| Chapter title |
Transposon-based and plasmid-based genetic tools for editing genomes of gram-negative bacteria.
|
|---|---|
| Chapter number | 16 |
| Book title |
Synthetic Gene Networks
|
| Published in |
Methods in molecular biology, January 2012
|
| DOI | 10.1007/978-1-61779-412-4_16 |
| Pubmed ID | |
| Book ISBNs |
978-1-61779-411-7, 978-1-61779-412-4
|
| Authors |
Esteban Martínez-García, Víctor de Lorenzo, Martínez-García, Esteban, Lorenzo, Víctor, Lorenzo, Víctor de |
| Editors |
Wilfried Weber, Martin Fussenegger |
| Abstract |
A good part of the contemporary synthetic biology agenda aims at reprogramming microorganisms to enhance existing functions and/or perform new tasks. Moreover, the functioning of complex regulatory networks, or even a single gene, is revealed only when perturbations are entered in the corresponding dynamic systems and the outcome monitored. These endeavors rely on the availability of genetic tools to successfully modify á la carte the chromosome of target bacteria. Key aspects to this end include the removal of undesired genomic segments, systems for the production of directed mutants and allelic replacements, random mutant libraries to discover new functions, and means to stably implant larger genetic networks into the genome of specific hosts. The list of gram-negative species that are appealing for such genetic refactoring operations is growingly expanding. However, the repertoire of available molecular techniques to do so is very limited beyond Escherichia coli. In this chapter, utilization of novel tools is described (exemplified in two plasmids systems: pBAM1 and pEMG) tailored for facilitating chromosomal engineering procedures in a wide variety of gram-negative microorganisms. |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Denmark | 3 | 3% |
| Germany | 1 | <1% |
| Uruguay | 1 | <1% |
| Brazil | 1 | <1% |
| China | 1 | <1% |
| Spain | 1 | <1% |
| United States | 1 | <1% |
| Unknown | 100 | 92% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 21 | 19% |
| Researcher | 20 | 18% |
| Student > Master | 14 | 13% |
| Student > Bachelor | 12 | 11% |
| Student > Doctoral Student | 6 | 6% |
| Other | 14 | 13% |
| Unknown | 22 | 20% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 42 | 39% |
| Biochemistry, Genetics and Molecular Biology | 26 | 24% |
| Immunology and Microbiology | 6 | 6% |
| Chemical Engineering | 4 | 4% |
| Engineering | 2 | 2% |
| Other | 5 | 5% |
| Unknown | 24 | 22% |