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Microbial Methylotrophic Metabolism: Recent Metabolic Modeling Efforts and Their Applications In Industrial Biotechnology
- Source :
- Lieven, C, Herrgård, M J & Sonnenschein, N 2018, ' Microbial Methylotrophic Metabolism: Recent Metabolic Modeling Efforts and Their Applications In Industrial Biotechnology ', Biotechnology Journal, vol. 13, no. 8, 1800011 . https://doi.org/10.1002/biot.201800011
- Publication Year :
- 2018
- Publisher :
- Wiley, 2018.
-
Abstract
- Developing methylotrophic bacteria into cell factories that meet the chemical demand of the future could be both economical and environmentally friendly. Methane is not only an abundant, low‐cost resource but also a potent greenhouse gas, the capture of which could help to reduce greenhouse gas emissions. Rational strain design workflows rely on the availability of carefully combined knowledge often in the form of genome‐scale metabolic models to construct high‐producer organisms. In this review, the authors present the most recent genome‐scale metabolic models in aerobic methylotrophy and their applications. Further, the authors present models for the study of anaerobic methanotrophy through reverse methanogenesis and suggest organisms that may be of interest for expanding one‐carbon industrial biotechnology. Metabolic models of methylotrophs are scarce, yet they are important first steps toward rational strain‐design in these organisms.
- Subjects :
- 0301 basic medicine
Methanogenesis
One-carbon
Methanococcus
General Medicine
Industrial biotechnology
Models, Biological
Applied Microbiology and Biotechnology
Environmentally friendly
Metabolic modeling
Industrial Microbiology
03 medical and health sciences
Methylobacterium
030104 developmental biology
Greenhouse gas
Molecular Medicine
Environmental science
Anaerobiosis
Biochemical engineering
Methylotrophy
Methane
Cell factories
COBRA
Biotechnology
Subjects
Details
- ISSN :
- 18606768
- Volume :
- 13
- Database :
- OpenAIRE
- Journal :
- Biotechnology Journal
- Accession number :
- edsair.doi.dedup.....62531116ed921906e74c22410aae73e3
- Full Text :
- https://doi.org/10.1002/biot.201800011