Evidence map›Paper›PMID 30040895›Full record

ArticleACS synthetic biology2018

Predicting Transcriptional Output of Synthetic Multi-input Promoters.

David M Zong, Selahittin Cinar, David L Shis, Krešimir Josić, William Ott, Matthew R Bennett

Abstract read
In one paragraph

Article in ACS synthetic biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

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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

6 authors.

David M Zong
Selahittin CinarDepartment of Mathematics , ⊥Department of Biology and Biochemistry , University of Houston , Houston , Texas 77004 , United States.
David L Shis
Krešimir JosićDepartment of Mathematics , ⊥Department of Biology and Biochemistry , University of Houston , Houston , Texas 77004 , United States.
William OttDepartment of Mathematics , ⊥Department of Biology and Biochemistry , University of Houston , Houston , Texas 77004 , United States.
Matthew R BennettORCID 0000-0002-4975-8854

Funding

Expanding the utility of transcriptional bacterial computingR01GM117138 · NIGMS · RICE UNIVERSITY · PI BENNETT, MATTHEW R., JOSIC, KRESIMIR · 2016 to 2019
$2.0M
NIGMS NIH HHS R01 GM117138
6 · The paper itself

Abstract

Recent advances in synthetic biology have led to a wealth of well-characterized genetic parts. As parts libraries grow, so too does the potential to create novel multi-input promoters that integrate disparate signals to determine transcriptional output. Our ability to construct such promoters will outpace our ability to characterize promoter performance, due to the vast number of input combinations. In this study, we examine the input-output relations of recently developed synthetic multi-input promoters and describe two methods for predicting their behavior. The first method uses 1-dimensional induction data obtained from experiments on single-input systems to predict the n-dimensional induction responses of systems with n inputs. We demonstrate that this approach accurately predicts Boolean (on/off) responses of multi-input systems consisting of novel chimeric transcription factors and hybrid promoters in Escherichia coli. The second method uses only a small amount of multi-input response data to accurately predict analog system response over the entire landscape of input combinations. Taken together, these methods facilitate the design of synthetic circuits that utilize multi-input promoters.

Indexed as

Escherichia coliPromoter Regions, GeneticSynthetic BiologyTranscription, Geneticchimeric transcription factorsmulti-input synthetic hybrid promoterstranscriptional logic gatestranscriptional noise

Identifiers

PMID30040895
PMCPMC6608594

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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.