↓ Skip to main content

Challenges and Advances for Genetic Engineering of Non-model Bacteria and Uses in Consolidated Bioprocessing

Overview of attention for article published in Frontiers in Microbiology, October 2017
Altmetric Badge

About this Attention Score

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

Mentioned by

twitter
6 X users

Citations

dimensions_citation
79 Dimensions

Readers on

mendeley
183 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.
Title
Challenges and Advances for Genetic Engineering of Non-model Bacteria and Uses in Consolidated Bioprocessing
Published in
Frontiers in Microbiology, October 2017
DOI 10.3389/fmicb.2017.02060
Pubmed ID
Authors

Qiang Yan, Stephen S. Fong

Abstract

Metabolic diversity in microorganisms can provide the basis for creating novel biochemical products. However, most metabolic engineering projects utilize a handful of established model organisms and thus, a challenge for harnessing the potential of novel microbial functions is the ability to either heterologously express novel genes or directly utilize non-model organisms. Genetic manipulation of non-model microorganisms is still challenging due to organism-specific nuances that hinder universal molecular genetic tools and translatable knowledge of intracellular biochemical pathways and regulatory mechanisms. However, in the past several years, unprecedented progress has been made in synthetic biology, molecular genetics tools development, applications of omics data techniques, and computational tools that can aid in developing non-model hosts in a systematic manner. In this review, we focus on concerns and approaches related to working with non-model microorganisms including developing molecular genetics tools such as shuttle vectors, selectable markers, and expression systems. In addition, we will discuss: (1) current techniques in controlling gene expression (transcriptional/translational level), (2) advances in site-specific genome engineering tools [homologous recombination (HR) and clustered regularly interspaced short palindromic repeats (CRISPR)], and (3) advances in genome-scale metabolic models (GSMMs) in guiding design of non-model species. Application of these principles to metabolic engineering strategies for consolidated bioprocessing (CBP) will be discussed along with some brief comments on foreseeable future prospects.

X Demographics

X Demographics

The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
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.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 183 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 183 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 37 20%
Student > Master 22 12%
Student > Bachelor 21 11%
Researcher 19 10%
Student > Doctoral Student 12 7%
Other 22 12%
Unknown 50 27%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 47 26%
Agricultural and Biological Sciences 28 15%
Engineering 13 7%
Chemical Engineering 12 7%
Immunology and Microbiology 11 6%
Other 15 8%
Unknown 57 31%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 11 November 2017.
All research outputs
#13,572,275
of 23,006,268 outputs
Outputs from Frontiers in Microbiology
#10,636
of 25,101 outputs
Outputs of similar age
#165,426
of 327,740 outputs
Outputs of similar age from Frontiers in Microbiology
#291
of 535 outputs
Altmetric has tracked 23,006,268 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 25,101 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.3. This one has gotten more attention than average, scoring higher than 54% 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 327,740 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 535 others from the same source and published within six weeks on either side of this one. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.