RT Journal Article
SR Electronic
T1 Cameo: A Python Library for Computer Aided Metabolic Engineering and Optimization of Cell Factories
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 147199
DO 10.1101/147199
A1 João G. R. Cardoso
A1 Kristian Jensen
A1 Christian Lieven
A1 Anne Sofie Lærke Hansen
A1 Svetlana Galkina
A1 Moritz Beber
A1 Emre Özdemir
A1 Markus J. Herrgård
A1 Henning Redestig
A1 Nikolaus Sonnenschein
YR 2017
UL http://biorxiv.org/content/early/2017/06/09/147199.abstract
AB Computational systems biology methods enable rational design of cell factories on a genomescale 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.