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Future Trends in Biotechnology

Overview of attention for book
Attention for Chapter 137: Systems Metabolic Engineering: The Creation of Microbial Cell Factories by Rational Metabolic Design and Evolution
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Chapter title
Systems Metabolic Engineering: The Creation of Microbial Cell Factories by Rational Metabolic Design and Evolution
Chapter number 137
Book title
Future Trends in Biotechnology
Published in
Advances in biochemical engineering biotechnology, June 2012
DOI 10.1007/10_2012_137
Pubmed ID
Book ISBNs
978-3-64-236507-2, 978-3-64-236508-9
Authors

Chikara Furusawa, Takaaki Horinouchi, Takashi Hirasawa, Hiroshi Shimizu

Abstract

It is widely acknowledged that in order to establish sustainable societies, production processes should shift from petrochemical-based processes to bioprocesses. Because bioconversion technologies, in which biomass resources are converted to valuable materials, are preferable to processes dependent on fossil resources, the former should be further developed. The following two approaches can be adopted to improve cellular properties and obtain high productivity and production yield of target products: (1) optimization of cellular metabolic pathways involved in various bioprocesses and (2) creation of stress-tolerant cells that can be active even under severe stress conditions in the bioprocesses. Recent progress in omics analyses has facilitated the analysis of microorganisms based on bioinformatics data for molecular breeding and bioprocess development. Systems metabolic engineering is a new area of study, and it has been defined as a methodology in which metabolic engineering and systems biology are integrated to upgrade the designability of industrially useful microorganisms. This chapter discusses multi-omics analyses and rational design methods for molecular breeding. The first is an example of the rational design of metabolic networks for target production by flux balance analysis using genome-scale metabolic models. Recent progress in the development of genome-scale metabolic models and the application of these models to the design of desirable metabolic networks is also described in this example. The second is an example of evolution engineering with omics analyses for the creation of stress-tolerant microorganisms. Long-term culture experiments to obtain the desired phenotypes and omics analyses to identify the phenotypic changes are described here.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 55 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 4%
China 1 2%
Singapore 1 2%
Belgium 1 2%
Unknown 50 91%

Demographic breakdown

Readers by professional status Count As %
Researcher 13 24%
Student > Ph. D. Student 11 20%
Student > Bachelor 5 9%
Professor 5 9%
Student > Master 5 9%
Other 10 18%
Unknown 6 11%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 20 36%
Agricultural and Biological Sciences 16 29%
Engineering 5 9%
Physics and Astronomy 2 4%
Medicine and Dentistry 1 2%
Other 1 2%
Unknown 10 18%
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 04 July 2012.
All research outputs
#18,309,495
of 22,669,724 outputs
Outputs from Advances in biochemical engineering biotechnology
#146
of 224 outputs
Outputs of similar age
#126,274
of 164,182 outputs
Outputs of similar age from Advances in biochemical engineering biotechnology
#4
of 4 outputs
Altmetric has tracked 22,669,724 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 224 research outputs from this source. They receive a mean Attention Score of 4.1. This one is in the 20th percentile – i.e., 20% 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 164,182 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 9th percentile – i.e., 9% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 4 others from the same source and published within six weeks on either side of this one.