Literature DB >> 29558112

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

João G R Cardoso1, Kristian Jensen1, Christian Lieven1, Anne Sofie Lærke Hansen1, Svetlana Galkina1, Moritz Beber1, Emre Özdemir1, Markus J Herrgård1, Henning Redestig1, Nikolaus Sonnenschein1.   

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, 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 knockout, knock-in, overexpression, 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 Web site 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 .

Keywords:  Python; computer-aided design; genome-scale metabolic models; heterologous pathway predictions; metabolic engineering; software

Mesh:

Year:  2018        PMID: 29558112     DOI: 10.1021/acssynbio.7b00423

Source DB:  PubMed          Journal:  ACS Synth Biol        ISSN: 2161-5063            Impact factor:   5.110


  11 in total

1.  Growth-coupled bioconversion of levulinic acid to butanone.

Authors:  Christopher R Mehrer; Jacqueline M Rand; Matthew R Incha; Taylor B Cook; Benginur Demir; Ali Hussain Motagamwala; Daniel Kim; James A Dumesic; Brian F Pfleger
Journal:  Metab Eng       Date:  2019-06-19       Impact factor: 9.783

2.  Anaerobic production of medium-chain fatty alcohols via a β-reduction pathway.

Authors:  Christopher R Mehrer; Matthew R Incha; Mark C Politz; Brian F Pfleger
Journal:  Metab Eng       Date:  2018-05-25       Impact factor: 9.783

3.  Metabolic reconstruction of Pseudomonas chlororaphis ATCC 9446 to understand its metabolic potential as a phenazine-1-carboxamide-producing strain.

Authors:  Fabián Moreno-Avitia; José Utrilla; Francisco Bolívar; Juan Nogales; Adelfo Escalante
Journal:  Appl Microbiol Biotechnol       Date:  2020-09-28       Impact factor: 4.813

4.  FLYCOP: metabolic modeling-based analysis and engineering microbial communities.

Authors:  Beatriz García-Jiménez; José Luis García; Juan Nogales
Journal:  Bioinformatics       Date:  2018-09-01       Impact factor: 6.937

5.  Gsmodutils: a python based framework for test-driven genome scale metabolic model development.

Authors:  James Gilbert; Nicole Pearcy; Rupert Norman; Thomas Millat; Klaus Winzer; John King; Charlie Hodgman; Nigel Minton; Jamie Twycross
Journal:  Bioinformatics       Date:  2019-09-15       Impact factor: 6.937

6.  FlySilico: Flux balance modeling of Drosophila larval growth and resource allocation.

Authors:  Jürgen Wilhelm Schönborn; Lisa Jehrke; Tabea Mettler-Altmann; Mathias Beller
Journal:  Sci Rep       Date:  2019-11-20       Impact factor: 4.379

Review 7.  Bacterial biopolymers: from pathogenesis to advanced materials.

Authors:  M Fata Moradali; Bernd H A Rehm
Journal:  Nat Rev Microbiol       Date:  2020-01-28       Impact factor: 60.633

8.  CobraMod: A pathway-centric curation tool for constraint-based metabolic models.

Authors:  Stefano Camborda; Jan-Niklas Weder; Nadine Töpfer
Journal:  Bioinformatics       Date:  2022-02-24       Impact factor: 6.931

9.  Underground metabolism as a rich reservoir for pathway engineering.

Authors:  Szabolcs Cselgő Kovács; Balázs Szappanos; Roland Tengölics; Richard A Notebaart; Balázs Papp
Journal:  Bioinformatics       Date:  2022-04-20       Impact factor: 6.931

10.  Genome-Scale Metabolic Models and Machine Learning Reveal Genetic Determinants of Antibiotic Resistance in Escherichia coli and Unravel the Underlying Metabolic Adaptation Mechanisms.

Authors:  Nicole Pearcy; Yue Hu; Michelle Baker; Alexandre Maciel-Guerra; Ning Xue; Wei Wang; Jasmeet Kaler; Zixin Peng; Fengqin Li; Tania Dottorini
Journal:  mSystems       Date:  2021-08-03       Impact factor: 6.496

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.