<p align="center">
<img src="https://github.com/user-attachments/assets/e801eb01-4108-4fe0-a5ef-763002dd583f" width=250 />
</p>
<h3 align="center">Growth Balance Analysis for Python</h3>
<p align="center">
<br />
<a href="https://github.com/charlesrocabert/gbapy/releases/latest"><img src="https://img.shields.io/github/release/charlesrocabert/gbapy/all.svg" /></a>
<a href="https://badge.fury.io/py/gba"><img src="https://badge.fury.io/py/gba.svg" alt="PyPI version"></a>
<a href="https://github.com/charlesrocabert/gbapy/actions"><img src="https://github.com/charlesrocabert/gbapy/workflows/Upload Python Package/badge.svg" /></a>
<a href="https://github.com/charlesrocabert/gbapy/LICENSE.html"><img src="https://img.shields.io/badge/License-GPLv3-blue.svg" /></a>
</p>
<p align="center">
<a href="https://www.cs.hhu.de/en/research-groups/computational-cell-biology" target="_blank"><img src="https://github.com/user-attachments/assets/4e4b3b79-0d6a-4328-9c3f-3497401887e4" width=150 /></a>
<a href="https://www.hhu.de/en/" target="_blank"><img src="https://github.com/user-attachments/assets/7db5c8f7-e37a-415f-88c3-1b06a49e1f28" width=150 /></a>
</p>
-----------------
```
pip install gba
```
<p align="justify">
<strong>gbapy</strong> is a Python package that provides tools for building and analyzing <strong>self-replicating cell (SRC)</strong> models based on the <strong>growth balance analysis (GBA)</strong> mathematical formalism (<a href="https://doi.org/10.1371/journal.pcbi.1011156" target="_blank">Dourado et al. 2023</a>).
</p>
### Features
<p align="justify">
The module offers two core components:
- :wrench: A <strong>builder class</strong>, to construct SRC models of any size from first principles,
- :chart_with_upwards_trend: A <strong>model class</strong>, to manipulate and optimize models once they are built.
</p>
# Typical workflow
<p align="center">
<img width="400" alt="image" src="https://github.com/user-attachments/assets/710fecd4-0381-41a2-ae6e-d952eb8c40ad" />
</p>
```python
import gba
builder = gba.Builder(name="toy")
### Add model information (ODS sheet 'Info')
builder.add_info(category="General", key="Name", content="toy")
builder.add_info(category="General", key="Description", content="Toy model")
### Create and add proteins (one protein per enzyme per reaction)
### - Masses in Da.
p1 = gba.Protein(id="p1", mass=1000000.0)
p2 = gba.Protein(id="p2", mass=1000000.0)
builder.add_proteins([p1, p2])
### Create and add metabolites:
### - External and internal glucose
### - One generic Protein product
### - Masses in Da.
x_G = gba.Metabolite(id="x_G", species_location=gba.SpeciesLocation.EXTERNAL, mass=180.0)
G = gba.Metabolite(id="G", species_location=gba.SpeciesLocation.INTERNAL, mass=180.0)
Protein = gba.Metabolite(id="Protein", species_location=gba.SpeciesLocation.INTERNAL,mass=180.0)
builder.add_metabolites([x_G, G, Protein])
### Create and add transporter to import glucose:
### - Enzyme is composed of one protein p1
### - Reaction is irreversible
### - kcat values in 1/h
### - KM values in g/L
rxn1 = gba.Reaction(id="rxn1", lb=0.0, ub=1000.0,
reaction_type=gba.ReactionType.TRANSPORT,
metabolites={"x_G":-1.0, "G": 1.0},
proteins={"p1": 1.0})
rxn1.add_kcat_value(direction=gba.ReactionDirection.FORWARD, kcat_value=45000.0)
rxn1.add_km_value(metabolite_id="x_G", km_value=0.00013)
rxn1.complete(kcat_value=0.0, km_value=0.0)
builder.add_reaction(rxn1)
### Create and add ribosome reaction to produce proteins:
### - Enzyme is composed of one protein p2
### - Reaction is irreversible
ribosome = gba.Reaction(id="Ribosome", lb=0.0, ub=1000.0,
reaction_type=gba.ReactionType.METABOLIC,
metabolites={"G":-1.0, "Protein": 1.0},
proteins={"p2": 1.0})
ribosome.add_kcat_value(direction=gba.ReactionDirection.FORWARD, kcat_value=45000.0)
ribosome.add_km_value(metabolite_id="G", km_value=0.00013)
ribosome.complete(kcat_value=0.0, km_value=0.0)
builder.add_reaction(ribosome)
### Convert the model to GBA formalism (cf. Dourado et al. 2023)
builder.convert(ribosome_mass_kcat=4.55, ribosome_mass_km=8.3)
builder.build_GBA_model()
### Set cell's total density (g/L)
builder.set_rho(340.0)
### Create external conditions (in g/L)
x_G_conc = 1.0
for i in range(25):
builder.add_condition(condition_id=str(i+1), metabolites={"x_G": x_G_conc})
x_G_conc *= 2/3
### Save the model to an ODS file
builder.export_to_ods()
```
<p align="center">
<img width="500" alt="image" src="https://github.com/user-attachments/assets/7d0ec598-2fb2-497f-8d89-f15e27e24d43" />
</p>
```python
### Load the ODS model
model = gba.read_ods_model(name="toy")
### Find a valid initial solution
model.find_initial_solution()
### Optimize the model for all conditions
model.find_optimum_by_condition()
### Make a plot
model.plot(x="x_G", y="mu", title="Growth rate", logx=True)
### Export optimization data in CSV
model.export_optimization_data()
```
<p align="center">
<img width="550" alt="image" src="https://github.com/user-attachments/assets/16b7eb56-81d8-429c-b7f8-9aa4cac48de5" />
</p>
### Reference files
- 🔗 <a href="https://github.com/charlesrocabert/gbapy/blob/main/tutorials/my_toy_model.ipynb" target="_blank">Toy model tutorial</a>,
- 🔗 <a href="https://github.com/charlesrocabert/gbapy/blob/main/tutorials/toy.ods" target="_blank">ODS file</a>,
- 🔗 <a href="https://github.com/charlesrocabert/gbapy/blob/main/tutorials/toy_optimization_data.csv" target="_blank">CSV optimization data</a>.
# Table of contents
- [1) Installation](#installation)
- [1.1) Supported platforms](#supported_platforms)
- [1.2) Dependencies](#dependencies)
- [1.3) Manual installation](#manual_installation)
- [2) Tutorials](#tutorials)
- [3) Documentation](#documentation)
- [4) Contributing](#contributing)
- [5) Copyright](#copyright)
- [6) License](#license)
# 1) Installation <a name="installation"></a>
> [!CAUTION]
> Module not deployed on PyPI yet
The easiest way to install <strong>gbapy</strong> is from <a href="https://pypi.org/project/gba/" target="_blank">PyPI</a>:
```
pip install gba
```
> [!IMPORTANT]
<a href="https://github.com/charlesrocabert/gbacpp" target="_blank">gbacpp</a> software is required to run optimization tasks.
### 1.1) Supported platforms <a name="supported_platforms"></a>
<strong>gbapy</strong> has been primilary developed for Unix/Linux and macOS systems.
### 1.2) Dependencies <a name="dependencies"></a>
#### • Software
* <a href="https://github.com/charlesrocabert/gbacpp" target="_blank">gbacpp</a> is required to run optimization tasks.
#### • Licensed Python modules
* The Python API of <a href="https://www.gurobi.com/" target="_blank">GUROBI optimizer</a> must be installed and requires a user license (<a href="https://www.gurobi.com/academia/academic-program-and-licenses/" target="_blank">free for academics</a>).
#### • Other Python modules
* <a href="https://numpy.org/" target="_blank">NumPy</a>
* <a href="https://pandas.pydata.org/" target="_blank">pandas</a>
* <a href="https://ipython.org/" target="_blank">IPython</a>
* <a href="https://plotly.com/" target="_blank">plotly</a>
* <a href="https://opencobra.github.io/cobrapy/" target="_blank">cobrapy</a>
* <a href="https://github.com/cgohlke/molmass" target="_blank">molmass</a>
* <a href="https://biopython.org/" target="_blank">Biopython</a>
### 1.3) Manual installation <a name="manual_installation"></a>
If you want to install <strong>gbapy</strong> manually, download the <a href="https://github.com/charlesrocabert/gbapy/releases/latest">latest release</a>, and save it to a directory of your choice. Open a terminal and use the <code>cd</code> command to navigate to this directory. Then follow the steps below to compile and build the executables.
```
sh install.sh
```
> [!TIP]
> You can later uninstall the module using <code>sh uninstall.sh</code>.
# 2) Tutorials <a name="tutorials"></a>
<p align="center" style="font-size: 2.5em;">
Tutorials coming soon ...
</p>
# 3) Documentation <a name="documentation"></a>
<p align="center" style="font-size: 2.5em;">
Documentation coming soon ...
</p>
# 4) Contributing <a name="contributing"></a>
If you wish to contribute, do not hesitate to reach <a href="mailto:charles DOT rocabert AT hhu DOT de">the developer</a>.
# 5) Copyright <a name="copyright"></a>
Copyright © 2024-2025 Charles Rocabert, Furkan Mert.
# 6) License <a name="license"></a>
<p align="justify">
This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
</p>
<p align="justify">
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
</p>
<p align="justify">
You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/.
</p>
Raw data
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"keywords": "constraint-based-modeling growth-balance-analysis self-replicating-model systems-biology metabolic-network resource-allocation cellular-economics kinetic-modeling first-prnciples simulation evolutionary-algorithms predictive-evolution",
"author": "Charles Rocabert",
"author_email": "charles.rocabert@hhu.de",
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"description": "<p align=\"center\">\n <img src=\"https://github.com/user-attachments/assets/e801eb01-4108-4fe0-a5ef-763002dd583f\" width=250 />\n\n</p>\n<h3 align=\"center\">Growth Balance Analysis for Python</h3>\n\n<p align=\"center\">\n<br />\n<a href=\"https://github.com/charlesrocabert/gbapy/releases/latest\"><img src=\"https://img.shields.io/github/release/charlesrocabert/gbapy/all.svg\" /></a> \n<a href=\"https://badge.fury.io/py/gba\"><img src=\"https://badge.fury.io/py/gba.svg\" alt=\"PyPI version\"></a> \n<a href=\"https://github.com/charlesrocabert/gbapy/actions\"><img src=\"https://github.com/charlesrocabert/gbapy/workflows/Upload Python Package/badge.svg\" /></a> \n<a href=\"https://github.com/charlesrocabert/gbapy/LICENSE.html\"><img src=\"https://img.shields.io/badge/License-GPLv3-blue.svg\" /></a>\n</p>\n\n<p align=\"center\">\n <a href=\"https://www.cs.hhu.de/en/research-groups/computational-cell-biology\" target=\"_blank\"><img src=\"https://github.com/user-attachments/assets/4e4b3b79-0d6a-4328-9c3f-3497401887e4\" width=150 /></a>\n \n <a href=\"https://www.hhu.de/en/\" target=\"_blank\"><img src=\"https://github.com/user-attachments/assets/7db5c8f7-e37a-415f-88c3-1b06a49e1f28\" width=150 /></a>\n</p>\n\n-----------------\n\n```\npip install gba\n```\n\n<p align=\"justify\">\n<strong>gbapy</strong> is a Python package that provides tools for building and analyzing <strong>self-replicating cell (SRC)</strong> models based on the <strong>growth balance analysis (GBA)</strong> mathematical formalism (<a href=\"https://doi.org/10.1371/journal.pcbi.1011156\" target=\"_blank\">Dourado et al. 2023</a>).\n</p>\n\n### Features\n\n<p align=\"justify\">\nThe module offers two core components:\n \n- :wrench: A <strong>builder class</strong>, to construct SRC models of any size from first principles,\n- :chart_with_upwards_trend: A <strong>model class</strong>, to manipulate and optimize models once they are built.\n\n</p>\n\n# Typical workflow\n\n<p align=\"center\">\n<img width=\"400\" alt=\"image\" src=\"https://github.com/user-attachments/assets/710fecd4-0381-41a2-ae6e-d952eb8c40ad\" />\n</p>\n\n```python\nimport gba\n\nbuilder = gba.Builder(name=\"toy\")\n\n### Add model information (ODS sheet 'Info')\nbuilder.add_info(category=\"General\", key=\"Name\", content=\"toy\")\nbuilder.add_info(category=\"General\", key=\"Description\", content=\"Toy model\")\n\n### Create and add proteins (one protein per enzyme per reaction)\n### - Masses in Da.\np1 = gba.Protein(id=\"p1\", mass=1000000.0)\np2 = gba.Protein(id=\"p2\", mass=1000000.0)\nbuilder.add_proteins([p1, p2])\n\n### Create and add metabolites:\n### - External and internal glucose\n### - One generic Protein product\n### - Masses in Da.\nx_G = gba.Metabolite(id=\"x_G\", species_location=gba.SpeciesLocation.EXTERNAL, mass=180.0)\nG = gba.Metabolite(id=\"G\", species_location=gba.SpeciesLocation.INTERNAL, mass=180.0)\nProtein = gba.Metabolite(id=\"Protein\", species_location=gba.SpeciesLocation.INTERNAL,mass=180.0)\nbuilder.add_metabolites([x_G, G, Protein])\n\n### Create and add transporter to import glucose:\n### - Enzyme is composed of one protein p1\n### - Reaction is irreversible\n### - kcat values in 1/h\n### - KM values in g/L\nrxn1 = gba.Reaction(id=\"rxn1\", lb=0.0, ub=1000.0,\n reaction_type=gba.ReactionType.TRANSPORT,\n metabolites={\"x_G\":-1.0, \"G\": 1.0},\n proteins={\"p1\": 1.0})\nrxn1.add_kcat_value(direction=gba.ReactionDirection.FORWARD, kcat_value=45000.0)\nrxn1.add_km_value(metabolite_id=\"x_G\", km_value=0.00013)\nrxn1.complete(kcat_value=0.0, km_value=0.0)\nbuilder.add_reaction(rxn1)\n\n### Create and add ribosome reaction to produce proteins:\n### - Enzyme is composed of one protein p2\n### - Reaction is irreversible\nribosome = gba.Reaction(id=\"Ribosome\", lb=0.0, ub=1000.0,\n reaction_type=gba.ReactionType.METABOLIC,\n metabolites={\"G\":-1.0, \"Protein\": 1.0},\n proteins={\"p2\": 1.0})\nribosome.add_kcat_value(direction=gba.ReactionDirection.FORWARD, kcat_value=45000.0)\nribosome.add_km_value(metabolite_id=\"G\", km_value=0.00013)\nribosome.complete(kcat_value=0.0, km_value=0.0)\nbuilder.add_reaction(ribosome)\n\n### Convert the model to GBA formalism (cf. Dourado et al. 2023)\nbuilder.convert(ribosome_mass_kcat=4.55, ribosome_mass_km=8.3)\nbuilder.build_GBA_model()\n\n### Set cell's total density (g/L)\nbuilder.set_rho(340.0)\n\n### Create external conditions (in g/L)\nx_G_conc = 1.0\nfor i in range(25):\n builder.add_condition(condition_id=str(i+1), metabolites={\"x_G\": x_G_conc})\n x_G_conc *= 2/3\n\n### Save the model to an ODS file\nbuilder.export_to_ods()\n```\n\n<p align=\"center\">\n<img width=\"500\" alt=\"image\" src=\"https://github.com/user-attachments/assets/7d0ec598-2fb2-497f-8d89-f15e27e24d43\" />\n</p>\n\n```python\n### Load the ODS model\nmodel = gba.read_ods_model(name=\"toy\")\n\n### Find a valid initial solution\nmodel.find_initial_solution()\n\n### Optimize the model for all conditions\nmodel.find_optimum_by_condition()\n\n### Make a plot\nmodel.plot(x=\"x_G\", y=\"mu\", title=\"Growth rate\", logx=True)\n\n### Export optimization data in CSV\nmodel.export_optimization_data()\n```\n\n<p align=\"center\">\n<img width=\"550\" alt=\"image\" src=\"https://github.com/user-attachments/assets/16b7eb56-81d8-429c-b7f8-9aa4cac48de5\" />\n</p>\n\n### Reference files\n\n- \ud83d\udd17 <a href=\"https://github.com/charlesrocabert/gbapy/blob/main/tutorials/my_toy_model.ipynb\" target=\"_blank\">Toy model tutorial</a>,\n- \ud83d\udd17 <a href=\"https://github.com/charlesrocabert/gbapy/blob/main/tutorials/toy.ods\" target=\"_blank\">ODS file</a>,\n- \ud83d\udd17 <a href=\"https://github.com/charlesrocabert/gbapy/blob/main/tutorials/toy_optimization_data.csv\" target=\"_blank\">CSV optimization data</a>.\n\n# Table of contents\n\n- [1) Installation](#installation)\n - [1.1) Supported platforms](#supported_platforms)\n - [1.2) Dependencies](#dependencies)\n - [1.3) Manual installation](#manual_installation)\n- [2) Tutorials](#tutorials)\n- [3) Documentation](#documentation)\n- [4) Contributing](#contributing)\n- [5) Copyright](#copyright)\n- [6) License](#license)\n\n# 1) Installation <a name=\"installation\"></a>\n\n> [!CAUTION]\n> Module not deployed on PyPI yet\n\nThe easiest way to install <strong>gbapy</strong> is from <a href=\"https://pypi.org/project/gba/\" target=\"_blank\">PyPI</a>:\n \n```\npip install gba\n```\n\n> [!IMPORTANT]\n<a href=\"https://github.com/charlesrocabert/gbacpp\" target=\"_blank\">gbacpp</a> software is required to run optimization tasks.\n\n### 1.1) Supported platforms <a name=\"supported_platforms\"></a>\n<strong>gbapy</strong> has been primilary developed for Unix/Linux and macOS systems.\n\n### 1.2) Dependencies <a name=\"dependencies\"></a>\n\n#### \u2022 Software\n* <a href=\"https://github.com/charlesrocabert/gbacpp\" target=\"_blank\">gbacpp</a> is required to run optimization tasks.\n\n#### \u2022 Licensed Python modules\n* The Python API of <a href=\"https://www.gurobi.com/\" target=\"_blank\">GUROBI optimizer</a> must be installed and requires a user license (<a href=\"https://www.gurobi.com/academia/academic-program-and-licenses/\" target=\"_blank\">free for academics</a>).\n\n#### \u2022 Other Python modules\n* <a href=\"https://numpy.org/\" target=\"_blank\">NumPy</a>\n* <a href=\"https://pandas.pydata.org/\" target=\"_blank\">pandas</a>\n* <a href=\"https://ipython.org/\" target=\"_blank\">IPython</a>\n* <a href=\"https://plotly.com/\" target=\"_blank\">plotly</a>\n* <a href=\"https://opencobra.github.io/cobrapy/\" target=\"_blank\">cobrapy</a>\n* <a href=\"https://github.com/cgohlke/molmass\" target=\"_blank\">molmass</a>\n* <a href=\"https://biopython.org/\" target=\"_blank\">Biopython</a>\n\n### 1.3) Manual installation <a name=\"manual_installation\"></a>\n\nIf you want to install <strong>gbapy</strong> manually, download the <a href=\"https://github.com/charlesrocabert/gbapy/releases/latest\">latest release</a>, and save it to a directory of your choice. Open a terminal and use the <code>cd</code> command to navigate to this directory. Then follow the steps below to compile and build the executables.\n\n```\nsh install.sh\n```\n\n> [!TIP]\n> You can later uninstall the module using <code>sh uninstall.sh</code>.\n\n# 2) Tutorials <a name=\"tutorials\"></a>\n\n<p align=\"center\" style=\"font-size: 2.5em;\">\nTutorials coming soon ...\n</p>\n\n# 3) Documentation <a name=\"documentation\"></a>\n\n<p align=\"center\" style=\"font-size: 2.5em;\">\nDocumentation coming soon ...\n</p>\n\n# 4) Contributing <a name=\"contributing\"></a>\n\nIf you wish to contribute, do not hesitate to reach <a href=\"mailto:charles DOT rocabert AT hhu DOT de\">the developer</a>.\n\n# 5) Copyright <a name=\"copyright\"></a>\n\nCopyright \u00a9 2024-2025 Charles Rocabert, Furkan Mert.\n\n# 6) License <a name=\"license\"></a>\n\n<p align=\"justify\">\nThis program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.\n</p>\n\n<p align=\"justify\">\nThis program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.\n</p>\n\n<p align=\"justify\">\nYou should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/.\n</p>\n",
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"coveralls": false,
"github_actions": true,
"lcname": "gba"
}
JSON
