Speaker
Description
Inter-individual variability in biological systems is often considered experimental noise, yet its temporal structure can contain valuable information about underlying regulatory mechanisms. Here, we present a stochastic framework that uses fluctuations in cell population dynamics to infer regulatory processes that remain hidden when analyzing only mean behavior. Focusing on adult neural stem cells, we develop a stochastic model describing transitions between quiescent and active states and derive a diffusion approximation capturing the dynamics of both mean and variance of observable quantities.
Applying this approach to longitudinal data from wild-type and interferon-receptor knockout mice reveals that different regulatory mechanisms can produce similar average population dynamics while generating distinct fluctuation patterns. By jointly fitting mean and variance trajectories, we identify proliferation-rate regulation as the main determinant of fluctuation amplitude, whereas activation and self-renewal primarily control mean dynamics and long-term population outcomes.
These results demonstrate that stochastic variability is not merely noise but a rich source of mechanistic information that complements traditional analyses based solely on average dynamics.
This is join work with Anna Marciniak-Czochra, Diana-Patricia Danciu and Ren-Yi Wang.
