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Download Computational Modelling Of Gene Regulatory Networks: A Primer eBook

by Hamid Bolouri

Download Computational Modelling Of Gene Regulatory Networks: A Primer eBook
ISBN:
1848162219
Author:
Hamid Bolouri
Category:
Medicine & Health Sciences
Language:
English
Publisher:
Imperial College Pr; 1 edition (September 1, 2008)
Pages:
340 pages
EPUB book:
1978 kb
FB2 book:
1807 kb
DJVU:
1579 kb
Other formats
lrf txt azw lrf
Rating:
4.3
Votes:
714


This book serves as an introduction to the myriad computational approaches to gene regulatory modeling and analysis, and is written specifically with experimental biologists in mind. Mathematical jargon is avoided and explanations are given in intuitive terms.

This book serves as an introduction to the myriad computational approaches to gene regulatory modeling and analysis, and is written specifically with experimental biologists in mind. In cases where equations are unavoidable, they are derived from first principles or, at the very least, an intuitive description is provided. Extensive examples and a large number of model descriptions are provided for use in both classroom exercises as well as self-guided exploration and learning.

Use features like bookmarks, note taking and highlighting while reading Computational Modeling of Gene Regulatory Networks - A Primer. This book has three parts: the first is the introduction of modeling (Ch1-5); the second is about various models of regulation (Ch6-12), including implicit models, single-cell stochastic models, mass-action kinetics models, boolean models, Bayesian models, etc; the last part is about misc aspects of modeling (Ch13-22). They are well organized and coherent. The text is clear and easy to understand (I am not a native speaker), and the book is written for those who are suffering from math-phobia.

Modeling Biochemical Networks. In the present article, we introduce a new method for identifying a set of extreme regulatory pathways by using structural equations as a tool for modeling genetic networks. The first part of the book is concerned with models of gene regulation, including protein-DNA and DNA-DNA interactions. The method, first of all, generates data on reaction flows in a pathway.

Start by marking Computational Modeling Of Gene Regulatory Networks A Primer as Want to Read .

Start by marking Computational Modeling Of Gene Regulatory Networks A Primer as Want to Read: Want to Read savin. ant to Read. In cases where equations are unavoidable, they are derived from first principles or, at the very least, an This book serves as an introduction to the myriad computational approaches to gene regulatory modeling and analysis, and is written specifically with experimental biologists in mind.

Graphical Representations of Gene Regulatory Networks. Implicit Modeling via Interaction Network Maps. Books related to Computational Modeling of Gene Regulatory Networks - A Primer. The Biochemical Basis of Gene Regulation. A Single-Cell Model of Transcriptional Regulation. Simplified Models: Mass-Action Kinetics. Simplified Models: Boolean and Multi-valued Logic. Simplified Models: Bayesian Networks. The Relationship between Logic and Bayesian Networks. Network Inference in Practice. 39. Desirable features of computational GRN representations

Graphical Representations of Gene Regulatory Networks. Desirable features of computational GRN representations 39 Graphical representation of GRN activity in multiple compartments. 46. 6. 49. Data interpretation through implicit modeling.

The book, intended as a primer for both theoretical and experimental biologists, is organized in two parts: models of gene activity and models of interactions among gene .

The book, intended as a primer for both theoretical and experimental biologists, is organized in two parts: models of gene activity and models of interactions among gene products. Modeling examples are provided at several scales for each subject. Each chapter includes an overview of the biological system in question and extensive references to important work in the area.

Artificial Neural Networks have proven to be a useful form of parallel processing of information that employs principles from the organization of the brain.

The paper addresses these questions through dynamic interaction network (DIN) modelling: first, at a genetic level, a Gene Regulatory Network (GRN) model can be created from a time series gene expression data; second, at a cognitive level, a DIN can be created from a time series of data (. LFP/EEG data) related to perceptual or cognitive functions; and third, a DIN. model can be developed for cross-level dynamic interactions, . between GRN and brain signals measured as LFP/EEG. Artificial Neural Networks have proven to be a useful form of parallel processing of information that employs principles from the organization of the brain. Modeling examples are provided at several scales for each subject

The book, intended as a primer for both theoretical and experimental biologists, is organized in two parts: models of gene activity and models of interactions among gene products.

biological organization. The book is organized into two parts: Part I. Modeling Genetic Networks and Part II. Modeling Biochemical Networks. They include logical and probabilistic approaches, which are applied to problems including prokaryotic and eukaryotic systems.

This book serves as an introduction to the myriad computational approaches to gene regulatory modeling and analysis, and is written specifically with experimental biologists in mind. Mathematical jargon is avoided and explanations are given in intuitive terms. In cases where equations are unavoidable, they are derived from first principles or, at the very least, an intuitive description is provided. Extensive examples and a large number of model descriptions are provided for use in both classroom exercises as well as self-guided exploration and learning. As such, the book is ideal for self-learning and also as the basis of a semester-long course for undergraduate and graduate students in molecular biology, bioengineering, genome sciences, or systems biology.
  • Ylal
I have bought and read many systems biology books.This is one of my favourites
Even if the mathematical level is not so high the book is very practical What is also very important
is the fact that every single software available is presented
Highly recommended
  • krot
it is very good.fast and excellent
  • Yellow Judge
There are quite a few books about systems biology and gene regulatory networks, most of which are very disappointing. I find only books written by scientists who are really working in this field are good. If you are not sure about one book, check the author's publications!

On the way to the library to borrow this book, I was thinking, among ~10 books I have read, I would only recommend Eric Davidson's book (biology aspects) and Uri Alon's book (mathematics aspects). After briefly reading this one, I think this book would also be on my recommendation list as a practical guide. It is very funny to find that this author also recommended those two books.

This book has three parts: the first is the introduction of modeling (Ch1-5); the second is about various models of regulation (Ch6-12), including implicit models, single-cell stochastic models, mass-action kinetics models, boolean models, Bayesian models, etc; the last part is about misc aspects of modeling (Ch13-22). They are well organized and coherent. The text is clear and easy to understand (I am not a native speaker), and the book is written for those who are suffering from math-phobia. The best part is, there are many links to free softwares and examples codes of models, so that you can play these models immediately. That's why I take this book as a practical guide in my recommendation list.

A complaint: figures have no numbers and legends; formulas look amateur. I understand that the author doesn't want to intimidate common readers. But these annoy me a little bit.

In a summary, a must-have book for systems biology and gene regulatory networks.