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Optimal programs of pathway control: dissecting the influence of pathway topology and feedback inhibition on pathway regulation

Overview of attention for article published in BMC Bioinformatics, May 2015
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  • Above-average Attention Score compared to outputs of the same age (52nd percentile)
  • Average Attention Score compared to outputs of the same age and source

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Title
Optimal programs of pathway control: dissecting the influence of pathway topology and feedback inhibition on pathway regulation
Published in
BMC Bioinformatics, May 2015
DOI 10.1186/s12859-015-0587-z
Pubmed ID
Authors

Gundián M de Hijas-Liste, Eva Balsa-Canto, Jan Ewald, Martin Bartl, Pu Li, Julio R Banga, Christoph Kaleta

Abstract

Adjusting the capacity of metabolic pathways in response to rapidly changing environmental conditions is an important component of microbial adaptation strategies to stochastic environments. In this work, we use advanced dynamic optimization techniques combined with theoretical models to study which reactions in pathways are optimally targeted by regulatory interactions in order to minimize the regulatory effort that is required to adjust the flux through a complex metabolic network. Moreover, we analyze how constraints in the speed at which an organism can respond on a proteomic level influences these optimal targets of pathway control. We find that limitations in protein biosynthetic rates have a strong influence. With increasing protein biosynthetic rates the regulatory effort targeting the initial enzyme in a pathway is reduced while the regulatory effort in the terminal enzyme is increased. Studying the impact of allosteric regulation for different pathway topologies, we find that the presence of feedback inhibition by products of metabolic pathways allows organisms to reduce the regulatory effort that is required to control a metabolic pathway in all cases. In a linear pathway this even leads to the case where the sole transcriptional regulatory control of the terminal enzyme is sufficient to control flux through the entire pathway. We confirm the utilization of these pathway regulation strategies through the large-scale analysis of transcriptional regulation in several hundred prokaryotes. This work expands our knowledge about optimal programs of pathway control. Optimal targets of pathway control strongly depend on the speed at which proteins can be synthesized. Moreover, post-translational regulation such as allosteric regulation allows to strongly reduce the number of transcriptional regulatory interactions required to control a metabolic pathway across different pathway topologies.

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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.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 52 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 3 6%
Germany 1 2%
Singapore 1 2%
Brazil 1 2%
Unknown 46 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 14 27%
Student > Ph. D. Student 12 23%
Student > Postgraduate 4 8%
Professor 3 6%
Student > Master 3 6%
Other 6 12%
Unknown 10 19%
Readers by discipline Count As %
Agricultural and Biological Sciences 13 25%
Biochemistry, Genetics and Molecular Biology 9 17%
Computer Science 4 8%
Engineering 3 6%
Medicine and Dentistry 2 4%
Other 7 13%
Unknown 14 27%
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 29 September 2015.
All research outputs
#13,200,930
of 22,803,211 outputs
Outputs from BMC Bioinformatics
#4,001
of 7,281 outputs
Outputs of similar age
#123,323
of 265,295 outputs
Outputs of similar age from BMC Bioinformatics
#72
of 121 outputs
Altmetric has tracked 22,803,211 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,281 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 42nd percentile – i.e., 42% 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 265,295 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 52% of its contemporaries.
We're also able to compare this research output to 121 others from the same source and published within six weeks on either side of this one. This one is in the 38th percentile – i.e., 38% of its contemporaries scored the same or lower than it.