A Time-Varying Branching Process Approach to Model Self-Renewing Cells

2026-01-29

AI Summary

This study addresses the challenge of modeling stem cell self-renewal and proliferation dynamics under partial observability of daughter cell types by proposing a continuous-time branching process model with time-varying offspring distributions. The model introduces, for the first time, time-dependent division probabilities to characterize stem cell fate decisions and derives analytical expressions for the mean, variance, and autocovariance of cell population counts. To handle partially observed data, a likelihood-based inference framework grounded in the forward algorithm is developed for efficient parameter estimation. Simulations demonstrate that the method accurately recovers the underlying dynamic division probabilities, offering a quantitative tool for analyzing tissue development mechanisms in both normal and pathological conditions.

Technology Category

• Reasoning under Uncertainty: Relational Probabilistic Models
• Intelligent Robots: State Estimation
• Machine Learning: Probabilistic Circuits and Graphical Models

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Web Mining and Content Analysis: Models for Web evolution
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

Stem cells, through their ability to produce daughter stem cells and differentiate into specialized cells, are essential in the growth, maintenance, and repair of biological tissues. Understanding the dynamics of cell populations in the proliferation process not only uncovers proliferative properties of stem cells, but also offers insight into tissue development under both normal conditions and pathological disruption. In this paper, we develop a continuous time branching process model with time-dependent offspring distribution to characterize stem cell proliferation process. We derive analytical expressions for mean, variance, and autocovariance of the stem cell counts, and develop likelihood-based inference procedures to estimate model parameters. Particularly, we construct a forward algorithm likelihood to handle situations when some cell types cannot be directly observed. Simulation results demonstrate that our estimation method recovers the time-dependent division probabilities with good accuracy.

Problem

• stem cells
• cell proliferation
• time-varying branching process
• unobserved cell types
• population dynamics

Innovation

• time-varying branching process
• stem cell proliferation
• likelihood-based inference
• forward algorithm
• time-dependent offspring distribution