Evidence map›Paper›PMID 24735079›Full record

ArticleACS synthetic biology2015

Trade-offs in engineering sugar utilization pathways for titratable control.

Taliman Afroz, Konstantinos Biliouris, Kelsey E Boykin, Yiannis Kaznessis, Chase L Beisel

Abstract read
In one paragraph

Article in ACS synthetic biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed.

  1. Article
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  4. Improvement ofMicroorganisms · 2020
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  9. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Taliman AfrozDepartment of Chemical and Biomolecular Engineering North Carolina State University Raleigh, North Carolina 27695, United States.
Konstantinos Biliouris
Kelsey E Boykin
Yiannis Kaznessis
Chase L Beisel

Funding

Multiscale Models for Synthetic BiologyR01GM086865 · NIGMS · UNIVERSITY OF MINNESOTA · PI KAZNESSIS, YIANNIS · 2009 to 2010
$497k
NIGMS NIH HHS GM086865NIGMS NIH HHS R01 GM086865
6 · The paper itself

Abstract

Titratable systems are common tools in metabolic engineering to tune the levels of enzymes and cellular components as part of pathway optimization. For nonmodel microorganisms with limited genetic tools, inducible sugar utilization pathways offer built-in titratable systems. However, these pathways can exhibit undesirable single-cell behaviors that hamper the uniform and tunable control of gene expression. Here, we applied mathematical modeling and single-cell measurements of L-arabinose utilization in Escherichia coli to systematically explore how sugar utilization pathways can be altered to achieve desirable inducible properties. We found that different pathway alterations, such as the removal of catabolism, constitutive expression of high-affinity or low-affinity transporters, or further deletion of the other transporters, came with trade-offs specific to each alteration. For instance, sugar catabolism improved the uniformity and linearity of the response at the cost of requiring higher sugar concentrations to induce the pathway. Within these alterations, we also found that a uniform and linear response could be achieved with a single alteration: constitutively expressing the high-affinity transporter. Equivalent modifications to the D-xylose utilization pathway yielded similar responses, demonstrating the applicability of our observations. Overall, our findings indicate that there is no ideal set of typical alterations when co-opting natural utilization pathways for titratable control and suggest design rules for manipulating these pathways to advance basic genetic studies and the metabolic engineering of microorganisms for optimized chemical production.

Indexed as

Metabolic EngineeringModels, TheoreticalArabinoseCarbohydrate MetabolismEscherichia coliArabinosecarbon catabolismEscherichia colifeedbackinducible systemslinearizationsystems biology

Identifiers

PMID24735079
PMCPMC4384834

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.