↓ Skip to main content

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
  7. Altmetric Badge
    Chapter 6 Extended Metabolic Space Modeling
  8. Altmetric Badge
    Chapter 7 Computational Methods to Assess the Production Potential of Bio-Based Chemicals
  9. Altmetric Badge
    Chapter 8 Multiplex Genome Editing in Escherichia coli
  10. Altmetric Badge
    Chapter 9 Designing and Implementing Algorithmic DNA Assembly Pipelines for Multi-Gene Systems
  11. Altmetric Badge
    Chapter 10 An Adaptive Laboratory Evolution Method to Accelerate Autotrophic Metabolism
  12. Altmetric Badge
    Chapter 11 CRISPR-Cas9 Toolkit for Actinomycete Genome Editing
  13. Altmetric Badge
    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
  16. Altmetric Badge
    Chapter 15 Multi-capillary Column Ion Mobility Spectrometry of Volatile Metabolites for Phenotyping of Microorganisms
  17. Altmetric Badge
    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
  21. Altmetric Badge
    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 19: High-Throughput Microfluidics for the Screening of Yeast Libraries
Altmetric Badge

About this Attention Score

  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
2 X users

Readers on

mendeley
32 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Chapter title
High-Throughput Microfluidics for the Screening of Yeast Libraries
Chapter number 19
Book title
Synthetic Metabolic Pathways
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-7295-1_19
Pubmed ID
Book ISBNs
978-1-4939-7294-4, 978-1-4939-7295-1
Authors

Mingtao Huang, Haakan N. Joensson, Jens Nielsen

Abstract

Cell factory development is critically important for efficient biological production of chemicals, biofuels, and pharmaceuticals. Many rounds of the Design-Build-Test-Learn cycles may be required before an engineered strain meeting specific metrics required for industrial application. The bioindustry prefer products in secreted form (secreted products or extracellular metabolites) as it can lower the cost of downstream processing, reduce metabolic burden to cell hosts, and allow necessary modification on the final products , such as biopharmaceuticals. Yet, products in secreted form result in the disconnection of phenotype from genotype, which may have limited throughput in the Test step for identification of desired variants from large libraries of mutant strains. In droplet microfluidic screening, single cells are encapsulated in individual droplet and enable high-throughput processing and sorting of single cells or clones. Encapsulation in droplets allows this technology to overcome the throughput limitations present in traditional methods for screening by extracellular phenotypes. In this chapter, we describe a protocol/guideline for high-throughput droplet microfluidics screening of yeast libraries for higher protein secretion . This protocol can be adapted to screening by a range of other extracellular products from yeast or other hosts.

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 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 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 32 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 22%
Student > Master 5 16%
Student > Ph. D. Student 5 16%
Student > Doctoral Student 3 9%
Other 2 6%
Other 4 13%
Unknown 6 19%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 11 34%
Agricultural and Biological Sciences 8 25%
Chemical Engineering 2 6%
Pharmacology, Toxicology and Pharmaceutical Science 2 6%
Immunology and Microbiology 2 6%
Other 2 6%
Unknown 5 16%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 27 November 2017.
All research outputs
#17,920,654
of 23,008,860 outputs
Outputs from Methods in molecular biology
#7,286
of 13,157 outputs
Outputs of similar age
#310,312
of 442,295 outputs
Outputs of similar age from Methods in molecular biology
#868
of 1,498 outputs
Altmetric has tracked 23,008,860 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% 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 is in the 39th percentile – i.e., 39% of its peers scored the same or lower than it.
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 is in the 25th percentile – i.e., 25% of its contemporaries scored the same or lower than it.
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 is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.