| Chapter title |
Novel Technologies for Optimal Strain Breeding
|
|---|---|
| Chapter number | 33 |
| Book title |
Amino Acid Fermentation
|
| Published in |
Advances in biochemical engineering biotechnology, January 2016
|
| DOI | 10.1007/10_2016_33 |
| Pubmed ID | |
| Book ISBNs |
978-4-43-156518-5, 978-4-43-156520-8
|
| Authors |
Michael Bott, Lothar Eggeling, Bott, Michael, Eggeling, Lothar |
| Abstract |
The implementation of a knowledge-based bioeconomy requires the rapid development of highly efficient microbial production strains that are able to convert renewable carbon sources to value-added products, such as bulk and fine chemicals, pharmaceuticals, or proteins at industrial scale. Starting from classical strain breeding by random mutagenesis and screening in the 1950s via rational design by metabolic engineering initiated in the 1970s, a range of powerful new technologies have been developed in the past two decades that can revolutionize future strain engineering. In particular, next-generation sequencing technologies combined with new methods of genome engineering and high-throughput screening based on genetically encoded biosensors have allowed for new concepts. In this chapter, selected new technologies relevant for breeding microbial production strains with a special emphasis on amino acid producers will be summarized. |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| China | 1 | 5% |
| Unknown | 19 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 6 | 30% |
| Other | 3 | 15% |
| Researcher | 3 | 15% |
| Student > Bachelor | 1 | 5% |
| Student > Doctoral Student | 1 | 5% |
| Other | 2 | 10% |
| Unknown | 4 | 20% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 6 | 30% |
| Agricultural and Biological Sciences | 3 | 15% |
| Environmental Science | 2 | 10% |
| Economics, Econometrics and Finance | 2 | 10% |
| Chemical Engineering | 1 | 5% |
| Other | 1 | 5% |
| Unknown | 5 | 25% |