YeastHybridModelingFramework

Integration of a Boolean nutrient signaling network with an enzyme-constrained model of S. cerevisiae's metabolism.

This repository contains all the necessary data, model files and scripts for reproducing the results on the publication L. Österberg, I. Domenzain, J.Münch, J.Nielsen, S. Hohmann, M. Cvijovic A novel yeast hybrid modeling framework integrating Boolean and enzyme-constrained networks enables exploration of the interplay between signaling and metabolism (2021) PLoS Comput Biol 17(4): e1008891. https://doi.org/10.1371/journal.pcbi.1008891

• Abstract

The interplay between nutrient-induced signaling and metabolism plays an important role in maintaining homeostasis and malfunction of this interplay has been implicated in many different human diseases such as obesity, type 2 diabetes, cancer and neurological disorders. Therefore, unravelling the role of nutrients as signaling molecules and metabolites as well as their interconnectivity may provide a deeper understanding of how these conditions occur. Both signalling and metabolism have been extensively studied using various systems biology approaches. However, they are mainly studied individually and in addition current models lack both the complexity of the dynamics and the effects of the crosstalk in the signaling system. To gain a better understanding of the interconnectivity between nutrient signaling and metabolism in Eukaryotes, we developed a hybrid model by combining Boolean, describing the signalling layer, and enzyme constrained models accounting for metabolism using a regulatory network as a link for the yeast Saccharomyces cerevisiae. The model was capable of reproducing the regulatory effects that are associated with the Crabtree effect and glucose repression. We show that using this methodology one can investigate intrinsically different systems, such as signaling and metabolism, in the same model and gain insight into how the interplay between them can have non-trivial effects by showing a connection between Snf1 signaling and chronological lifespan by the regulation of NDE and NDI usage in respiring conditions. In addition, the model showed that during fermentation, enzyme utilization is the more important factor governing the protein allocation, while in low glucose conditions robustness and control is prioritized.

• Reference:

L. Österberg, I. Domenzain, J.Münch, J.Nielsen, S. Hohmann, M. Cvijovic A novel yeast hybrid modeling framework integrating Boolean and enzyme-constrained networks enables exploration of the interplay between signaling and metabolism (2021) PLoS Comput Biol 17(4): e1008891. https://doi.org/10.1371/journal.pcbi.1008891

• Last update: 2020-09-17

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

Installation

Required software:

• A functional Matlab installation (MATLAB_2019b or higher).
• RAVEN Toolbox 2 for MATLAB.
• libSBML MATLAB API (version 5.16.0 is recommended).
• Gurobi Optimizer for MATLAB.

Installation Instructions

• Clone the master branch from cvijoviclab GitHub.
• Add the directory to your Matlab path, instructions here.

Run simulations

• code/Boolean/main.m - Run the Boolean module
• code/Boolean/simulateKnockouts.m - Reproduce the knockout simulations in the Boolean model as described in paper.
• code/Hybrid/hybridSimulation.m - Run the hybrid model.

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.

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 devel.
• The person making the pull request and the one accepting the merge cannot be the same person.
• A merge from devel to the master branch invokes a new release.

Contributors

• Ìván Domenzain (@IVANDOMENZAIN), Chalmers University of Technology, Göteborg, Sweden
• Linnea Österberg (@linoste), Chalmers University of Technology, Göteborg, Sweden
• Julia Münch