Evidence map›Paper›PMID 37853171›Full record

ArticleCommunications chemistry2023

Generation of DNA oligomers with similar chemical kinetics via in-silico optimization.

Michael Tobiason, Bernard Yurke, William L Hughes

Abstract read
In one paragraph

Article in Communications chemistry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Michael TobiasonDepartment of Computer Science, Boise State University, Boise, ID, USA. michaeltobiason@u.boisestate.edu.ORCID http://orcid.org/0000-0002-6667-9736
Bernard YurkeMicron School of Materials Science & Engineering, Boise State University, Boise, ID, USA.ORCID http://orcid.org/0000-0003-3913-2855
William L HughesSchool of Engineering, University of British Columbia Okanagan Campus, Kelowna, BC, Canada. will.hughes@ubc.ca.ORCID http://orcid.org/0000-0003-0102-3573

Funding

Low Cost Diagnosis of Disease using Synthetic DNA ReactionsK25GM093233 · NIGMS · BOISE STATE UNIVERSITY · PI HUGHES, WILLIAM L. · 2011 to 2015
$686k
NIGMS NIH HHS K25 GM093233
6 · The paper itself

Abstract

Networks of interacting DNA oligomers are useful for applications such as biomarker detection, targeted drug delivery, information storage, and photonic information processing. However, differences in the chemical kinetics of hybridization reactions, referred to as kinetic dispersion, can be problematic for some applications. Here, it is found that limiting unnecessary stretches of Watson-Crick base pairing, referred to as unnecessary duplexes, can yield exceptionally low kinetic dispersions. Hybridization kinetics can be affected by unnecessary intra-oligomer duplexes containing only 2 base-pairs, and such duplexes explain up to 94% of previously reported kinetic dispersion. As a general design rule, it is recommended that unnecessary intra-oligomer duplexes larger than 2 base-pairs and unnecessary inter-oligomer duplexes larger than 7 base-pairs be avoided. Unnecessary duplexes typically scale exponentially with network size, and nearly all networks contain unnecessary duplexes substantial enough to affect hybridization kinetics. A new method for generating networks which utilizes in-silico optimization to mitigate unnecessary duplexes is proposed and demonstrated to reduce in-vitro kinetic dispersions as much as 96%. The limitations of the new design rule and generation method are evaluated in-silico by creating new oligomers for several designs, including three previously programmed reactions and one previously engineered structure.

Identifiers

PMID37853171
PMCPMC10584830

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Registered trials

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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.