%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 `__ 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 `__. $ pip install cameo In case you downloaded or cloned the source code from `GitHub `__ or your own fork, you can run the following to install cameo for development. $ pip install -e # 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 `__ for further details. Documentation and Examples ~~~~~~~~~~~~~~~~~~~~~~~~~~ Documentation is available on `cameo.bio `__. Numerous `Jupyter notebooks `__ provide examples and tutorials and also form part of the documentation. They are also availabe in executable form on (`try.cameo.bio `__). Furthermore, course materials for a two day cell factory engineering course are available `here `__. 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 `__ 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 `__ 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 `__ 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 `__ 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 `__. $ pip install cameo In case you downloaded or cloned the source code from `GitHub `__ or your own fork, you can run the following to install cameo for development. $ pip install -e # 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 `__ for further details. Documentation and Examples ~~~~~~~~~~~~~~~~~~~~~~~~~~ Documentation is available on `cameo.bio `__. Numerous `Jupyter notebooks `__ provide examples and tutorials and also form part of the documentation. They are also availabe in executable form on (`try.cameo.bio `__). Furthermore, course materials for a two day cell factory engineering course are available `here `__. 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 `__ 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 `__ 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 `__ 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 `__ 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 `__. $ pip install cameo In case you downloaded or cloned the source code from `GitHub `__ or your own fork, you can run the following to install cameo for development. $ pip install -e # 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 `__ for further details. Documentation and Examples ~~~~~~~~~~~~~~~~~~~~~~~~~~ Documentation is available on `cameo.bio `__. Numerous `Jupyter notebooks `__ provide examples and tutorials and also form part of the documentation. They are also availabe in executable form on (`try.cameo.bio `__). Furthermore, course materials for a two day cell factory engineering course are available `here `__. 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 `__ 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 `__ 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 `__ 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 - 0.13.6-1 - Package Spec generated