Evolution of the Age Structured Populations and Demography

We describe the simulation method of modelling the population evolution using Monte Carlo based on the Penna model. Individuals in the populations are represented by their diploid genomes. Genes expressed after the minimum reproduction age are under a weaker selection pressure and accumulate more mutations than those expressed before the minimum reproduction age. The generated gradient of defective genes determines the ageing of individuals and age-structured populations are very similar to the natural, sexually reproducing populations. The genetic structure of a population depends on the way how the random death affects the population. The improvement of the medical care and healthier life styles are responsible for the increasing of the life expectancy of humans during the last century. Introducing a noise into the relations between the genotype, phenotype, and environment, it is possible to simulate some other effects, like the role of immunological systems and a mother care. One of the most interesting results was the evolution of sex chromosomes. Placing the male sex determinants on one chromosome of a pair of sex chromosomes is enough to condemn it for shrinking if the population is panmictic (random-mating is assumed). If males are indispensable for taking care of their offspring and have to be faithful to their females, the male sex chromosome does not shrink.

💡 Research Summary

The paper presents a Monte‑Carlo simulation framework built on the Penna ageing model to study how age‑structured populations evolve under genetic, environmental, and social pressures. Each individual is represented by a diploid genome consisting of two binary strings; each bit corresponds to a gene that becomes active at a specific chronological age. A minimum reproductive age (R) separates two regimes of selection: genes expressed before R are under strong purifying selection and therefore accumulate few deleterious mutations, whereas genes expressed after R experience weaker selection and tend to collect more defects. When the number of active defective genes reaches a predefined threshold (T), the individual dies, producing an age‑dependent mortality curve that closely mimics that of real human populations.

The authors manipulate four major parameters to explore demographic outcomes. First, random death probabilities are assigned as a function of age to model stochastic environmental hazards. Second, a Gaussian “noise” term couples genotype, phenotype, and environment, allowing individuals with identical genomes to display variable fitness. Third, the model incorporates improvements in medical care and lifestyle by gradually reducing both age‑specific death rates and the variance of the noise term, thereby reproducing the observed increase in life expectancy over the past century. Fourth, a “maternal care” buffer is introduced: mothers protect newborns for a limited period, lowering the offspring’s susceptibility to environmental noise and consequently enhancing early‑life survival and genetic diversity.

A separate set of experiments investigates sex‑chromosome evolution. The X and Y chromosomes are each modeled as independent bit strings, with the male‑determining factor placed on the Y. Two mating regimes are compared. In a panmictic population where males are not required for offspring care, the Y chromosome rapidly loses functional bits and shrinks, reflecting its reduced selective relevance. Conversely, when male parental investment and fidelity are enforced, the Y chromosome retains functional genes and does not undergo shrinkage, illustrating how social structure can shape chromosomal architecture.

Key findings include: (1) age‑dependent selection creates a gradient of defective genes that drives ageing and produces realistic age‑structured mortality; (2) the intensity of random death and environmental noise critically determines average lifespan and population growth; (3) advances in healthcare and healthier lifestyles effectively slow the accumulation of deleterious mutations, extending life expectancy; (4) maternal protection buffers early mortality, stabilizing population dynamics; and (5) sex‑chromosome size and composition are contingent on mating systems and male parental roles.

The study demonstrates that a relatively simple, biologically grounded computational model can integrate genetics, demography, and social behavior to generate quantitative predictions about population ageing and evolution. The authors suggest future extensions such as disease‑specific gene modules, climate‑induced environmental stressors, and policy‑driven changes in healthcare access, which would further enhance the model’s applicability to public‑health planning and evolutionary biology.