Chapter title |
Retrosynthetic design of heterologous pathways.
|
---|---|
Chapter number | 9 |
Book title |
Systems Metabolic Engineering
|
Published in |
Methods in molecular biology, January 2013
|
DOI | 10.1007/978-1-62703-299-5_9 |
Pubmed ID | |
Book ISBNs |
978-1-62703-298-8, 978-1-62703-299-5
|
Authors |
Carbonell P, Planson AG, Faulon JL, Carbonell, Pablo, Planson, Anne-Gaëlle, Faulon, Jean-Loup, Pablo Carbonell, Anne-Gaëlle Planson, Jean-Loup Faulon |
Abstract |
Tools from metabolic engineering and synthetic biology are synergistically used in order to develop high-performance cell factories. However, the number of successful applications has been limited due to the complexity of exploring efficiently the metabolic space for the discovery of candidate heterologous pathways. To address this challenge, retrosynthetic biology provides an integrated framework to formalize and rationalize the problem of importing biosynthetic pathways into a chassis organism using methods at the interface from bottom-up and top-down strategies. Here, we describe step by step the process of implementing a retrosynthetic framework for the design of heterologous biosynthetic pathways in a chassis organism. The method consists of the following steps: choosing the chassis and the target, selection of an in silico model for the chassis, definition of the metabolic space, pathway enumeration, gene selection, estimation of yields, toxicity prediction of pathway metabolites, definition of an objective function to select the best pathway candidates, and pathway implementation and verification. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Iran, Islamic Republic of | 1 | 2% |
France | 1 | 2% |
Thailand | 1 | 2% |
Unknown | 59 | 95% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 17 | 27% |
Researcher | 11 | 18% |
Student > Master | 7 | 11% |
Professor | 4 | 6% |
Student > Doctoral Student | 4 | 6% |
Other | 10 | 16% |
Unknown | 9 | 15% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 20 | 32% |
Biochemistry, Genetics and Molecular Biology | 15 | 24% |
Engineering | 4 | 6% |
Immunology and Microbiology | 4 | 6% |
Computer Science | 3 | 5% |
Other | 5 | 8% |
Unknown | 11 | 18% |