Hydrocarbon and lipid microbiology protocols : statistics, data analysis, bioinformatics and modelling /

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Bibliographic Details
Imprint:	Berlin : Springer, 2016.
Description:	1 online resource (xii, 180 pages)
Language:	English
Series:	Springer protocols handbooks, 1949-2448 Springer protocols (Series)
Subject:	Lipids -- Laboratory manuals. Lipids -- Biotechnology -- Laboratory manuals. Hydrocarbons -- Laboratory manuals. Hydrocarbons. Lipids. Lipids -- Biotechnology. Laboratory Manual. Laboratory manuals. Laboratory manuals.
Format:	E-Resource Book
URL for this record:	http://pi.lib.uchicago.edu/1001/cat/bib/11265426

Hidden Bibliographic Details
Other authors / contributors:	McGenity, Terry J., editor. Timmis, K. N., editor. Nogales, Balbina, editor.
ISBN:	9783662493106 3662493101 9783662493113 366249311X 9783662570005 3662570009 9783662493090 3662493098
Digital file characteristics:	text file PDF
Notes:	Includes bibliographical references. Online resource; title from PDF title page (SpringerLink, viewed August 18, 2016).
Summary:	This volume covers protocols for in-silico approaches to hydrocarbon microbiology, including the selection and use of appropriate statistical tools for experimental design replication, data analysis, and computer-assisted approaches to data storage, management and utilisation. The application of algorithms to analyse the composition and function of microbial communities is presented, as are prediction tools for biodegradation and protein interactions. The basics of a major open-source programming language, Python, are explained.
Other form:	Print version: Hydrocarbon and lipid microbiology protocols. Berlin : Springer, 2016 9783662493090 3662493098
Standard no.:	10.1007/978-3-662-49310-6

Table of Contents:

Introduction to computer-assisted analysis in lipid and hydrocarbon microbiology
Application of Ecological Network Theory
Statistical tools for data analysis
Statistical tools for study design and replication
MG-RAST, a metagenomics service for analysis of microbial community structure and function
Using QIIME to evaluate the microbial communities within hydrocarbon environments
Biodegradation Prediction Tools
Predicting protein interactions
Syntax and Semantics of Coding in Python
Protocols for calculating reaction kinetics and thermodynamics
Modelling the environmental fate of hydrocarbons during bioremediation.

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