Harnessing biological variability for mechanistic inference: A stochastic framework applied to neural stem cell dynamics.

Authors

Ren-Yi Wang, Diana-Patricia Danciu, Filip Z Klawe, Anna Marciniak-Czochra

Published in

iScience. Volume 29. Issue 9. Pages 117269. Sep 18, 2026. Epub Aug 22, 2026.

Abstract

Inter-individual heterogeneity is often treated as noise, yet its temporal evolution can reveal regulatory mechanisms hidden from mean-field behavior. We present a stochastic framework that exploits variability for mechanistic inference in cell population dynamics. Using adult neurogenesis as a case study, we develop a state-dependent stochastic model of transitions between quiescent and active states and derive a diffusion approximation for the dynamics of both mean and variance. Applied to repeated cross-sectional data from wild-type and interferon-receptor knockout mice, we show that distinct regulatory mechanisms can produce similar mean dynamics but different fluctuation patterns. Jointly fitting mean and variance identifies proliferation-rate regulation as the dominant contributor to variability, while activation and self-renewal primarily govern average and long-term dynamics. Wild-type mice exhibit regulation of all three processes, whereas knockout mice lose activation control. These results show that population-level variability provides mechanistic information beyond average dynamics and helps distinguish between competing mechanistic models.

PMID:
42668621
Bibliographic data and abstract were imported from PubMed on 30 Aug 2026.

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