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Abstract
Computational systems biology methods enable rational design of cell factories on a genome-scale and thus accelerate the engineering of cells for the production of valuable chemicals and proteins. Unfortunately, for the majority of these methods' implementations are either not published, rely on proprietary software, or do not provide documented interfaces, which has precluded their mainstream adoption in the field. In this work we present cameo, a platform-independent software that enables in silico design of cell factories and targets both experienced modelers as well as users new to the field. It is written in Python and implements state-of-the-art methods for enumerating and prioritizing knock-out, knock-in, over-expression, and down-regulation strategies and combinations thereof. Cameo is an open source software project and is freely available under the Apache License 2.0. A dedicated website including documentation, examples, and installation instructions can be found at http://cameo.bio. Users can also give cameo a try at http://try.cameo.bio.
Original language | English |
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Journal | A C S Synthetic Biology |
Volume | 7 |
Issue number | 4 |
Pages (from-to) | 1163-1166 |
ISSN | 2161-5063 |
DOIs | |
Publication status | Published - 2018 |
Bibliographical note
This is an open access article published under a Creative Commons Attribution (CC-BY) License, which permits unrestricted use, distribution and reproduction in any medium,provided the author and source are cited.
Keywords
- Metabolic engineering
- Genome-scale metabolic models
- Heterologous pathway predictions
- Computer-aided design
- Software
- Python
Fingerprint
Dive into the research topics of 'Cameo: A Python Library for Computer Aided Metabolic Engineering and Optimization of Cell Factories'. Together they form a unique fingerprint.Projects
- 1 Finished
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DD-DeCaF: Bioinformatics Services for Data-Driven Design of Cell Factories and Communities
Herrgard, M., Sonnenschein, N., Kutuzova, S., Redestig, N. H., Beber, M. E., Dannaher, D., Lopez Benito, A., Kaafarani, A., Lieven, C., Lohmann, R., Rasmussen, B. K., Kjiproski, D. & Knudsen, E. B.
01/03/2016 → 29/02/2020
Project: Research