamical Modeling with Coursera! This course has received great reviews and is highly recommended by its users. Many users praise the practical approach and hands-on experience gained from the course.

Key Aspect Users Liked About This Course

The practical approach and hands-on experience gained from the course are highly praised by users.

Pros from User Reviews

Great practical approach
Hands-on experience
Engaging and informative lectures
Easy to follow instructions
Useful assignments and quizzes

Cons from User Reviews

Some users found the course material to be too difficult
Not enough support for beginners
Some users found the pace to be too slow
Not enough visual aids in the lectures
Some users found the assignments to be too time-consuming

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