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Synthetic Metabolic Pathways

Overview of attention for book
Cover of 'Synthetic Metabolic Pathways'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Parts Characterization for Tunable Protein Expression
  3. Altmetric Badge
    Chapter 2 Enzyme Nicotinamide Cofactor Specificity Reversal Guided by Automated Structural Analysis and Library Design
  4. Altmetric Badge
    Chapter 3 Bacterial Genome Editing Strategy for Control of Transcription and Protein Stability
  5. Altmetric Badge
    Chapter 4 An Automated Pipeline for Engineering Many-Enzyme Pathways: Computational Sequence Design, Pathway Expression-Flux Mapping, and Scalable Pathway Optimization
  6. Altmetric Badge
    Chapter 5 Computational Approaches on Stoichiometric and Kinetic Modeling for Efficient Strain Design
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    Chapter 6 Extended Metabolic Space Modeling
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    Chapter 7 Computational Methods to Assess the Production Potential of Bio-Based Chemicals
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    Chapter 8 Multiplex Genome Editing in Escherichia coli
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    Chapter 9 Designing and Implementing Algorithmic DNA Assembly Pipelines for Multi-Gene Systems
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    Chapter 10 An Adaptive Laboratory Evolution Method to Accelerate Autotrophic Metabolism
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    Chapter 11 CRISPR-Cas9 Toolkit for Actinomycete Genome Editing
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    Chapter 12 Assembly and Multiplex Genome Integration of Metabolic Pathways in Yeast Using CasEMBLR
  14. Altmetric Badge
    Chapter 13 A Modified Gibson Assembly Method for Cloning Large DNA Fragments with High GC Contents
  15. Altmetric Badge
    Chapter 14 Coupling Yeast Golden Gate and VEGAS for Efficient Assembly of the Violacein Pathway in Saccharomyces cerevisiae
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    Chapter 15 Multi-capillary Column Ion Mobility Spectrometry of Volatile Metabolites for Phenotyping of Microorganisms
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    Chapter 16 Selection of Highly Expressed Gene Variants in Escherichia coli Using Translationally Coupled Antibiotic Selection Markers
  18. Altmetric Badge
    Chapter 17 Design, Engineering, and Characterization of Prokaryotic Ligand-Binding Transcriptional Activators as Biosensors in Yeast
  19. Altmetric Badge
    Chapter 18 A Capture-SELEX Strategy for Multiplexed Selection of RNA Aptamers Against Small Molecules
  20. Altmetric Badge
    Chapter 19 High-Throughput Microfluidics for the Screening of Yeast Libraries
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    Chapter 20 Growth-Coupled Carotenoids Production Using Adaptive Laboratory Evolution
  22. Altmetric Badge
    Chapter 21 Two-Scale 13C Metabolic Flux Analysis for Metabolic Engineering
Attention for Chapter 4: An Automated Pipeline for Engineering Many-Enzyme Pathways: Computational Sequence Design, Pathway Expression-Flux Mapping, and Scalable Pathway Optimization
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (55th percentile)
  • High Attention Score compared to outputs of the same age and source (83rd percentile)

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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.

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X Demographics

The data shown below were collected from the profiles of 7 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 24 Mendeley readers of this research output. Click here to see the associated Mendeley record.

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%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 04 July 2018.
All research outputs
#7,541,834
of 23,008,860 outputs
Outputs from Methods in molecular biology
#2,339
of 13,157 outputs
Outputs of similar age
#153,693
of 442,295 outputs
Outputs of similar age from Methods in molecular biology
#230
of 1,498 outputs
Altmetric has tracked 23,008,860 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,157 research outputs from this source. They receive a mean Attention Score of 3.4. This one has done well, scoring higher than 76% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 442,295 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 55% of its contemporaries.
We're also able to compare this research output to 1,498 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 83% of its contemporaries.