Cameo

Cameo—Computer Aided Metabolic Engineering and Optimization

.. summary-start

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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.

.. summary-end

Installation
~~~~~~~~~~~~

.. installation-start

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.

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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/>.

.. showcase-start

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>__

.. showcase-end

Contributions

... are very welcome! Please read the `guideline <CONTRIBUTING.rst>`__ for instructions how to contribute.


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