Introduction to Computational and Systems Biology

Published on Jan 20, 2015

MIT 7.91J Foundations of Computational and Systems Biology, Spring 2014 View the complete course: http://ocw.mit.edu/7-91JS14 Instructor: Christopher Burge, David Gifford, Ernest Fraenkel In this lecture, Professors Burge, Gifford, and Fraenkel give an historical overview of the field of computational and systems biology, as well as outline the material they plan to cover throughout the semester. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu

Systems Biology – Neuroscience

Systems Biology – Physiology

Systems Biology – Genetics

Systems Biology – Applied Math

Systems biology course 2018

by Dr. Uri Alon

Lecture 1 – Basic concepts

Lecture 2 – Auto-regulation , a network motif

Lecture 3 Part a – Feed Forward Loops

Lecture 3 – Part b – Feed Forward Loop

Lecture 4a – unavailable

Lecture 4 b – Temporal Order, Global Structure, and Memory

Lecture 5 a – Robustness using bifunctional components

Lecture 5 b – Robustness using bifunctional components

Lecture 6 a – Robustness in bacterial chemotaxis

Lecture 6 b – Robustness in bacterial chemotaxis

Lecture 7 part A – Fold Change Detection

Lecture 7 part B – Fold Change Detection

Lecture 8 A – Dynamic Compensation

Lecture 8 B – Dynamic Compensation

Lecture 8 C – Dynamic Compensation

Lecture 9 How to build a Biological Oscillator

Lecture 10 Optimality in Biological Circuits

Lecture 11 Evolution and Multi-Objective Optimality

Lecture 12 Modularity

Information theory in systems biology. Part I: Gene regulatory and metabolic networks

“A Mathematical Theory of Communication”, was published in 1948 by Claude Shannon to establish a framework that is now known as information theory. In recent decades, information theory has gained much attention in the area of systems biology. The aim of this paper is to provide a systematic review of those contributions that have applied information theory in inferring or understanding of biological systems. Based on the type of system components and the interactions between them, we classify the biological systems into 4 main classes: gene regulatory, metabolic, protein–protein interaction and signaling networks. In the first part of this review, we attempt to introduce most of the existing studies on two types of biological networks, including gene regulatory and metabolic networks, which are founded on the concepts of information theory.

Information theory in systems biology. Part II: protein–protein interaction and signaling networks

By the development of information theory in 1948 by Claude Shannon to address the problems in the field of data storage and data communication over (noisy) communication channel, it has been successfully applied in many other research areas such as bioinformatics and systems biology. In this manuscript, we attempt to review some of the existing literatures in systems biology, which are using the information theory measures in their calculations. As we have reviewed most of the existing information-theoretic methods in gene regulatory and metabolic networks in the first part of the review, so in the second part of our study, the application of information theory in other types of biological networks including protein–protein interaction and signaling networks will be surveyed.