European Conference on Mathematical and Theoretical Biology (ECMTB 2026)

Detection Noise Renormalizes Apparent Kinetic Rates in Stochastic Gene Regulatory Networks

MS77-02

16 Jul 2026, 18:00

20m

11.02 - HS (University of Graz)

Minisymposium Talk Systems Biology and Biochemical Networks Mechanistic Model Inference for Stochastic Single-Cell Dynamics

Speaker

Iryna Zabaikina (Comenius University)

Description

Imperfect molecular detection in single-cell experiments introduces technical noise that can distort the observed dynamics of gene regulatory networks, hence complicating the inference of the true kinetic parameters from single-cell data. We extend binomial capture models from simple gene-expression systems to general, possibly time-dependent, regulatory networks, using both chemical master equation and piecewise-deterministic Markov process descriptions. Our main result is that the effects of technical noise can be absorbed into the renormalization of a subset of the kinetic rates. This occurs when transcription factor abundance is not too small. In this regime, imperfect capture leads to apparently smaller mean burst sizes of gene products and apparently larger transcription factor binding rates. Together, these results provide a systematic framework for interpreting noisy single-cell measurements.

Bibliography

@misc{zabaikina_imperfect_2025,
title = {Imperfect molecular detection renormalizes apparent kinetic rates in stochastic gene regulatory networks},
copyright = {Creative Commons Attribution Share Alike 4.0 International},
url = {https://arxiv.org/abs/2512.02908},
doi = {10.48550/ARXIV.2512.02908},
urldate = {2026-03-28},
publisher = {arXiv},
author = {Zabaikina, Iryna and Grima, Ramon},
year = {2025},
keywords = {Molecular Networks (q-bio.MN), Quantitative Methods (q-bio.QM), Subcellular Processes (q-bio.SC), FOS: Biological sciences, FOS: Biological sciences},
}