The Use of Modeling a Population by Scientists: Monte Carlo Simulations of Allele Frequencies

Demographic modeling is used to demonstrate various interpretations through various disciplines. For example, scientists can present mathematical models to represent demographics or genetic drift in specific environments over the years. Demographic modeling has been used to demonstrate mathematically, psychologically, and scientifically that an understanding (in use) number of (in use) can be manipulated by changing its generation and proportion It was.

Introduction: The Hardy-Weinberg model was named after two scientists who were born at the beginning of the century to describe and predict genotype and allele frequencies in an evolving population. This model has five basic assumptions: 1) large population (ie without genetic drift), 2) gene flow between populations, migration or transfer from gametes, 3) mutation is negligible , 4) random mating of individuals. Natural selection does not apply to population. Given these assumptions, the population's genotype and allele frequency remain unchanged for consecutive generations and the population is said to be in Hardy-Weinberg equilibrium. The Hardy Weinberg model can also be applied to genotype frequencies of individual genes.

Pepper moth simulates many factors that can cause changes in the genetic makeup of the population. As population genotype and allele frequency change over time, the population is said to be developing. Hardy-Weinberg equilibrium theory is used in population genetics to determine whether these factors affect the population, and then whether it is developing. - Have you ever wondered how animals know so much? This is very easy. It all depends on how they behave and what they know. When animals are born, it has instincts. These instincts help living beings survive and let them act in some way. This is called congenital behavior. However, a few things were taught. Unlike congenital behavior, it comes from genes and needs to learn other behaviors. Learning behavior

The Markov chain Monte Carlo method has two parts. Monte Carlo is a common method of using repeated sample at random to obtain numerical answers. Monte Carlo can be thought of as doing a lot of experiments, changing the variables in the model, and observing the response each time. By choosing random values ​​you can search most of the parameter space which is the range of possible values ​​of the variable. The parameter space of the problem using normal a priori variables (details are described below) are as follows.