Cooperative Tradeoff Analysis of Consortia for Plant-Based Fermentation

Genome-scale metabolic modeling of lactic acid bacteria (LAB) consortia in legume-based fermentation. Master's project, CEB, Universidade do Minho.

This repository contains the jupyter notebooks, notes, results and supporting materials developed for a bioinformatics project focused on modeling lactic acid bacteria (LAB) consortium in legume-based fermentation systems.

Project Overview

The project aims to use genome-scale metabolic models (GSMMs) to study microbial interactions in plant-based matrices, with a focus on chickpea and fava bean fermentations. The main goal is to investigate how selected LAB strains may contribute to the mitigation of off-flavors and to the identification of favorable fermentation conditions through computational modeling.

The workflow combines:

• curation and standardization of GEMs;
• definition of legume-based growth media;
• interaction analysis using SMETANA;
• community simulation using MICOM.

Objectives

1. GEM curation — curate and standardize iLP728 and Koduru2022, evaluating quality (mass balance, blocked reactions, biomass consistency) with memote before and after correction.
2. Legume medium construction — translate chickpea and fava bean composition (sugars, amino acids, organic acids, lipids) into exchange bounds in COBRApy, using literature values and an approximation of the Sauer equation.
3. Interaction analysis — screen pairwise cross-feeding potential between the two LAB with SMETANA.
4. Community modeling — simulate the LAB consortium in both legume matrices with MICOM, comparing community behavior against solo-FBA baselines and assessing sensitivity to relative abundance.
5. Off-flavor analysis — interpret predicted flux distributions for compounds tied to known off-flavor pathways and evaluate which interactions plausibly contribute to mitigation.

Folder Organization & Contents

docs/ Final deliverables: the final article (Artigo_Final_pg59766.pdf, LNCS format), the intermediate report submitted earlier in the project (Artigo_Intercalar_pg59766.pdf), and presentation slides (ppt_SaraSousa_pg59766.pdf).

models/raw/ GSMMs as originally published/downloaded, before any curation. Kept for traceability.

models/curated/ Curated and harmonized versions of iLP728 and Koduru2022 used in all downstream analyses.

notebooks/ All analysis code, one notebook per objective:

• obj1_* notebooks curate the two GEMs and document why the third candidate strain was excluded;
• obj2 builds the chickpea/fava bean media;
• obj3 runs SMETANA;
• obj4_obj5 runs MICOM community simulations and the off-flavor flux analysis.

reports/memote/ Automated model quality reports generated by memote. _v2 files are post-curation reports; files without _v2 are pre-curation.

results/ Numerical outputs and figures from the notebooks, named by objective. obj4_* covers community growth rates, abundance sweeps, cross-feeding predictions and solo-vs-consortium comparisons. obj5_* covers the off-flavor perturbation analysis. smetana_* covers the three SMETANA interaction scores (MU, MIP, MRO).

Root files legume_medium1_v2.py and utils.py are shared Python modules imported by the notebooks — medium construction logic and general helper functions, respectively.

How to Run

1. Download/clone the repository, keeping the full folder structure inside one parent folder.

2. Set up the environment, installing all the necessary packages.

3. Install Gurobi.

4. Run notebooks in order from notebooks/: obj1_* → obj2_* → obj3_* → obj4_obj5_*. Each depends on outputs from the previous one.

Tools and Technologies

• Python, COBRApy, Genome-scale metabolic models (GSMMs), SMETANA, MICOM, ReFramed

Author

Sara Sousa (PG59766) Supervisor: Óscar Dias