Evidence map›Paper›PMID 39086602›Full record

ReviewSynthetic biology (Oxford, England)2024

A new era of synthetic biology-microbial community design.

Anna Matuszyńska, Oliver Ebenhöh, Matias D Zurbriggen, Daniel C Ducat, Ilka M Axmann

Abstract readReview
In one paragraph

Review in Synthetic biology (Oxford, England), 2024. 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.

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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.

Anna MatuszyńskaComputational Life Science, Department of Biology, RWTH Aachen University, Aachen 52074, Germany.ORCID https://orcid.org/0000-0003-0882-6088
Oliver EbenhöhCluster of Excellence on Plant Sciences, CEPLAS, Heinrich Heine University Düsseldorf, Düsseldorf 40225, Germany.ORCID https://orcid.org/0000-0002-7229-7398
Matias D ZurbriggenCluster of Excellence on Plant Sciences, CEPLAS, Heinrich Heine University Düsseldorf, Düsseldorf 40225, Germany.
Daniel C DucatMSU-DOE Plant Research Laboratory, Michigan State University, East Lansing, MI 48824, United States.
Ilka M AxmannCluster of Excellence on Plant Sciences, CEPLAS, Heinrich Heine University Düsseldorf, Düsseldorf 40225, Germany.ORCID https://orcid.org/0000-0002-0856-8667

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Synthetic biology conceptualizes biological complexity as a network of biological parts, devices, and systems with predetermined functionalities and has had a revolutionary impact on fundamental and applied research. With the unprecedented ability to synthesize and transfer any DNA and RNA across organisms, the scope of synthetic biology is expanding and being recreated in previously unimaginable ways. The field has matured to a level where highly complex networks, such as artificial communities of synthetic organisms, can be constructed. In parallel, computational biology became an integral part of biological studies, with computational models aiding the unravelling of the escalating complexity and emerging properties of biological phenomena. However, there is still a vast untapped potential for the complete integration of modelling into the synthetic design process, presenting exciting opportunities for scientific advancements. Here, we first highlight the most recent advances in computer-aided design of microbial communities. Next, we propose that such a design can benefit from an organism-free modular modelling approach that places its emphasis on modules of organismal function towards the design of multispecies communities. We argue for a shift in perspective from single organism-centred approaches to emphasizing the functional contributions of organisms within the community. By assembling synthetic biological systems using modular computational models with mathematical descriptions of parts and circuits, we can tailor organisms to fulfil specific functional roles within the community. This approach aligns with synthetic biology strategies and presents exciting possibilities for the design of artificial communities.

Indexed as

community designcomputational biologymodular modelling approachsynthetic biologysynthetic communities

Identifiers

PMID39086602
PMCPMC11290361

What OpenQuestion holds

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Registered trials

None linked

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.