Evidence map›Paper›PMID 42244749›Full record

ArticlebioRxiv : the preprint server for biology2026

Highly Constrained Kinetic Models for Single-Cell Gene Expression Analysis.

Hyeon Jin Cho, Christopher H Bohrer, Pawel Trzaskoma, Jee Min Kim, Aleksandra Pękowska, Rafael C Casellas, Rob Patro, Carson C Chow, Daniel R Larson

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

9 authors.

Hyeon Jin ChoDepartment of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD, USA.ORCID 0000-0002-5781-1002
Christopher H BohrerLaboratory of Receptor Biology and Gene Expression, National Cancer Institute, NIH, Bethesda, MD, USA.
Pawel TrzaskomaMolecular Immunology and Inflammation Branch, National Institute of Arthritis and Musculoskeletal and Skin Diseases, NIH, Bethesda, MD, USA.
Jee Min KimLaboratory of Receptor Biology and Gene Expression, National Cancer Institute, NIH, Bethesda, MD, USA.ORCID 0000-0002-9353-6431
Aleksandra PękowskaDioscuri Center for Chromatin Biology and Epigenomics, Nencki Institute of Experimental Biology, Polish Academy of Science, Warsaw, Poland.
Rafael C CasellasDepartment of Hematopoietic Biology & Malignancy, Division of Cancer Medicine, MD Anderson Cancer Center, Houston, TX.
Rob PatroDepartment of Computer Science and Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD, USA.ORCID 0000-0001-8463-1675
Carson C ChowLaboratory of Biological Modeling, National Institute of Diabetes and Digestive and Kidney Diseases, NIH, Bethesda, MD, USA.ORCID 0000-0003-1463-9553
Daniel R LarsonLaboratory of Receptor Biology and Gene Expression, National Cancer Institute, NIH, Bethesda, MD, USA.ORCID 0000-0001-9253-3055

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Advances in single-cell RNA sequencing (scRNA-seq) and high-resolution imaging techniques, such as single-molecule tracking (SMT) of RNA and transcription factors, allow researchers to quantitatively explore dynamics and variation but have never been integrated into a single coherent model. In this study, we propose a kinetic model that intakes multiple data types, including steady-state and time-resolved datasets, to simulate and fit stochastic models of gene transcription to experimental data. We find that 3-state models provide an essential improvement over the widely used 2-state model for most genes and have the property of kinetic proofreading, which we argue is advantageous in the cellular context. We further identify two dimensionless quantities derived from the rate equations which are broadly conserved across genes. Finally, we extend this model to scRNA-seq datasets to infer kinetic rates under defined perturbations and reveal biochemical insight into the mechanism of action of transcription factors.

Indexed as

kinetic proofreadingStochastic modelingtranscription burstingtranscription dynamics

Identifiers

PMID42244749
PMCPMC13232213

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.