Evidence map›Paper›PMID 41158874›Full record

ArticleFrontiers in molecular biosciences2025

Role of intercellular interactions on single cell and population level responses: considerations for multicellular bioreporter design.

Douglas Lin, Michael Martin

Abstract read
In one paragraph

Article in Frontiers in molecular biosciences, 2025. 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

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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

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
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.

Douglas LinMath Science Technology Magnet, duPont Manual High School, Louisville, KY, United States.
Michael MartinMicro/Nano Technology Center, University of Louisville, Louisville, KY, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Bioreporters are genetically engineered cells that produce detectable responses in the presence of specific analytes, providing a cheap, mass-producible, and accurate method of analyte detection. Most research focuses on the single cell-level, where all engineering is concentrated on the interactions within a single cell. However, intercellular communication is a well-known natural phenomenon that has been associated with sensitive responses to certain chemical stimuli, yet incorporation of intercellular communication into bioreporter design is exceedingly rare and the effect of intercellular signaling on single-cellular and population level responses has not been explicitly characterized before. Methods: In this work, a multicellular simulator implementing the Gillespie algorithm and compatible with Virtual Cell-designed networks is created and used to demonstrate nuances to multicellular stochastic simulations. The algorithm was used to simulate multiple cells in a reaction network in which a self-promoting and membrane permeable transcription factor also induces production of a cell-bound reporter protein. A proof-of-concept bioreporter that responded to an environmental analyte while leveraging intercellular interactions for signal production was also designed and simulated for 50 s of simulation time. Simulated systems with multiple cells were compared to single-cellular simulations. Results: Simulations for 20 s of simulated time show that while final reporter protein count per cell decreased as cell count increased, aggregate final reporter protein number across all cells significantly increased. Interestingly, 50 s simulations show final reporter protein count per cell significantly increasing as the number of cells increases. Greater number of bioreporter cells resulted in significantly greater amounts of signal protein produced in response to the same amount of starting analyte both on the population level and on the individual cellular level. Discussion: The results show significant differences between the results of multicellular and single cellular simulations, which demonstrates the importance of simulating multiple cells to obtain nuanced results. The amplification of signal protein produced by an increasing number of simulated bioreporter cells indicates great potential for multicellular bioreporter designs to amplify response magnitude and sensitivity on the individual cellular level. These results warrant further research into the application of different simulation algorithms and multicellular bioreporter design and modeling.

Indexed as

bioreporterGillespie algorithmmulticellularstochastic simulationunicellularvalidation

Identifiers

PMID41158874
PMCPMC12554554

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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.