Evidence map›Paper›PMID 35606858›Full record

ArticleJournal of biological engineering2022

Robust network topologies for temperature-inducible bioswitches.

Di Wu, Hongli Wang, Qi Ouyang

Abstract read
In one paragraph

Article in Journal of biological engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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.

Di WuThe State Key Laboratory for Artificial Microstructures and Mesoscopic Physics, School of Physics, Peking University, Beijing, 100871, China.
Hongli WangThe State Key Laboratory for Artificial Microstructures and Mesoscopic Physics, School of Physics, Peking University, Beijing, 100871, China. hlwang@pku.edu.cn.
Qi OuyangThe State Key Laboratory for Artificial Microstructures and Mesoscopic Physics, School of Physics, Peking University, Beijing, 100871, China.

Funding

Major Program of National Natural Science Foundation of China 12090051National Key Research and Development Program of China 2018YFA0900200National Natural Science Foundation of China 11434001
6 · The paper itself

Abstract

backgroundThermoinducible bioswitches are unique in that the all-or-none switch response is triggered by temperature, which is a global factor that impacts all biochemical reaction processes. To date, temperature-inducible bioswitches rely exclusively on special thermal sensing biomolecules of DNA, RNA, proteins and lipids whose conformations are critically temperature dependent.

methodThis paper extends the traditional thermal switch by utilizing purposely designed network topologies of biomolecular interactions to achieve the switching function. By assuming the general Arrhenius law for biochemical reactions, we explore the full space of all three-node genetic interaction networks to screen topologies capable of thermal bioswitches. Three target bioswitches, i.e., thermal-inducible Off-On, cold-inducible On-Off, and hybrid Off-On-Off double switches, are considered separately.

conclusionsWe identify the minimal and core network skeletons that are basic and essential for building robust high-performance bioswitches: three Off-On motifs, three On-Off motifs, and an incoherent feedforward motif for an Off-On-Off double switch. Functional topologies are implicitly preferential in choosing parameter values to achieve the target functions. The scenario of the topology-based bioswitch we propose here is an extension of molecule-based bioswitches and would be valuable in aiding the rational design and synthesis of efficient high-performance thermal bioswitches.

Indexed as

Genetic circuitsNetwork motifThermoinducible bioswitch

Identifiers

PMID35606858
PMCPMC9128120

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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.