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|
%global _empty_manifest_terminate_build 0
Name: python-cameo
Version: 0.13.6
Release: 1
Summary: cameo - computer aided metabolic engineering & optimization
License: Apache License Version 2.0
URL: http://cameo.bio
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/ec/bf/1c15ef2236e6b15a838025dc135bffb02651b00723c68b740a9f664b7ebe/cameo-0.13.6.tar.gz
BuildArch: noarch
Requires: python3-numpy
Requires: python3-scipy
Requires: python3-blessings
Requires: python3-pandas
Requires: python3-ordered-set
Requires: python3-cobra
Requires: python3-future
Requires: python3-optlang
Requires: python3-numexpr
Requires: python3-requests
Requires: python3-networkx
Requires: python3-escher
Requires: python3-IProgress
Requires: python3-inspyred
Requires: python3-lazy-object-proxy
Requires: python3-palettable
Requires: python3-gnomic
Requires: python3-openpyxl
Requires: python3-click
Requires: python3-bokeh
Requires: python3-ipywidgets
Requires: python3-pytest-benchmark
Requires: python3-lxml
Requires: python3-libsbml
Requires: python3-redis
Requires: python3-plotly
Requires: python3-jupyter
Requires: python3-numpydoc
Requires: python3-Sphinx
Requires: python3-pytest-cov
Requires: python3-ipyparallel
Requires: python3-pytest
Requires: python3-bokeh
Requires: python3-Sphinx
Requires: python3-numpydoc
Requires: python3-jupyter
Requires: python3-ipywidgets
Requires: python3-redis
Requires: python3-ipyparallel
Requires: python3-plotly
Requires: python3-libsbml
Requires: python3-lxml
Requires: python3-pytest
Requires: python3-pytest-cov
Requires: python3-pytest-benchmark
%description
|Join the chat at https://gitter.im/biosustain/cameo| |PyPI| |License|
|Build Status| |Coverage Status| |DOI| |zenhub| |binder|
What is cameo?
~~~~~~~~~~~~~~
**Cameo** is a high-level python library developed to aid the strain
design process in metabolic engineering projects. The library provides a
modular framework of simulation and strain design methods that targets
developers that want to develop new design algorithms and custom analysis workflows.
Furthermore, it exposes a high-level API to users that just want to
compute promising strain designs.
Curious? Head over to `try.cameo.bio <http://try.cameo.bio>`__
and give it a try.
Please cite https://doi.org/10.1021/acssynbio.7b00423 if you've used cameo in a scientific publication.
Installation
~~~~~~~~~~~~
Use pip to install cameo from `PyPI <https://pypi.python.org/pypi/cameo>`__.
$ pip install cameo
In case you downloaded or cloned the source code from `GitHub <https://github.com/biosustain/cameo>`__
or your own fork, you can run the following to install cameo for development.
$ pip install -e <path-to-cameo-repo> # recommended
You might need to run these commands with administrative
privileges if you're not using a virtual environment (using ``sudo`` for example).
Please check the `documentation <http://cameo.bio/installation.html>`__
for further details.
Documentation and Examples
~~~~~~~~~~~~~~~~~~~~~~~~~~
Documentation is available on `cameo.bio <http://cameo.bio>`__. Numerous `Jupyter notebooks <http://nbviewer.ipython.org/github/biosustain/cameo-notebooks/tree/master/>`__
provide examples and tutorials and also form part of the documentation. They are also availabe in executable form on (`try.cameo.bio <http://try.cameo.bio>`__).
Furthermore, course materials for a two day cell factory engineering course are available `here <https://biosustain.github.io/cell-factory-design-course/>`__.
High-level API (for users)
^^^^^^^^^^^^^^^^^^^^^^^^^^
Compute strain engineering strategies for a desired product in a number
of host organisms using the high-level interface (runtime is on the order of hours).
from cameo.api import design
design(product='L-Serine')
`Output <http://nbviewer.ipython.org/github/biosustain/cameo-notebooks/blob/master/08-high-level-API.ipynb>`__
The high-level API can also be called from the command line.
$ cameo design vanillin
For more information run
$ cameo --help
Low-level API (for developers)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Find gene knockout targets using evolutionary computation.
from cameo import models
from cameo.strain_design.heuristic import GeneKnockoutOptimization
from cameo.strain_design.heuristic.objective_functions import biomass_product_coupled_yield
model = models.bigg.e_coli_core
obj = biomass_product_coupled_yield(
model.reactions.Biomass_Ecoli_core_w_GAM,
model.reactions.EX_succ_e,
model.reactions.EX_glc_e)
ko = GeneKnockoutOptimization(model=model, objective_function=obj)
ko.run(max_evaluations=50000, n=1, mutation_rate=0.15, indel_rate=0.185)
`Output <http://nbviewer.ipython.org/github/biosustain/cameo-notebooks/blob/master/05-predict-gene-knockout-strategies.ipynb>`__
Predict heterologous pathways for a desired chemical.
from cameo.strain_design import pathway_prediction
predictor = pathway_prediction.PathwayPredictor(model)
pathways = predictor.run(product="vanillin")
`Output <http://nbviewer.ipython.org/github/biosustain/cameo-notebooks/blob/master/07-predict-heterologous-pathways.ipynb>`__
Contributions
~~~~~~~~~~~~~
%package -n python3-cameo
Summary: cameo - computer aided metabolic engineering & optimization
Provides: python-cameo
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-cameo
|Join the chat at https://gitter.im/biosustain/cameo| |PyPI| |License|
|Build Status| |Coverage Status| |DOI| |zenhub| |binder|
What is cameo?
~~~~~~~~~~~~~~
**Cameo** is a high-level python library developed to aid the strain
design process in metabolic engineering projects. The library provides a
modular framework of simulation and strain design methods that targets
developers that want to develop new design algorithms and custom analysis workflows.
Furthermore, it exposes a high-level API to users that just want to
compute promising strain designs.
Curious? Head over to `try.cameo.bio <http://try.cameo.bio>`__
and give it a try.
Please cite https://doi.org/10.1021/acssynbio.7b00423 if you've used cameo in a scientific publication.
Installation
~~~~~~~~~~~~
Use pip to install cameo from `PyPI <https://pypi.python.org/pypi/cameo>`__.
$ pip install cameo
In case you downloaded or cloned the source code from `GitHub <https://github.com/biosustain/cameo>`__
or your own fork, you can run the following to install cameo for development.
$ pip install -e <path-to-cameo-repo> # recommended
You might need to run these commands with administrative
privileges if you're not using a virtual environment (using ``sudo`` for example).
Please check the `documentation <http://cameo.bio/installation.html>`__
for further details.
Documentation and Examples
~~~~~~~~~~~~~~~~~~~~~~~~~~
Documentation is available on `cameo.bio <http://cameo.bio>`__. Numerous `Jupyter notebooks <http://nbviewer.ipython.org/github/biosustain/cameo-notebooks/tree/master/>`__
provide examples and tutorials and also form part of the documentation. They are also availabe in executable form on (`try.cameo.bio <http://try.cameo.bio>`__).
Furthermore, course materials for a two day cell factory engineering course are available `here <https://biosustain.github.io/cell-factory-design-course/>`__.
High-level API (for users)
^^^^^^^^^^^^^^^^^^^^^^^^^^
Compute strain engineering strategies for a desired product in a number
of host organisms using the high-level interface (runtime is on the order of hours).
from cameo.api import design
design(product='L-Serine')
`Output <http://nbviewer.ipython.org/github/biosustain/cameo-notebooks/blob/master/08-high-level-API.ipynb>`__
The high-level API can also be called from the command line.
$ cameo design vanillin
For more information run
$ cameo --help
Low-level API (for developers)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Find gene knockout targets using evolutionary computation.
from cameo import models
from cameo.strain_design.heuristic import GeneKnockoutOptimization
from cameo.strain_design.heuristic.objective_functions import biomass_product_coupled_yield
model = models.bigg.e_coli_core
obj = biomass_product_coupled_yield(
model.reactions.Biomass_Ecoli_core_w_GAM,
model.reactions.EX_succ_e,
model.reactions.EX_glc_e)
ko = GeneKnockoutOptimization(model=model, objective_function=obj)
ko.run(max_evaluations=50000, n=1, mutation_rate=0.15, indel_rate=0.185)
`Output <http://nbviewer.ipython.org/github/biosustain/cameo-notebooks/blob/master/05-predict-gene-knockout-strategies.ipynb>`__
Predict heterologous pathways for a desired chemical.
from cameo.strain_design import pathway_prediction
predictor = pathway_prediction.PathwayPredictor(model)
pathways = predictor.run(product="vanillin")
`Output <http://nbviewer.ipython.org/github/biosustain/cameo-notebooks/blob/master/07-predict-heterologous-pathways.ipynb>`__
Contributions
~~~~~~~~~~~~~
%package help
Summary: Development documents and examples for cameo
Provides: python3-cameo-doc
%description help
|Join the chat at https://gitter.im/biosustain/cameo| |PyPI| |License|
|Build Status| |Coverage Status| |DOI| |zenhub| |binder|
What is cameo?
~~~~~~~~~~~~~~
**Cameo** is a high-level python library developed to aid the strain
design process in metabolic engineering projects. The library provides a
modular framework of simulation and strain design methods that targets
developers that want to develop new design algorithms and custom analysis workflows.
Furthermore, it exposes a high-level API to users that just want to
compute promising strain designs.
Curious? Head over to `try.cameo.bio <http://try.cameo.bio>`__
and give it a try.
Please cite https://doi.org/10.1021/acssynbio.7b00423 if you've used cameo in a scientific publication.
Installation
~~~~~~~~~~~~
Use pip to install cameo from `PyPI <https://pypi.python.org/pypi/cameo>`__.
$ pip install cameo
In case you downloaded or cloned the source code from `GitHub <https://github.com/biosustain/cameo>`__
or your own fork, you can run the following to install cameo for development.
$ pip install -e <path-to-cameo-repo> # recommended
You might need to run these commands with administrative
privileges if you're not using a virtual environment (using ``sudo`` for example).
Please check the `documentation <http://cameo.bio/installation.html>`__
for further details.
Documentation and Examples
~~~~~~~~~~~~~~~~~~~~~~~~~~
Documentation is available on `cameo.bio <http://cameo.bio>`__. Numerous `Jupyter notebooks <http://nbviewer.ipython.org/github/biosustain/cameo-notebooks/tree/master/>`__
provide examples and tutorials and also form part of the documentation. They are also availabe in executable form on (`try.cameo.bio <http://try.cameo.bio>`__).
Furthermore, course materials for a two day cell factory engineering course are available `here <https://biosustain.github.io/cell-factory-design-course/>`__.
High-level API (for users)
^^^^^^^^^^^^^^^^^^^^^^^^^^
Compute strain engineering strategies for a desired product in a number
of host organisms using the high-level interface (runtime is on the order of hours).
from cameo.api import design
design(product='L-Serine')
`Output <http://nbviewer.ipython.org/github/biosustain/cameo-notebooks/blob/master/08-high-level-API.ipynb>`__
The high-level API can also be called from the command line.
$ cameo design vanillin
For more information run
$ cameo --help
Low-level API (for developers)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Find gene knockout targets using evolutionary computation.
from cameo import models
from cameo.strain_design.heuristic import GeneKnockoutOptimization
from cameo.strain_design.heuristic.objective_functions import biomass_product_coupled_yield
model = models.bigg.e_coli_core
obj = biomass_product_coupled_yield(
model.reactions.Biomass_Ecoli_core_w_GAM,
model.reactions.EX_succ_e,
model.reactions.EX_glc_e)
ko = GeneKnockoutOptimization(model=model, objective_function=obj)
ko.run(max_evaluations=50000, n=1, mutation_rate=0.15, indel_rate=0.185)
`Output <http://nbviewer.ipython.org/github/biosustain/cameo-notebooks/blob/master/05-predict-gene-knockout-strategies.ipynb>`__
Predict heterologous pathways for a desired chemical.
from cameo.strain_design import pathway_prediction
predictor = pathway_prediction.PathwayPredictor(model)
pathways = predictor.run(product="vanillin")
`Output <http://nbviewer.ipython.org/github/biosustain/cameo-notebooks/blob/master/07-predict-heterologous-pathways.ipynb>`__
Contributions
~~~~~~~~~~~~~
%prep
%autosetup -n cameo-0.13.6
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-cameo -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Mon May 15 2023 Python_Bot <Python_Bot@openeuler.org> - 0.13.6-1
- Package Spec generated
|
