diverseYeasts_metabolism

Metabolic modelling simulation and analysis tools for diverse yeast species.

• Brief Description

This repository contains a collection of scripts for simulation, modification, data integration and visualization using Genome-scale mEtabolic Models (GEMs) for probing metabolic capabilities of yeast organisms.

• KeyWords

  Utilisation: GEMs reconstruction, FBA, metabolic engineering, data integration; Field: Constraint-based methods; Omic Source: NA

Last update: 2020-12-11

This repository is administered by @IVANDOMENZAIN, Division of Systems and Synthetic Biology, Department of Biology and Biological Engineering, Chalmers University of Technology

Installation

Required Software

• The scripts in this repository use the RAVEN toolbox for MATLAB (version 2.0) as a dependency. Alternatively, the models can also be directly used with the COBRA toolbox for MATLAB.

  Dependencies - Recommended Software

• Please see the RAVEN toolbox repository for dependencies regarding RAVEN.

  Installation Instructions

• Clone master branch from this GitHub repository.

  Reconstruction

• The models in this repository have been reconstructed based on homology of genes with S. cerevisiae and using the model YeastGEM as a template.

Development Guidelines

Anybody is welcome to contribute to the development of this modeling and simulation Toolbox, but please abide by the following guidelines.

Each function should start with a commented section describing the function and explaining the parameters. Existing functions can clarify what style should be used. When making any changes to an existing function (*.m-file), change the date and name of developer near the bottom of this commented section in the last modified line.

Bugfixes, new features and functions

• For any development, whether bugfixes, introducing new functions or new/updated features for existing functions: make a separate branch from devel and name the branch for instance after the function/feature you are fixing/developing. If you work on a fix, start the branch name with fix/, if you work on a feature, start the branch name with feat/. Examples: fix/format_reactions or feat/new_algorithms.
• Make commits to this branch while developing. Aim for backwards compatibility, and try to avoid very new MATLAB functions when possible, to accommodate users with older MATLAB versions.
• When you are happy with your new function/feature, make a pull request to the devel branch. Also, see Pull request below.

Semantic commits

Use semantic commit messages to make it easier to show what you are aiming to do:

• chore: updating binaries (model MATLAB structures), UniProt databases, physiology and protemics data files, etc.
• doc: updating documentation (in doc folder) or explanatory comments in functions.
• feat: new feature added, e.g. new function introduced / new parameters / new algorithm / etc.
• fix: bugfix.
• refactor: see code refactoring.
• style: minor format changes of functions (spaces, semi-colons, etc., no code change).

Examples:

feat: exportModel additional export to YAML
chore: update UniProt database for CENPK113-7D
fix: optimizeProb parsing results from Gurobi

More detailed explanation or comments can be left in the commit description.

Pull request

• No changes should be directly commited to the master or devel branches. Commits are made to side-branches, after which pull requests are made for merging with master or devel.
• The person making the pull request and the one accepting the merge cannot be the same person.
• A merge with the master branch invokes a new release.