Markov models of aging: theory and practice

Area of Science:

• Gerontology
• Biostatistics
• Mathematical Biology

Background:

• Aging is a complex process with inherent variability between individuals and over a single lifespan.
• Understanding the sources and nature of this variability (stochasticity) is crucial for aging research.

Purpose of the Study:

• To review and structure mathematical and statistical models of aging developed over the last 50 years.
• To explore the partitioning of randomness in aging processes.
• To propose integration of survival analysis with mathematical and biological aging models.

Main Methods:

• Literature review and synthesis of mathematical and statistical models related to aging.
• Analysis of how models address the sources and types of randomness in aging.
• Identification of connections between different research areas in aging modeling.

Main Results:

• Mathematical models primarily focus on partitioning the randomness in aging.
• Aging variability can be categorized by individual differences, lifecourse changes, cumulative vs. short-term randomness.
• Survival analysis offers potential for addressing these questions when linked to biological models.

Conclusions:

• Explicitly linking statistical survival analysis with mathematical and biological aging models can enhance understanding of aging stochasticity.
• Future research should explore information-theoretic measures for evaluating stochastic aging models.
• This integration can improve experimental analysis and theoretical discussions on aging.