optithor 0.1.1

Linear-programming–based optimization of microbial growth media under elemental stoichiometric constraints.

OptiThor

OptiThor is a Python package for linear-programming-based optimization of microbial growth media under elemental stoichiometric constraints.

Given:

• a set of candidate compounds (identified by PubChem CIDs),
• elemental growth requirements (e.g. C, N, P, S, other trace metals),
• and a target biomass yield,

OptiThor computes the optimal compound concentrations that satisfy all elemental requirements while minimizing total compound mass.

The package ships with a pre-built compound database derived from PubChem, so no external data preparation is required for most use cases.

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Installation

pip install optithor

Python requirement: ≥ 3.10

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Quick check (successful installation)

from optithor import CompoundDb

db = CompoundDb().load()
print(db.head())

This confirms that the packaged compound database is available and readable.

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Real example: optimizing a defined medium

This is a real, end-to-end example using actual PubChem CIDs and real elemental requirements, identical in logic to examples/basic_usage.py.

from optithor import CompoundDb, optimize_medium
from optithor.config import SolverConfig
from optithor.types import ElementRequirement, MediumOptimizationInput
from optithor.reports import (
    build_selected_cids_table,
    build_optimization_tables,
)

repo = CompoundDb()

compound_cids = [
    "57429219", "5284359", "24643", "61482", "91618179",
    "5793", "5360315", "24480", "23672064", "4873",
    "23673662", "62640",
]

selected_df = build_selected_cids_table(
    compound_repository=repo,
    compound_cids=compound_cids,
    force_reload=False,
)

print("\n=== Selected compounds (from DB) ===")
print(selected_df.to_string(index=False))

reference_yields = {
    "C": 1, "N": 8, "S": 100, "P": 33, "K": 100,
    "Mg": 200, "Ca": 100, "Fe": 200,
    "Mn": 1e4, "Zn": 1e4, "Cu": 1e5, "Co": 1e5,
}

excess_factors = {
    "C": 1, "N": 3, "S": 5, "P": 5, "K": 5,
    "Mg": 5, "Ca": 10, "Fe": 10,
    "Mn": 20, "Zn": 20, "Cu": 20, "Co": 20,
}

required_elements = {
    e: ElementRequirement(
        reference_yield_g_cdw_per_g=reference_yields[e],
        excess_factor=excess_factors[e],
    )
    for e in reference_yields
}

medium_input = MediumOptimizationInput(
    compound_cids=compound_cids,
    required_elements=required_elements,
    max_dry_biomass_g_per_l=10.0,
)

result = optimize_medium(
    medium_input=medium_input,
    compound_repository=repo,
    solver_config=SolverConfig(),
)

print("\nSuccess:", result.success)
print("Message:", result.message)

tables = build_optimization_tables(
    medium_input=medium_input,
    result=result,
    compound_repository=repo,
    decimals=2,
    include_anhydrous_mass=False,
)

print("\n=== Compounds results table ===")
print(tables.compounds.to_string(index=False))

print("\n=== Element validation table ===")
print(tables.elements.to_string(index=False))

---

Running the example produces three tables.

(1) Selected compounds

This table lists the compounds found in the local database before optimization.

Purpose:

• Confirms that all requested PubChem CIDs exist in the database
• Shows the molecular formula and molar mass used internally
• Detects missing compounds early (before solving)

PubChem CID	PubChem Name	Formula	MW
57429219	Ammonium chloride monohydrate	ClH6NO	71.51
5284359	Calcium chloride	CaCl2	110.98
24643	Cobaltous chloride hexahydrate	Cl2CoH12O6	237.93
61482	Cupric chloride dihydrate	Cl2CuH4O2	170.48
91618179	Ferric chloride dihydrate	Cl3FeH4O2	198.23
5793	D-Glucose	C6H12O6	180.16
5360315	Magnesium chloride	Cl2Mg	95.21
24480	Manganese (II) chloride	Cl2Mn	125.84
23672064	Monosodium phosphate	H2NaO4P	119.98
4873	Potassium chloride	ClK	74.55
23673662	Sodium bisulfate monohydrate	H3NaO5S	138.08
62640	Zinc sulfate heptahydrate	H14O11SZn	287.60

If any CIDs are missing, they are reported explicitly and the optimization should not be run until resolved.

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(2) Compound results (optimized medium)

This table represents the final optimized medium recipe.

Key columns:

• Obtained Concentration - optimal amount per compound
• Unit - automatically scaled (g/L, mg/L, µg/L)
• Values satisfy all elemental constraints while minimizing total compound mass

PubChem CID	Compound	MW (g/mol)	Obtained Concentration	Unit
5360315	Magnesium chloride	95.21	979.33	mg/L
4873	Potassium chloride	74.55	953.37	mg/L
62640	Zinc sulfate heptahydrate	287.60	87.98	mg/L
24480	Manganese (II) chloride	125.84	45.81	mg/L
5793	D-Glucose	180.16	25.00	g/L
57429219	Ammonium chloride monohydrate	71.51	19.14	g/L
24643	Cobaltous chloride hexahydrate	237.93	8.07	mg/L
23672064	Monosodium phosphate	119.98	5.87	g/L
61482	Cupric chloride dihydrate	170.48	5.37	mg/L
5284359	Calcium chloride	110.98	2.77	g/L
23673662	Sodium bisulfate monohydrate	138.08	2.11	g/L
91618179	Ferric chloride dihydrate	198.23	1.77	g/L

Interpretation:

• High concentrations correspond to bulk nutrients (C, N, major salts)
• Low concentrations typically supply trace elements
• This is a mathematical optimum, not an experimentally validated recipe

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(3) Element validation table

This table verifies that all elemental constraints are satisfied.

Key columns:

• Reference Yield - biological assumption (g CDW per g element)
• Excess Factor - safety margin applied
• Required Mass - target elemental mass after excess
• Obtained Mass - mass supplied by the optimized medium
• Match (%) - agreement between required and obtained values

Element	Reference Yield (gCDW/g)	Excess Factor	Required Mass	Obtained Mass	Unit	Match (%)
C	1	1	10.00	10.00	g/L	100.0
N	8	3	3.75	3.75	g/L	100.0
P	33	5	1515.15	1515.15	mg/L	100.0
Ca	100	10	1000.00	1000.00	mg/L	100.0
S	100	5	500.00	500.00	mg/L	100.0
K	100	5	500.00	500.00	mg/L	100.0
Fe	200	10	500.00	500.00	mg/L	100.0
Mg	200	5	250.00	250.00	mg/L	100.0
Mn	10000	20	20.00	20.00	mg/L	100.0
Zn	10000	20	20.00	20.00	mg/L	100.0
Cu	100000	20	2000.00	2000.00	µg/L	100.0
Co	100000	20	2000.00	2000.00	µg/L	100.0

A 100% match indicates that the solver satisfied the constraint within numerical tolerance.

Core concepts

Compound database

OptiThor ships with a pre-generated, offline compound database containing molecular formulas, molar masses, elemental composition, and hydrate handling.

from optithor import CompoundDb
repo = CompoundDb()

On first use, the packaged database (shipped in the wheel) is copied into a user-writable local cache. Subsequent runs use this cache and no network access occurs by default.

To reset the local cache to the packaged database:

repo.load(force_reload=True)

---

PubChem CIDs

Compounds are referenced using PubChem Compound IDs (CIDs) provided as strings:

compound_cids = ["5793", "4873", "62640"]

---

Elemental requirements

Elemental constraints are defined using ElementRequirement:

ElementRequirement(
    reference_yield_g_cdw_per_g=...,  # g CDW per g element
    excess_factor=...,                # safety margin
)

These parameters encode biological assumptions and must be adapted to the organism and growth model.

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Optimization model

OptiThor formulates a linear programming problem that:

• satisfies elemental requirements,
• respects compound availability,
• minimizes total compound mass.

Default solver settings are suitable for most use cases.

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Advanced usage

Fetching missing compounds from PubChem

To explicitly allow network access and fetch missing compounds from PubChem:

df = repo.get_compounds_by_cids(
    compound_cids,
    fetch_missing=True,
    update_cache=True,
)

⚠️ PubChem is never accessed unless explicitly requested.

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Diagnostics and reporting tables

from optithor.reports import build_optimization_tables

tables = build_optimization_tables(
    medium_input=medium_input,
    result=result,
    compound_repository=repo,
    decimals=2,
)

tables.compounds
tables.elements

Returned objects are Pandas DataFrames and can be exported or plotted.

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Project structure

src/optithor/
    resources/           # Packaged compound database
examples/                # Usage examples
tests/                   # Test suite
compound_db_builder/     # Database construction pipeline
pyproject.toml
README.md
CONTRIBUTING.md
LICENSE

The compound_db_builder/ directory documents how the packaged compound database was generated and curated. It is not required for normal usage of OptiThor and is not used at runtime, but is included for transparency and reproducibility.

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Contributing

Contributions are welcome.

Please open an issue or pull request on GitHub.

Contribution guidelines: https://github.com/harounbensaadi/optithor/blob/main/CONTRIBUTING.md

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Citation

If you use OptiThor in academic work, please cite or reference this repository.

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License

MIT License