Network

Phylogenetic reconstruction is of crucial importance to elucidate bacterial population structure, epidemiology and evolutionary histories. By far phylogenetic networks and trees are the most common approaches used for studying the evolutionary history of a bacterial population. However, concepts and methodology underlying phylogenetic reconstruction can be challenging to beginners. As such, I share my notes on relevant literature in this post to address these obstacles. In particular, I compare different kinds of phylogenetic networks to show their pros and cons under various conditions.

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 construction^{1, 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.