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./p>

- Modeling in Systems Biology
- Computing with MATLAB
- Dynamical systems tools to analyze behavior of mathematical models
- Modeling emergent Properties: bistability in biochemical signaling
- Modeling the cell cycle
- Modeling electrical signaling in neurons
- Mathematical models of electrical signals that propagate in space and time
- Modeling the spatial dependence of intracellular signaling
- Mathematical models of processes in which randomness is important

Each class session will consist of an approximately one hour lecture divided into multiple shorter segments, which will include solving a sample problem to illustrate the method being taught in the lecture.

For evaluation, students will be given assignments that will require: 1) implementing a mathematical model; 2) performing simulations and/or analysis of the model; and 3) interpreting the biological implications of the results in multiple choice format. The average of the six assignments will be used to calculate overall course grade.

Yes. Students who successfully complete the course will get a Statement of Accomplishment signed by the Course Director.

Dates:

- 2 March 2015, 7 weeks
- 31 March 2014, 8 weeks

Included in selections:

Bioinformatics

Bioinformatics and mathematical methods in biology

Bioinformatics and mathematical methods in biology

More on this topic:

System Design and Analysis based on AD and Complexity Theories

This course studies what makes a good design and how one develops a good design...

This course studies what makes a good design and how one develops a good design...

Network Analysis in Systems Biology

An introduction to data integration and statistical methods used in contemporary...

An introduction to data integration and statistical methods used in contemporary...

Regenerative Medicine: from Bench to Bedside

Regenerative medicine involves the repair and regeneration of tissues for therapeutic...

Regenerative medicine involves the repair and regeneration of tissues for therapeutic...

Analysis of Biological Networks (BE.440)

This class analyzes complex biological processes from the molecular, cellular...

This class analyzes complex biological processes from the molecular, cellular...

A Love-Hate Relationship: Cholesterol in Health and Disease

In this class, we will examine cholesterol's role in the cell and in the body...

In this class, we will examine cholesterol's role in the cell and in the body...

More from 'Mathematics, Statistics and Data Analysis':

RiceX Linear Algebra Part 1

This course is an introduction to linear algebra. You will discover the basic...

This course is an introduction to linear algebra. You will discover the basic...

Data Science: R Basics

Build a foundation in R and learn how to wrangle, analyze, and visualize data...

Build a foundation in R and learn how to wrangle, analyze, and visualize data...

Data Science: Visualization

Learn basic data visualization principles and how to apply them using ggplot2...

Learn basic data visualization principles and how to apply them using ggplot2...

Data Science: Probability

Learn probability theory -- essential for a data scientist -- using a case study...

Learn probability theory -- essential for a data scientist -- using a case study...

Data Science: Inference and Modeling

Learn inference and modeling, two of the most widely used statistical tools...

Learn inference and modeling, two of the most widely used statistical tools...

More from 'Coursera':

First Year Teaching (Secondary Grades) - Success from the Start

Success with your students starts on Day 1. Learn from NTC's 25 years developing...

Success with your students starts on Day 1. Learn from NTC's 25 years developing...

Understanding 9/11: Why Did al Qai’da Attack America?

This course will explore the forces that led to the 9/11 attacks and the policies...

This course will explore the forces that led to the 9/11 attacks and the policies...

Aboriginal Worldviews and Education

This course will explore indigenous ways of knowing and how this knowledge can...

This course will explore indigenous ways of knowing and how this knowledge can...

Analytic Combinatorics

Analytic Combinatorics teaches a calculus that enables precise quantitative...

Analytic Combinatorics teaches a calculus that enables precise quantitative...

Accountable Talk®: Conversation that Works

Designed for teachers and learners in every setting - in school and out, in...

Designed for teachers and learners in every setting - in school and out, in...

© 2013-2019