GEMS — Genomic & Experimental Metabolic Suite

GEMS is an end-to-end platform for fungal metabolic model reconstruction, ML-driven growth-condition optimisation, and geometry-aware optimisation design using polytope sampling. It combines a ModelSEED-based genome-scale model (GEM) pipeline, a trained multi-target regressor, and a convex-geometry analysis layer for four industrial fungal strains.

Overview

GEMS has three integrated components:

Component	Purpose
GEM Pipeline	Protein FASTA → draft metabolic model → gapfill → FBA analysis → validation
ML Recommender	Historical growth data (online learning) → train Random Forest / XGBoost / LightGBM → recommend optimal media conditions
Experimental / Geometry Aware	Fungal GEM + scenario generator → polytope sampling → geometry features → surrogate ML → industrial ranking

All three components are accessible through a single Streamlit UI and a FastAPI backend.

Architecture

┌─────────────────────────────────────────────────────────────────────┐
│                  Streamlit UI  (frontend_app.py)                   │
│  ┌───────────────┐   ┌──────────────────────┐  ┌───────────────┐  │
│  │  GEM Pipeline │   │   ML Recommender     │  │  Experimental │  │
│  │  Tab          │   │   Tab                │  │  Analysis Tab │  │
│  └───────┬───────┘   └──────────┬───────────┘  └───────┬───────┘  │
└──────────┼──────────────────────┼─────────────────────┼────────────┘
           │ HTTP (REST)          │ Direct Python        │ Direct Python
           ▼                      ▼                      ▼
┌─────────────────────┐   ┌────────────────────────┐  ┌──────────────────────┐
│  FastAPI Backend    │   │  ML Backend (backend/) │  │  Experimental/       │
│  (backend/main.py)  │   │  model_trainer.py      │  │  Polytopes/          │
│                     │   │  recommender.py        │  │  dataset_builder.py  │
│  POST /run          │   │  retrainer.py          │  │  train_model.py      │
│  POST /run/custom   │   │  data_ingestion.py     │  │  postprocess_scores.py│
│  GET  /health       │   └────────────────────────┘  └──────────────────────┘
└──────────┬──────────┘
           │ subprocess
           ▼
┌─────────────────────────────────────────────────┐
│              GEM Pipeline (scripts/ + src/)     │
│  run_mvp_pipeline.py  →  analyze_mvp.py         │
│  → validate_mvp.py                              │
└─────────────────────────────────────────────────┘

Project Structure

GEMS/
├── frontend_app.py            # Streamlit UI — GEM Pipeline + ML Recommender + Experimental tabs
├── requirements.txt           # Python dependencies
├── installation.txt           # Step-by-step setup and pipeline walkthrough
├── USAGE.md                   # Detailed usage examples
├── ARCHITECTURE.md            # In-depth architecture notes
│
├── backend/                   # ML recommender + API orchestration
│   ├── main.py                # FastAPI app — /run, /run/custom, /health
│   ├── pipeline_runner.py     # PipelineRunner: orchestrates MVP pipeline steps
│   ├── config.py              # Paths, feature/target columns, model hyperparams
│   ├── data_loader.py         # Load / save combined training dataset
│   ├── feature_engineering.py # Encoders, scalers, sample weight computation
│   ├── model_trainer.py       # Train RF / XGBoost / LightGBM; CV; persistence
│   ├── recommender.py         # Generate Exploit + Explore condition recommendations
│   ├── retrainer.py           # Adaptive retraining with round tracking
│   ├── data_ingestion.py      # Validate and ingest new wet-lab CSV results
│   ├── lab_exporter.py        # Export recommendations to Excel lab sheets
│   └── __init__.py
│
├── scripts/                   # CLI entry points for the GEM pipeline
│   ├── run_mvp_pipeline.py    # Step 1 — build draft model, gapfill, COBRA inspect
│   ├── analyze_mvp.py         # Steps 2–4 — theoretical / preset / custom analysis
│   ├── validate_mvp.py        # Step 5 — FBA, dead-ends, FVA, gene essentiality
│   ├── build_draft_model.py   # Standalone draft-model builder
│   ├── gapfill_and_export_model.py
│   ├── inspect_with_cobra.py
│   ├── screen_media.py
│   ├── diagnose_exchange_space.py
│   ├── debug_growth.py
│   ├── run_oracle_growth.py
│   ├── screen_oracle_medium.py
│   ├── benchmark_bio2.py
│   ├── inspect_oracle_condition.py
│   ├── first_modelseed_step.py
│   ├── prepare_input.py
│   └── compare_template_runs.py
│
├── src/                       # Core GEM pipeline library
│   ├── paths.py               # Canonical path constants (PROJECT_ROOT, MODELS_DIR, …)
│   ├── reconstruction.py      # MSBuilder draft-model construction
│   ├── template_loader.py     # Load built-in or local ModelSEED templates
│   ├── gapfill.py             # Best-effort minimal gapfilling
│   ├── export_model.py        # SBML / JSON model export helpers
│   ├── cobra_loader.py        # Load COBRA model from directory
│   ├── cobra_inspect.py       # FBA, exchange table, baseline optimization
│   ├── cobra_outputs.py       # Save COBRA inspection outputs
│   ├── cobra_debug.py         # Debug utilities for COBRA models
│   ├── mvp_analysis.py        # Theoretical / preset / custom condition analysis
│   ├── mvp_outputs.py         # Save all MVP analysis outputs + plots
│   ├── validation.py          # Dead-end analysis, exchange FVA, gene essentiality
│   ├── validation_outputs.py  # Save validation dashboard and summary files
│   ├── media_screen.py        # First-pass media screening
│   ├── media_outputs.py       # Save media screen outputs
│   ├── exchange_diagnostics.py
│   ├── exchange_diagnostic_outputs.py
│   ├── oracle_growth.py       # Oracle growth check
│   ├── oracle_medium.py       # Oracle-derived debug media
│   ├── oracle_medium_outputs.py
│   ├── bio2_benchmark.py      # Benchmark bio2 reaction rate
│   ├── bio2_benchmark_outputs.py
│   ├── modelseed_step.py      # ModelSEED first-pass step helpers
│   ├── input_parser.py        # Detect protein FASTA / genome FASTA / accession input
│   ├── model_io.py            # Save model summary text/JSON
│   ├── plot_utils.py          # Ranked bar chart plotting helpers
│   ├── report_utils.py        # Plain-text report builders
│   ├── logging_utils.py       # Configured logger factory
│   └── __init__.py
│
├── Experimental/              # Geometry-aware fermentation optimisation (polytope module)
│   ├── README.md              # Experimental module documentation
│   ├── A_oryzae_optimized.xml # Aspergillus oryzae GEM (SBML) used for simulations
│   ├── scenarios_fungi.json   # Fermentation scenario definitions (nutrients, T, pH, mixing)
│   ├── scenarios.json         # Additional scenarios (standard exchange reaction names)
│   ├── dataset_builder.py     # Main engine: FBA → FVA → PolyRound → polytope sampling → features
│   ├── scenario_generator_adaptive.py  # Adaptive explore/exploit scenario generator
│   ├── train_model.py         # Train Random Forest surrogate on dataset.csv
│   ├── rank_scenarios.py      # Rank scenarios by predicted overall_rank_score
│   ├── postprocess_scores.py  # Compute economic, morphology, meatiness, industrial scores
│   ├── rank_scenarios_industrial.py   # Rank by industrial_score
│   ├── feature_importance.py  # Feature importance from trained surrogate model
│   ├── top_region_summary.py  # Summarise top-performing scenario region (medians, ranges)
│   ├── plot_pareto.py         # Pareto plot: growth vs byproduct burden
│   ├── plot_industrial_tradeoff.py    # Industrial score vs growth scatter + trend line
│   ├── plot_geometry_vs_growth.py     # 3-panel: geometry/byproduct/validation plots
│   ├── reactions.py           # Search model reactions by keyword
│   ├── test_fungal_model.py   # Verify GEM loading and biomass reaction
│   ├── ml_pipeline.py         # ML pipeline utility
│   └── Results A_oryzae/      # Pre-computed results for Aspergillus oryzae
│       ├── dataset.csv                            # Raw FBA + geometry dataset
│       ├── dataset_postprocessed.csv              # With industrial scores
│       ├── model.pkl                              # Trained surrogate model
│       ├── feature_importances.csv
│       ├── predicted_ranked_scenarios.csv
│       ├── predicted_ranked_scenarios_industrial.csv
│       ├── top_region_summary.txt
│       ├── pareto_growth_vs_byproduct.png
│       └── plot_industrial_tradeoff.png
│
├── polytopes/                 # Mirror of Experimental/ (identical content)
│
├── data/
│   ├── synthetic_fungal_growth_dataset.csv   # 2,000-row synthetic training set
│   ├── intermediate/          # Combined dataset, encoded features (auto-generated)
│   ├── models/                # GEM model output directories + ML model checkpoints
│   └── raw/uploads/           # Uploaded protein FASTA files
│
├── config/
│   └── media_library.yml      # Named media definitions for screening
│
├── ModelSEEDDatabase/         # Local copy of the ModelSEED reference database
│   ├── Templates/
│   │   ├── Fungi/Fungi.json   # Fungal reconstruction template (local source)
│   │   ├── Core/              # Core template
│   │   └── …                  # GramNeg, GramPos, Human, Plant, etc.
│   ├── Biochemistry/          # Compounds, reactions, aliases, structures
│   └── Annotations/           # Complexes, Roles
│
└── docs/                      # Pipeline diagrams and template comparison reports

Backend

The backend/ package contains two distinct responsibilities:

1. FastAPI Pipeline API (`main.py` + `pipeline_runner.py`)

Endpoint	Method	Description
`/run`	POST	Upload a `.faa` file; run the 4-step MVP pipeline; return `model_id` + step status
`/run/custom`	POST	Run an optional custom-condition analysis on an existing model
`/health`	GET	Liveness check

PipelineRunner (in pipeline_runner.py) orchestrates:

run_mvp_pipeline.py — build draft model
analyze_mvp.py --mode theoretical
analyze_mvp.py --mode preset
validate_mvp.py --mode theoretical_upper_bound

Each step is a child subprocess. If a step fails its returncode, the pipeline stops and returns partial results.

2. ML Recommender Backend

Module	Responsibility
`config.py`	Feature columns, target names, model hyperparameters, directory paths
`data_loader.py`	Load/save the combined (synthetic + real) training CSV
`feature_engineering.py`	Label-encode categoricals, min-max scale numerics, compute sample weights
`model_trainer.py`	Cross-validate Random Forest / XGBoost / LightGBM; select best; persist with `joblib`
`recommender.py`	Sample 2,000 candidate conditions; predict all targets; return top-N exploit + explore
`retrainer.py`	Adaptive retraining loop with round tracking (`retrain_log.json`)
`data_ingestion.py`	Validate lab CSV schema; rename columns; recompute composite score; append to combined dataset
`lab_exporter.py`	Render recommendations into an Excel workbook for the wet lab

Frontend

frontend_app.py is a Streamlit single-page application with three top-level tabs:

🧫 GEM Pipeline Tab

Upload & Run — upload a .faa file, choose template (Core / Fungal), toggle RAST, click ▶ Run Pipeline
View Results — model selector dropdown; six sub-tabs:
1. Draft Model — mvp_summary.json metrics card + mode comparison plot
2. Theoretical Upper Bound — FBA benchmark plot, condition table, JSON summary
3. Preset Conditions — ranked bar chart, conditions table, text summary
4. Custom Condition — run and display a user-defined media condition
5. Validation — dashboard image, FBA status, dead-end metabolites, exchange FVA, gene essentiality
6. Full Pipeline Files — all 12 intermediate file outputs in pipeline order

🤖 ML Recommender Tab

Train — train all 3 model types × 4 targets; display CV R² scores
Recommendations — select strain, get top-N exploit + explore conditions; download Excel lab sheet
Upload & Retrain — upload a filled lab results CSV, ingest, retrain with updated data

🔬 Experimental Analysis Tab (Polytope Module)

Visualises results from the geometry-aware fermentation optimisation pipeline in Experimental/:

Results Overview — display pre-computed Results A_oryzae/ outputs
Scenario Rankings — tabular view of predicted_ranked_scenarios.csv and predicted_ranked_scenarios_industrial.csv
Pareto Analysis — Pareto plot image (growth vs byproduct burden)
Industrial Tradeoff — industrial score vs growth scatter with trend line
Geometry vs Growth — 3-panel figure: feasible space log-volume / byproduct pressure / ML validation
Feature Importances — bar chart of which variables drive the surrogate model score
Top Region Summary — median and range of the top-performing scenario cluster

GEM Pipeline

The MVP pipeline runs in a fixed order via scripts/run_mvp_pipeline.py:

Protein FASTA (.faa)
        │
        ▼
  MSGenome.from_fasta()         — load features
        │
        ▼
  MSBuilder.build_metabolic_model()   — draft reconstruction
        │  (template: Core builtin  OR  Fungi local)
        ▼
  gapfill_model_minimally()     — best-effort gapfill on bio1
        │
        ▼
  save_model_sbml_if_possible() — export model.xml (SBML) or model.json
        │
        ▼
  load_cobra_model()            — load via COBRApy
  run_baseline_optimization()   — FBA baseline
  get_exchange_table()          — exchange metabolite fluxes
        │
        ▼
  save_mvp_summary()            — mvp_summary.json / .txt

Analysis steps (run after step 1):

Script	Mode	Output
`analyze_mvp.py`	`theoretical`	theoretical_upper_bound.{json,txt,png,csv}
`analyze_mvp.py`	`preset`	preset_conditions.{json,csv,txt,png}
`analyze_mvp.py`	`custom`	custom_condition_NAME.{json,txt,png}
`validate_mvp.py`	`theoretical_upper_bound`	validation dashboard, dead-end CSV, FVA CSV, gene essentiality CSV

Templates

Label	`--template-name`	`--template-source`	File
Core Template (built-in)	`template_core`	`builtin`	modelseedpy built-in
Fungal Template (local)	`fungi`	`local`	`ModelSEEDDatabase/Templates/Fungi/Fungi.json`

Experimental Analysis — Polytope Module

Located in Experimental/ (and mirrored in polytopes/), this module implements a geometry-aware, biologically grounded optimisation framework for fermentation design.

Concept

Instead of optimising a single metabolic solution, this system:

Explores the full feasible metabolic flux space (solution polytope) for a fungal GEM
Extracts geometric and biological features from that space
Trains a surrogate ML model that learns how environmental conditions shape performance
Identifies robust and efficient operating regions for fermentation

Pipeline

scenarios_fungi.json          — fermentation scenario definitions
        │
        ▼
  dataset_builder.py
  ├── cobra.io.read_sbml_model(A_oryzae_optimized.xml)
  ├── apply_model_specific_medium(scenario)
  ├── model.optimize()                  — FBA
  ├── flux_variability_analysis()       — FVA range
  ├── polyround_preprocess()            — convert SBML → polytope (Ax ≤ b)
  ├── PolytopeSampler.sample_from_polytope()  — MCMC interior sampling
  ├── back_transform()                  — recover flux vectors
  └── extract geometry features         — log-volume, anisotropy, flux_std
        │
        ▼
  dataset.csv                           — FBA + geometry features per scenario
        │
        ▼
  train_model.py                        — Random Forest surrogate on overall_rank_score
        │
        ▼
  rank_scenarios.py                     — predicted_ranked_scenarios.csv
        │
        ▼
  postprocess_scores.py
  ├── economic scores (substrate + mixing cost / yield)
  ├── morphology score (growth × mixing × pH penalty)
  └── meatiness score (growth + biomass + morphology − byproducts)
        │
        ▼
  dataset_postprocessed.csv             — enhanced with industrial scores
        │
        ▼
  rank_scenarios_industrial.py          — predicted_ranked_scenarios_industrial.csv

Scripts Reference

Script	Description	Key Output
`dataset_builder.py`	Main engine — FBA + FVA + polytope sampling + feature extraction	`results/dataset.csv`
`scenario_generator_adaptive.py`	Generate uniform explore + local exploit scenarios	`scenarios.json`
`train_model.py`	Train RandomForest surrogate; evaluate R² / MAE	`results/model.pkl`
`rank_scenarios.py`	Apply trained model; rank by predicted score	`results/predicted_ranked_scenarios.csv`
`postprocess_scores.py`	Compute economic, morphology, meatiness, industrial scores	`results/dataset_postprocessed.csv`
`rank_scenarios_industrial.py`	Rank by composite industrial score	`results/predicted_ranked_scenarios_industrial.csv`
`feature_importance.py`	Extract and display feature importances	`results/feature_importances.csv`
`top_region_summary.py`	Summarise top-performing scenario cluster (medians, ranges)	`results/top_region_summary.txt`
`plot_pareto.py`	Pareto view: growth vs total byproducts	`results/pareto_growth_vs_byproduct.png`
`plot_industrial_tradeoff.py`	Industrial score vs growth with trend line	`results/plot_industrial_tradeoff.png`
`plot_geometry_vs_growth.py`	3-panel: geometry / byproduct pressure / ML validation	`results/final_3panel_figure.png`
`test_fungal_model.py`	Verify GEM loads and biomass reaction exists	—
`reactions.py`	Search GEM reactions by keyword	—

Geometry Features Extracted

Feature	Description
`log_volume`	Log of polytope volume (sum of log eigenvalues of the flux covariance matrix)
`anisotropy_log`	Log of max eigenvalue / median eigenvalue — measures directionality of the flux space
`flux_std`	Mean standard deviation of flux samples — measures overall variability
`biomass_flux_mean`	Mean biomass flux across polytope samples
`biomass_std`	Standard deviation of biomass flux
`fva_range`	Mean FVA (min→max) range across all reactions

Industrial Score Components

Component	Weight	Description
Growth (FBA)	0.25	Raw FBA biomass rate
Biomass flux mean	0.15	Average biomass across polytope samples
Biomass yield	0.15	Growth / glucose_uptake
Economic score	0.20	1 − (substrate cost + mixing cost) / yield
Morphology score	0.15	Growth × mixing_fibrousness − pH penalty
Meatiness score	0.10	Growth + biomass + morphology − byproducts
Byproduct penalty	−0.20	Total byproduct excretion

Running the Experimental Pipeline

cd GEMS/Experimental

# 1. generate the Scenarios
python scenario_generator_adaptive.py

# 2. Build the dataset (requires PolyRound + PolytopeSampler)
python dataset_builder.py

# 3. Train the surrogate model
python train_model.py

# 4. Rank scenarios by overall score
python rank_scenarios.py

# 5. Add industrial scoring layer
python postprocess_scores.py
python rank_scenarios_industrial.py

# 6. Analyse and visualise
python feature_importance.py
python top_region_summary.py
python plot_pareto.py
python plot_industrial_tradeoff.py
python plot_geometry_vs_growth.py

Data

File	Description
`data/synthetic_fungal_growth_dataset.csv`	2,000 synthetic growth experiments across 4 strains; features include carbon source, nitrogen source, pH, temperature, RPM, inoculum size, nutrient concentrations
`data/intermediate/combined_dataset.csv`	Merged synthetic + real uploaded data (auto-generated after ingest)
`data/intermediate/features.pkl`	Fitted encoder/scaler pipeline (auto-generated after training)
`data/models/`	One directory per GEM model run; one directory per ML training run (`run_YYYYMMDD_HHMMSS/`)
`Experimental/Results A_oryzae/dataset.csv`	FBA + geometry features for A. oryzae simulated scenarios
`Experimental/Results A_oryzae/dataset_postprocessed.csv`	Enhanced dataset with industrial scores
`Experimental/Results A_oryzae/model.pkl`	Trained Random Forest surrogate for A. oryzae

Quick Start

# 1. Install dependencies
pip install -r GEMS/requirements.txt
pip install modelseedpy cobra fastapi uvicorn

# 2. Start the API server (from the GEMS/ directory)
cd GEMS
uvicorn backend.main:app --reload --port 8000

# 3. Start the Streamlit UI (separate terminal, from GEMS/ directory)
cd GEMS
streamlit run frontend_app.py

Navigate to http://localhost:8501 to access the UI.

CLI Usage

# Build a draft fungal model using the local Fungi template
python GEMS/scripts/run_mvp_pipeline.py \
  --input ncbi_dataset/data/GCA_000182925.2/protein.faa \
  --model-id fungi_test \
  --use-rast \
  --template-name fungi \
  --template-source local

# Run theoretical upper bound analysis
python GEMS/scripts/analyze_mvp.py \
  --model-dir GEMS/data/models/fungi_test \
  --mode theoretical

# Run preset conditions
python GEMS/scripts/analyze_mvp.py \
  --model-dir GEMS/data/models/fungi_test \
  --mode preset

# Run validation
python GEMS/scripts/validate_mvp.py \
  --model-dir GEMS/data/models/fungi_test \
  --mode theoretical_upper_bound \
  --biomass-reaction bio2

API Reference

`POST /run`

Upload a protein FASTA and run the full 4-step MVP pipeline.

Field	Type	Default	Description
`file`	`.faa` upload	required	Protein FASTA file
`use_rast`	bool	`false`	Annotate with RAST
`template_name`	string	`template_core`	`template_core` or `fungi`
`template_source`	string	`builtin`	`builtin` or `local`

Response: model_id, steps[] (name, returncode, stdout, stderr), all_succeeded

`POST /run/custom`

Run a single custom-condition analysis on an existing model.

Field	Type	Default	Description
`model_id`	string	required	Existing model directory name
`condition_name`	string	required	Output filename stem
`preset_seed`	string	`rich_debug_medium`	Starting preset
`metabolite_ids`	string	optional	Comma-separated metabolite IDs

Supported Fungal Strains

Works with any SBML file and any protein FASTA file.

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Repository files navigation

GEMS — Genomic & Experimental Metabolic Suite

Table of Contents

Overview

Architecture

Project Structure

Backend

1. FastAPI Pipeline API (`main.py` + `pipeline_runner.py`)

2. ML Recommender Backend

Frontend

🧫 GEM Pipeline Tab

🤖 ML Recommender Tab

🔬 Experimental Analysis Tab (Polytope Module)

GEM Pipeline

Templates

Experimental Analysis — Polytope Module

Concept

Pipeline

Scripts Reference

Geometry Features Extracted

Industrial Score Components

Running the Experimental Pipeline

Data

Quick Start

CLI Usage

API Reference

`POST /run`

`POST /run/custom`

Supported Fungal Strains

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Name		Name	Last commit message	Last commit date
Latest commit History 44 Commits
Experimental		Experimental
GCA_000184455.3		GCA_000184455.3
ModelSEEDDatabase		ModelSEEDDatabase
backend		backend
config		config
data		data
docs		docs
polytopes		polytopes
scripts		scripts
src		src
.gitignore		.gitignore
DEPENDENCIES.md		DEPENDENCIES.md
README.md		README.md
frontend_app.py		frontend_app.py
installation.txt		installation.txt
requirements.txt		requirements.txt

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GEMS — Genomic & Experimental Metabolic Suite

Table of Contents

Overview

Architecture

Project Structure

Backend

1. FastAPI Pipeline API (main.py + pipeline_runner.py)

2. ML Recommender Backend

Frontend

🧫 GEM Pipeline Tab

🤖 ML Recommender Tab

🔬 Experimental Analysis Tab (Polytope Module)

GEM Pipeline

Templates

Experimental Analysis — Polytope Module

Concept

Pipeline

Scripts Reference

Geometry Features Extracted

Industrial Score Components

Running the Experimental Pipeline

Data

Quick Start

CLI Usage

API Reference

POST /run

POST /run/custom

Supported Fungal Strains

About

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1. FastAPI Pipeline API (`main.py` + `pipeline_runner.py`)

`POST /run`

`POST /run/custom`

Packages