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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.<\/jats:p>","DOI":"10.64898\/2026.05.22.727214","type":"posted-content","created":{"date-parts":[[2026,5,25]],"date-time":"2026-05-25T20:00:14Z","timestamp":1779739214000},"source":"Crossref","is-referenced-by-count":1,"title":["Highly Constrained Kinetic Models for Single-Cell Gene Expression Analysis"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5781-1002","authenticated-orcid":false,"given":"Hyeon Jin","family":"Cho","sequence":"first","affiliation":[{"name":"Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD, USA"},{"name":"Laboratory of Receptor Biology and Gene Expression, National Cancer Institute, NIH, Bethesda, MD, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christopher 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