Chapter title |
An Automated Pipeline for Engineering Many-Enzyme Pathways: Computational Sequence Design, Pathway Expression-Flux Mapping, and Scalable Pathway Optimization
|
---|---|
Chapter number | 4 |
Book title |
Synthetic Metabolic Pathways
|
Published in |
Methods in molecular biology, January 2018
|
DOI | 10.1007/978-1-4939-7295-1_4 |
Pubmed ID | |
Book ISBNs |
978-1-4939-7294-4, 978-1-4939-7295-1
|
Authors |
Sean M. Halper, Daniel P. Cetnar, Howard M. Salis, Halper, Sean M., Cetnar, Daniel P., Salis, Howard M. |
Abstract |
Engineering many-enzyme metabolic pathways suffers from the design curse of dimensionality. There are an astronomical number of synonymous DNA sequence choices, though relatively few will express an evolutionary robust, maximally productive pathway without metabolic bottlenecks. To solve this challenge, we have developed an integrated, automated computational-experimental pipeline that identifies a pathway's optimal DNA sequence without high-throughput screening or many cycles of design-build-test. The first step applies our Operon Calculator algorithm to design a host-specific evolutionary robust bacterial operon sequence with maximally tunable enzyme expression levels. The second step applies our RBS Library Calculator algorithm to systematically vary enzyme expression levels with the smallest-sized library. After characterizing a small number of constructed pathway variants, measurements are supplied to our Pathway Map Calculator algorithm, which then parameterizes a kinetic metabolic model that ultimately predicts the pathway's optimal enzyme expression levels and DNA sequences. Altogether, our algorithms provide the ability to efficiently map the pathway's sequence-expression-activity space and predict DNA sequences with desired metabolic fluxes. Here, we provide a step-by-step guide to applying the Pathway Optimization Pipeline on a desired multi-enzyme pathway in a bacterial host. |
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 States | 2 | 29% |
India | 1 | 14% |
Australia | 1 | 14% |
Unknown | 3 | 43% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 6 | 86% |
Scientists | 1 | 14% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 24 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 10 | 42% |
Researcher | 4 | 17% |
Student > Doctoral Student | 2 | 8% |
Student > Master | 2 | 8% |
Student > Bachelor | 1 | 4% |
Other | 2 | 8% |
Unknown | 3 | 13% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 10 | 42% |
Agricultural and Biological Sciences | 4 | 17% |
Engineering | 2 | 8% |
Nursing and Health Professions | 1 | 4% |
Medicine and Dentistry | 1 | 4% |
Other | 1 | 4% |
Unknown | 5 | 21% |