Evidence map›Paper›PMID 33143595›Full record

ArticleJournal of the Royal Society, Interface2020

Modelling co-translational dimerization for programmable nonlinearity in synthetic biology.

Ruud Stoof, Ángel Goñi-Moreno

Abstract read
In one paragraph

Article in Journal of the Royal Society, Interface, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Why cellular computations challenge our design principles.Seminars in cell & developmental biology · 2025
    Review
  3. Article
  4. The nonlinearity of regulation in biological networks.NPJ systems biology and applications · 2023
    Article
  5. An electrogenetic toggle switch model.Microbial biotechnology · 2023
    Article
  6. 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

2 authors.

Ruud StoofSchool of Computing, Newcastle University, Urban Sciences Building, Science Square, Newcastle upon Tyne NE4 5TG, UK.
Ángel Goñi-MorenoSchool of Computing, Newcastle University, Urban Sciences Building, Science Square, Newcastle upon Tyne NE4 5TG, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Nonlinearity plays a fundamental role in the performance of both natural and synthetic biological networks. Key functional motifs in living microbial systems, such as the emergence of bistability or oscillations, rely on nonlinear molecular dynamics. Despite its core importance, the rational design of nonlinearity remains an unmet challenge. This is largely due to a lack of mathematical modelling that accounts for the mechanistic basis of nonlinearity. We introduce a model for gene regulatory circuits that explicitly simulates protein dimerization-a well-known source of nonlinear dynamics. Specifically, our approach focuses on modelling co-translational dimerization: the formation of protein dimers during-and not after-translation. This is in contrast to the prevailing assumption that dimer generation is only viable between freely diffusing monomers (i.e. post-translational dimerization). We provide a method for fine-tuning nonlinearity on demand by balancing the impact of co- versus post-translational dimerization. Furthermore, we suggest design rules, such as protein length or physical separation between genes, that may be used to adjust dimerization dynamics

Indexed as

Gene Regulatory NetworksSynthetic BiologyDimerizationModels, TheoreticalNonlinear Dynamicsgenetic circuitsmathematical modellingnonlinearityprotein dimerizationsynthetic biologysystems biology

Identifiers

PMID33143595
PMCPMC7729047

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.