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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Inter-individual variability is often treated as noise, yet its temporal evolution can reveal regulatory mechanisms hidden from mean behavior. We present a stochastic framework that exploits fluctuations to infer regulatory mechanisms in cell population dynamics. Focusing on adult neural stem cells, we build a state-dependent stochastic model of transitions between quiescent and active states and derive a diffusion approximation for mean and variance trajectories of observable quantities. Applied to longitudinal data from wild-type and interferon-receptor knockout mice, we show that distinct mechanisms produce similar mean dynamics but different fluctuation patterns. Fitting both mean and variance dynamics reveals proliferationrate regulation as the dominant driver of fluctuation amplitude, while activation and self-renewal mainly shape mean behavior and long-term fate. In wild-type mice all three processes are regulated, but knockout mice lose activation control. This demonstrates that stochastic variability is a valuable source of mechanistic information beyond average dynamics.
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