=====
ETFBA
=====
ETFBA is a Python package designed for performing **e**\ nzyme protein allocation and **t**\ hermodynamics constraint-based **f**\ lux **b**\ alance **a**\ nalysis. It extends traditional flux balance analysis (FBA) by incorporating constraints based on the availability of catalytic enzyme proteins and the second law of thermodynamics, offering a more comprehensive approach to modeling metabolic fluxes.
ETFBA allows for the application of enzyme protein allocation and thermodynamic constraints either individually or jointly within the model. This flexibility enables users to solve various optimization problems, including:
- FBA: Traditional flux balance analysis.
- EFBA: FBA with enzyme protein allocation constraints.
- TFBA: FBA with thermodynamic constraints.
- ETFBA: FBA with both enzyme protein allocation and thermodynamic constraints.
Variability analysis allows you to evaluate the potential ranges of metabolic fluxes, enzyme protein costs, and reaction Gibbs energy changes while ensuring that the objective function remains within a specified level of optimality. This analysis includes:
- FVA: Flux variability analysis.
- EFVA: Enzyme-constrained FVA.
- TFVA: Thermodynamic FVA.
- EVA: Enzyme variability analysis.
- TEVA: Thermodynamic EVA.
- TVA: Thermodynamic variability analysis.
- ETVA: Enzyme-constrained TVA.
For further details, refer to our `documentation <https://etfba.readthedocs.io/en/latest/index.html>`__.
Installation
============
ETFBA has been tested with Python versions 3.8, 3.9, 3.10 and 3.11. It can be installed using *pip* from PyPI:
.. code-block:: python
python -m pip install --upgrade pip
pip install etfba
Alternatively, you can install it from source (assuming `git <https://git-scm.com/>`__ is installed):
.. code-block:: python
git clone https://github.com/Chaowu88/etfba.git /path/to/etfba
pip install /path/to/etfba
Note: It is recommended to install ETFBA within a `virtual environment <https://docs.python.org/3.8/tutorial/venv.html>`__.
Solver installation
===================
ETFBA uses the modeling language `Pyomo <https://www.pyomo.org/>`__ to formulate linear programming (LP) and mixed integer linear programming (MILP) problems. You can install the freely available solver GLPK via conda:
.. code-block:: python
conda install -c conda-forge glpk
For larger models, such as genome scale models, it is highly recommended to use the commercial optimizer `Gurobi <https://www.gurobi.com/>`__ and install the Python support:
.. code-block:: python
conda install -c gurobi gurobi
Example Usage
=============
You can build an ETFBA model from scratch or convert it from a `COBRA <https://cobrapy.readthedocs.io/en/latest/io.html>`__ model (refer to `here <https://etfba.readthedocs.io/en/latest/building_model.html>`__ for more details). Below is an example of estimating the flux distribution constrained by enzyme protein allocation and thermodynamics:
.. code-block:: python
from etfba import Model
model = Model.load('/path/to/model.bin')
res = model.optimize(
'etfba',
objective=objective, # typically the growth rate
flux_bound=flux_bound, # bounds for metabolic fluxes
conc_bound=conc_bound, # bounds for metabolite concentrations
preset_flux=preset_flux, # preset values for specific metabolic fluxes
preset_conc=preset_conc, # preset values for specific metabolite concentrations
ex_thermo_cons=ex_rxns, # reactions excluded from thermodynamic constraint
inc_enz_cons=eff_rxns, # reactions included in enzyme protein constraint
enz_prot_lb=enz_ub, # upper bound on enzyme protein allocation
parsimonious=True # to obtain parsimonious flux distributions
).solve(solver='gurobi')
opt_growth_rate = res.opt_objective
opt_metabolic_fluxes = res.opt_fluxes
To estimate the variability of fluxes:
.. code-block:: python
res = model.evaluate_variability(
'etfva',
objective=objective,
obj_value=obj_value, # optimal objective value obtained by "optimize"
gamma=gamma, # fraction of the optimum objective to achieve
flux_bound=flux_bound,
conc_bound=conc_bound,
preset_flux=preset_flux,
preset_conc=preset_conc,
ex_thermo_cons=ex_rxns,
inc_enz_cons=eff_rxns,
enz_prot_lb=enz_ub
).solve(solver='gurobi', n_jobs=100)
metabolic_flux_ranges = res.flux_ranges
For more detailed information, please refer to the complete `documentation <https://etfba.readthedocs.io/en/latest/index.html>`__.
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"description": "=====\r\nETFBA\r\n=====\r\n\r\nETFBA is a Python package designed for performing **e**\\ nzyme protein allocation and **t**\\ hermodynamics constraint-based **f**\\ lux **b**\\ alance **a**\\ nalysis. It extends traditional flux balance analysis (FBA) by incorporating constraints based on the availability of catalytic enzyme proteins and the second law of thermodynamics, offering a more comprehensive approach to modeling metabolic fluxes.\r\n\r\nETFBA allows for the application of enzyme protein allocation and thermodynamic constraints either individually or jointly within the model. This flexibility enables users to solve various optimization problems, including:\r\n\r\n- FBA: Traditional flux balance analysis.\r\n- EFBA: FBA with enzyme protein allocation constraints.\r\n- TFBA: FBA with thermodynamic constraints.\r\n- ETFBA: FBA with both enzyme protein allocation and thermodynamic constraints.\r\n\r\nVariability analysis allows you to evaluate the potential ranges of metabolic fluxes, enzyme protein costs, and reaction Gibbs energy changes while ensuring that the objective function remains within a specified level of optimality. This analysis includes:\r\n\r\n- FVA: Flux variability analysis.\r\n- EFVA: Enzyme-constrained FVA.\r\n- TFVA: Thermodynamic FVA.\r\n- EVA: Enzyme variability analysis.\r\n- TEVA: Thermodynamic EVA.\r\n- TVA: Thermodynamic variability analysis.\r\n- ETVA: Enzyme-constrained TVA.\r\n\r\nFor further details, refer to our `documentation <https://etfba.readthedocs.io/en/latest/index.html>`__.\r\n\r\nInstallation\r\n============\r\n\r\nETFBA has been tested with Python versions 3.8, 3.9, 3.10 and 3.11. It can be installed using *pip* from PyPI:\r\n\r\n.. code-block:: python\r\n\r\n python -m pip install --upgrade pip\r\n pip install etfba\r\n\r\nAlternatively, you can install it from source (assuming `git <https://git-scm.com/>`__ is installed):\r\n\r\n.. code-block:: python\r\n\r\n git clone https://github.com/Chaowu88/etfba.git /path/to/etfba\r\n pip install /path/to/etfba\r\n\r\nNote: It is recommended to install ETFBA within a `virtual environment <https://docs.python.org/3.8/tutorial/venv.html>`__.\r\n\r\nSolver installation\r\n===================\r\n\r\nETFBA uses the modeling language `Pyomo <https://www.pyomo.org/>`__ to formulate linear programming (LP) and mixed integer linear programming (MILP) problems. You can install the freely available solver GLPK via conda:\r\n\r\n.. code-block:: python\r\n\r\n conda install -c conda-forge glpk\r\n\r\nFor larger models, such as genome scale models, it is highly recommended to use the commercial optimizer `Gurobi <https://www.gurobi.com/>`__ and install the Python support:\r\n\r\n.. code-block:: python\r\n\r\n conda install -c gurobi gurobi\r\n\r\nExample Usage\r\n=============\r\n\r\nYou can build an ETFBA model from scratch or convert it from a `COBRA <https://cobrapy.readthedocs.io/en/latest/io.html>`__ model (refer to `here <https://etfba.readthedocs.io/en/latest/building_model.html>`__ for more details). Below is an example of estimating the flux distribution constrained by enzyme protein allocation and thermodynamics:\r\n\r\n.. code-block:: python\r\n\r\n from etfba import Model\r\n\r\n model = Model.load('/path/to/model.bin')\r\n \r\n res = model.optimize(\r\n 'etfba',\r\n objective=objective, # typically the growth rate\r\n flux_bound=flux_bound, # bounds for metabolic fluxes \r\n conc_bound=conc_bound, # bounds for metabolite concentrations\r\n preset_flux=preset_flux, # preset values for specific metabolic fluxes\r\n preset_conc=preset_conc, # preset values for specific metabolite concentrations\r\n ex_thermo_cons=ex_rxns, # reactions excluded from thermodynamic constraint\r\n inc_enz_cons=eff_rxns, # reactions included in enzyme protein constraint\r\n enz_prot_lb=enz_ub, # upper bound on enzyme protein allocation\r\n parsimonious=True # to obtain parsimonious flux distributions\r\n ).solve(solver='gurobi')\r\n\r\n opt_growth_rate = res.opt_objective\r\n opt_metabolic_fluxes = res.opt_fluxes\r\n\r\nTo estimate the variability of fluxes:\r\n\r\n.. code-block:: python\r\n\r\n res = model.evaluate_variability(\r\n 'etfva',\r\n objective=objective,\r\n obj_value=obj_value, # optimal objective value obtained by \"optimize\"\r\n gamma=gamma, # fraction of the optimum objective to achieve\r\n flux_bound=flux_bound,\r\n conc_bound=conc_bound,\r\n preset_flux=preset_flux,\r\n preset_conc=preset_conc,\r\n ex_thermo_cons=ex_rxns,\r\n inc_enz_cons=eff_rxns,\r\n enz_prot_lb=enz_ub\r\n ).solve(solver='gurobi', n_jobs=100)\r\n\r\n metabolic_flux_ranges = res.flux_ranges\r\n\r\nFor more detailed information, please refer to the complete `documentation <https://etfba.readthedocs.io/en/latest/index.html>`__.\r\n\r\n\r\n\r\n",
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