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Cameo: A Python Library for Computer Aided Metabolic Engineering and Optimization of Cell Factories

Cardoso, J. G. R., Technical University of Denmark
Jensen, K., The Novo Nordisk Foundation Center for Biosustainability
Özdemir, E., Technical University of Denmark

In this work we present Cameo, a platform independent software framework that enables in silico strain design of bacterial and eukaryotic cell factories on a genome-scale. It is written in Python and implements novel and state-of-the-art methods for enumerating and prioritizing knock-out, knock-in, over-expression, and down-regulation strategies and combinations thereof. Cameo targets both experienced modelers as well as experimental strain engineers: a modular and extendible library of classes and functions simplifies the implementation of new strain design algorithms, and a high-level 'design' function enables the generation of hundreds of strain designs for desired compounds. Long-lasting computations have been implemented with parallelization in mind and can take advantage of multicore systems and high-performance computing infrastructure. Since Cameo is based on the constraint-based modeling software cobrapy and thus part of a larger community and ecosystem of modeling software, users can easily take advantage of, for example, system-level data integration, interactive pathway visualizations, and programmatic access to a database of curated models. Furthermore, Cameo is tightly integrated with a number of widely used scientific Python tools, including the Jupyter notebook interface, pandas, and sympy. Cameo is an open source software project and welcomes contributions by the community on GitHub. It is freely available under the Apache License 2.0 and can be installed either from source or PyPI. A dedicated website including documentation, examples, and installation instructions can be found at http://cameo.bio.