Title |
Predicting Translation Initiation Rates for Designing Synthetic Biology
|
---|---|
Published in |
Frontiers in Bioengineering and Biotechnology, January 2014
|
DOI | 10.3389/fbioe.2014.00001 |
Pubmed ID | |
Authors |
Benjamin Reeve, Thomas Hargest, Charlie Gilbert, Tom Ellis |
Abstract |
In synthetic biology, precise control over protein expression is required in order to construct functional biological systems. A core principle of the synthetic biology approach is a model-guided design and based on the biological understanding of the process, models of prokaryotic protein production have been described. Translation initiation rate is a rate-limiting step in protein production from mRNA and is dependent on the sequence of the 5'-untranslated region and the start of the coding sequence. Translation rate calculators are programs that estimate protein translation rates based on the sequence of these regions of an mRNA, and as protein expression is proportional to the rate of translation initiation, such calculators have been shown to give good approximations of protein expression levels. In this review, three currently available translation rate calculators developed for synthetic biology are considered, with limitations and possible future progress discussed. |
X Demographics
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 1 | 20% |
Unknown | 4 | 80% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 4 | 80% |
Scientists | 1 | 20% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 4 | 1% |
United States | 2 | <1% |
South Africa | 1 | <1% |
Chile | 1 | <1% |
Mexico | 1 | <1% |
New Zealand | 1 | <1% |
China | 1 | <1% |
Argentina | 1 | <1% |
Unknown | 260 | 96% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 71 | 26% |
Student > Master | 46 | 17% |
Researcher | 42 | 15% |
Student > Bachelor | 32 | 12% |
Other | 10 | 4% |
Other | 26 | 10% |
Unknown | 45 | 17% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 104 | 38% |
Agricultural and Biological Sciences | 77 | 28% |
Engineering | 16 | 6% |
Immunology and Microbiology | 7 | 3% |
Computer Science | 5 | 2% |
Other | 18 | 7% |
Unknown | 45 | 17% |