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bioRxiv · 10.64898/2026.01.22.701043

Harnessing Biological Variability for Mechanistic Inference: A Practical Stochastic Framework

Wang, R.-Y.·Danciu, D.-P.·Klawe, F. Z.·Marciniak-Czochra, A.
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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.

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Wang, R.-Y., Danciu, D.-P., Klawe, F. Z., Marciniak-Czochra, A.. 2026-01-22. Harnessing Biological Variability for Mechanistic Inference: A Practical Stochastic Framework. https://doi.org/10.64898/2026.01.22.701043

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