Linux is a popular family of operating systems (OS) used in bioinformatics. Amongst its numerous distributions, Xubuntu is a lightweight derivative of ubuntu Linux, and aims to run on a machine with low system requirements. As a Windows user, I often need to switch to a Linux environment for program development and test. To this end, VirtualBox offers an easy-to-use but low-in-resources alternative to a dedicated physical machine or disc space (dual OS). This post records my key steps for setting up Xubuntu in VirtualBox for basic bioinformatic work.
The systems biology markup language (SBML) is a community-driven, software and platform independent standard for expressing and exchanging systems models between different simulation and analysis software. It is defined using the unified modelling language (UML) and represented using the extensible markup language (XML)1. The SBML does not aim to produce model files that can be readily read by humans, but to provide different software with a unified medium for exchanging models. Each piece of software can then translate imported models into its own internal format1. It is important to understand the SBML for metabolic modelling and engineering because this data language has quickly become the most popular standard of model files since its first publication in 20031.
In my first post of this tutorial, I have demonstrated basic ways to inspect a genome-scale metabolic model (GEM). Now, let’s get our hands dirty — to reconstruct a draft metabolic network from genome annotations, which is the starting point of the protocol proposed by Thiele and Palsson for bottom-up GEM construction1, 2. In this post, we will be using several bioinformatic tools to reconstruct draft metabolic networks from annotations of the fully resolved chromosomal genome of Clostridium beijerinckii str. NCIMB 8052 (KEGG organism code: cbe; PATRIC genome ID: 290402.41), a well-known butanol-producing microorganism. A GEM iCM925 of this strain has been published by Milne et al3.
This is my first post of a series of tutorials for constructing genome-scale metabolic models (GEMs) for single-cellular microbes. In this tutorial, I follow the protocol created by Heirendt et al. to demonstrate reading and visualisation of existing GEMs1.