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auxotrophrangeexpansionmodel

https://github.com/simonvanvliet/auxotrophrangeexpansionmodel

Science Score: 57.0%

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Repository

Basic Info
  • Host: GitHub
  • Owner: simonvanvliet
  • License: other
  • Language: Jupyter Notebook
  • Default Branch: main
  • Size: 21.6 MB
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  • Stars: 0
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  • Releases: 5
Created over 2 years ago · Last pushed 11 months ago
Metadata Files
Readme Citation

README.md

Auxotroph Range Expansion Model

Model code for: Engineering microbial consortia: uptake and leakage rate differentially shape community arrangement and composition.
Estelle Pignon [1], Gábor Holló [1], Théodora Steiner [1], Simon van Vliet [1,2], Yolanda Schaerli [1]
[1] Department of Fundamental Microbiology, University of Lausanne.
[2] Biozentrum, University of Basel.

Installation

Create conda environment using provided environment.yml file. i.e. use conda env create -f environment.yml (install time several minutes)

Content

  • Experimental-datafiles folder. Contains text files with experimental measurements.
    • growth_rates.txt: experimentally measured growth rate of all strains, in batch culture
    • community_data.csv: measurements of range expansion patterns (equilibrium frequency, sector size, overall growth)
  • Processed-datafiles folder. Contains model output. All files are created by running code below.
    • communitydatamean.csv: processed format of data in communitydata.csv (data averaged over replicates), output from fitparametersrangeexpansion.ipynb
    • fitparameters.txt: fitted model parameters, output from fitparametersrangeexpansion.ipynb
    • predictionseqfraction.csv: model predictions, output from plotmodelpredictionseqfraction.ipynb
    • predictionssectorwidth.csv: model predictions, output from plotmodelpredictionssectorWidth.ipynb
  • community.py: python code of community class, implements community predictions using analytical equations from S1 Text and S2 text from Ref 1.
  • fitparametersrange_expansion.ipynb: jupyter notebook used to fit model parameters
  • plotmodelpredictions_eqfraction.ipynb: jupyter notebook used to make model predictions for community composition
  • plotmodelpredictions_sectorWidth.ipynb: jupyter notebook used to make model predictions for sector width

Usage

  1. run fitparametersrangeexpansion.ipynb to refit model parameters (runtime ~1min), saves fitted parameters to Processed-datafiles/fitparameters.txt.
  2. run plotmodelpredictions_eqfraction.ipynb to recreate model predictions for community composition (runtime ~1min), saves figures to the Figures subfolder.
  3. run plotmodelpredictions_sectorWidth.ipynb to recreate model predictions for sector width (runtime ~1min), saves figures to the Figures subfolder.

[Ref 1]: van Vliet S, Hauert C, Fridberg K, Ackermann M, Dal Co A (2022) Global dynamics of microbial communities emerge from local interaction rules. PLOS Computational Biology 18(3): e1009877. doi.org/10.1371/journal.pcbi.1009877

Owner

  • Name: Simon van Vliet
  • Login: simonvanvliet
  • Kind: user
  • Location: Basel
  • Company: Biozentrum, University of Basel

Project Leader in Systems Biology at Biozentrum, University Basel

Citation (CITATION.cff) 

cff-version: 1.2.0
title: >-
  Code for: Engineering microbial consortia: uptake and leakage rate differentially shape community arrangement and composition.
message: >-
  If you use this code, please cite it using the metadata from this file.
type: software
authors:
  - given-names: Simon
    family-names: van Vliet
    email: simon.vanvliet@unibas.ch
    affiliation: University of Basel and University of Lausanne
    orcid: 'https://orcid.org/0000-0003-2532-483X'
  - given-names: Estelle
    family-names: Pignon
    affiliation: University of Lausanne
  - given-names: Yolanda
    family-names: Schaerli
    email: yolanda.schaerli@unil.ch
    affiliation: University of Lausanne
    orcid: 'https://orcid.org/0000-0002-9083-7343'
# identifiers:
#   - type: doi
#     value: 
#     description: Preprint
abstract: >-
  This repository contains the model code used in the following
  publication:

  Engineering microbial consortia: uptake and leakage rate differentially shape community arrangement and composition.  
  Estelle Pignon [1], Gábor Holló [1], Théodora Steiner [1], Simon van Vliet [1,2], Yolanda Schaerli [1]  
  [1] Department of Fundamental Microbiology, University of Lausanne.  
  [2] Biozentrum, University of Basel.

  Model developed by Simon van Vliet
  Data by Estelle Pignon and Yolanda Schaerli

license: MIT

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Dependencies

environment.yml conda
  • ipykernel
  • jupyterlab
  • matplotlib
  • nb_conda_kernels
  • numba
  • pandas
  • pathlib
  • pip
  • python 3.10.*
  • scipy
  • seaborn