OptAux

Files and scripts needed to reproduce the figures in "The genetic basis for adaptation of model-designed syntrophic co-cultures"

Docker

Project can be installed using docker by running from the docker folder

docker build -t optaux:everything .

this will install everything needed to reproduce most of the results outlined below including the community iJL1678b ME-model.

Reproducing Figures/Tables

OptAux simulations

Run python [optaux]/scripts_figures_and_tables/make_optaux_supplement.py

This runs OptAux algorithm in aerobic glucose minimal media conditions for all carbon containing exchange reactions in iJO1366. It will by default run OptAux for 4 competing_metabolite_uptake_thresholds (0, 0.01, 0.1, and 2) and output the results in supplement_1_optaux_solutions.xls as well as intermediate results as optaux_intermediate_trace_[threhold_value].xls

To process the results into an MSE summary spreadsheet and the set of EBC designs with the smallest number of knockouts, run output_optaux_summaries.xls

Relative abundance approximations

Running python output_relative_abundance_results.py will:

1. Check if abundance_by_characteristic_[pair_name].csv and abundance_by_coverage_[pair_name].csv are located in [optaux]/scripts_figures_and_tables/relative_abundance/tables. If not, these CSVs will be created using the information in the read Bowtie2 alignment BAM files for all sequencing samples (not provided for now) and breseq generated mutation calls found in [optaux]/optaux/resources/resequencing_data/AUX [pair_name] Mutation Table.csv

   • Some values in the Mutation Tables for the characteristic strain mutations are missing because breseq rounds high/low mutation frequencies to 100% or 0%, respectively. These values are filled in with their frequencies in relative_abundance.py

2. Output bar charts of the characteristic mutation/alignment coverage base approximations of relative strain abundances in [optaux]/scripts_figures_and_tables/relative_abundance/figures as well as a comparison of the predictions based on each method.

Duplications

Read coverages are plotted using the alignment files produced from breseq (Not provided due to filesize limits on github. They can be obtained by contacting cjlloyd@ucsd.edu for access to the alignment files or accessing the raw reads hosted on the Sequence Read Archive under accession no. SRP161177).

Running python output_duplications.py will:

1. Check if [optaux]/scripts_figures_and_tables/duplications/[pair_name]_coverage_dict.json exists. If not it will produce this dictionary using the bam files output from breseq.

2. Find the genes with >80% of their base pairs above the 1.25x fit mean cutoff. These are compiled and output in [optaux]/scripts_figures_and_tables/duplications/duplicated_genes

3. Output the plots used to create Figure 7 and the supplementary figures in [optaux]/scripts_figures_and_tables/duplications/

Community Modeling Sims and Plotting

The iJL1678b ME-model (constructed using COBRAme/ECOLIme v0.0.9) is provided as json files with two different k_eff parameter sets:

• iJL1678b.json: The model with default parameters

• iJL1678b_null_keffs.json: The model with k_effs values set to those obtained from Dividi et. al.. Metabolic k_effs not included in this dataset were imputed with the median value, 6.2 s^-1. Non-metabolic reactions outside the scope of this dataset were set to 65 s^-1 as in the default model.

The key results from the study can be reproduced with the following

1. Build iJL1678b-community models with: python [optuax]/optaux/me_community/make_me_communityl.py.

   • This will make community models based on the two ME-models described as well as community model with all k_effs set to 65 s^-1.

2. All of the simulations needed to reproduce Figures 8 and 9 can then be ran with: python run_community_me_sims.py

   • This code uses python's multiprocessesing functionality with 2 processes by default. To speed up these simulations, more processes can be used. A single simulation typically requires 4-8 gb of RAM to solve with qMINOS.
   • This will output the results into [optaux]/scripts_figures_and_tables/community_sims_output_[null/65/default]_keffs. Alternatively, tar.gz files are containing the output of these simulations are already included in this package. Note: If using these files instead of running new simulations you must run python unpack_community_me_sims.py to unpack the tar.gz files in order to plot.

To recreate Figure 9, part of Figure 8, and the supplementary community ME-figures:

3. To plot community growth rates for varying strain abundances run: python [optaux]/scripts_figures_and_tables/output_computed_community_growth_rates.py

4. To plot metabolite cross-feeding for varying strain abundances run: python [optaux]/scripts_figures_and_tables/output_computed_metabolite_crossfeeding.py

5. To plot community growth rates for substrate limited ME-model and M-model run: python [optaux]/scripts_figures_and_tables/output_glucose_limited_me_m_comparison.py

6. To output the supplement community growth comparison between steadycom and jointfba M-model: python [optaux]/scripts_figures_and_tables/output_steadycom_jointfba_comparison.py

Software

The following software and versions were used for publication:

• Python 3.6
• An MILP solver. The scripts here use Gurobi by default.
• COBRApy v0.5.11
• COBRAme v0.0.9
• ECOLIme v0.0.9
• solvemepy v1.0.1
  • Including the qMINOS solver
• pysam v0.14.1
• openpyxl v2.3.2
• pandas v0.22.0
• matplotlib v2.0.2
• numpy v1.14.2
• scipy v0.19.0
• Biopython v1.66
• Seaborn v0.7.1