Chevron Left
Back to Dynamical Modeling Methods for Systems Biology

Learner Reviews & Feedback for Dynamical Modeling Methods for Systems Biology by Icahn School of Medicine at Mount Sinai

4.7
stars
218 ratings

About the Course

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....

Top reviews

GS

Nov 24, 2020

Interesting subject, I liked the practical approach. The tests are not easy but I liked the homework style approach. It forces you to understand the subject better than the regular quizzes.

ED

May 28, 2018

New to systems biology and I am really impressed with the clear explanations. Currently, on week 4 but aiming to finish to the end. Thanks to the dude who made it! :) #lifesaver

Filter by:

26 - 42 of 42 Reviews for Dynamical Modeling Methods for Systems Biology

By Thiago T V

•

Jan 16, 2016

I liked it a lot!

By Musalula S

•

Sep 11, 2016

Great course

By 廖文杰

•

Apr 19, 2016

terrific！

By mac g

•

Jan 17, 2018

great

By Salvador C A

•

Dec 10, 2015

good

By Ricardo S

•

Jun 30, 2016

T

By Jacob M

•

Aug 8, 2023

Course is available at all times, but the link to MATLAB is not.

The material is excellent and the lessons are enjoyable. It could benefit from a little more background in mathematics before diving into the code itself, and a little more focus on diagrams in each problem would help systematize how to apply these principles.

By Christopher N

•

Sep 12, 2023

I really enjoyed the course. It was exactly what I was looking for. However, project files (*.sboj) were missing from downloads. I would like to have used Simbiology. Luckily, MATLAB files (*.m) were still available. Otherwise, I would have given the course 5 stars.

By MARCELA M M

•

Sep 27, 2020

Nice course thank you! I consider the first assignment is confusing and the descriptions of assignment in general could be improved. Also, I think the predefined ODE solvers must be used from the begining instead of manually implement Euler's method. THANKS!

By Samuel B

•

Feb 12, 2021

Very good course. The first 5 weeks are incredible, the last couple weeks are decidedly of a lower quality with no assignments to accompany the material.

By Stefano M

•

Feb 16, 2017

Very interesting course: I'm a Computer Engineer and I found very interesting the lessons about bistability and about stochastic modelling.

By Bill T

•

Dec 17, 2015

Very informative course with good exercises. Would recommend having PDE example code, as well.

By Meghana D

•

Jul 3, 2021

The assignments seemed very tough. Other than that this course was very knowledgeable.

By Hazem H

•

Feb 7, 2016

it's quite very interesting

By Akhil K

•

May 27, 2019

Good

By Judhajit S

•

Jul 22, 2023

Good course but not able to apply learnings from this course as Capstone Project is still not available

By Jonathan G

•

Mar 31, 2016

a little too much for my taste, but I learned a lot cellular biology.