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Mathematical foundations of population genetics

ДВС.3.03

Вибіркова дисципліна для ОП

First

2023/2024

7 Semester

3

LO 3. Formalize problem sets up in the language of a particular subject area; define their mathematical formulation and choose a rational problem-solving approach; solve the obtained problems with analytical and numerical methods, evaluate the accuracy and reliability of the results obtained. PLO 22.3. Understand the main areas of mathematical logic, theory of algorithms and computational theory, programming theory, probability theory and mathematical statistics; PLO 23.3. Be able to use professional knowledge, skills and abilities in the field of fundamental sections of mathematics and computer science for research of real processes of different nature;

Full-time form

To successfully learn the discipline "Mathematical foundations of population genetics" the student should satisfy the following requirements. They know fundamental bases of mathematical methods of construction, verification, analysis of qualitative characteristics of deterministic and stochastic mathematical models; classical methods of mathematical analysis, probability theory, random processes. They can conduct research on the qualitative characteristics of the constructed mathematical models; apply classical methods for the study of applied problems in deterministic and stochastic models. They should be able to apply classical methods of mathematical analysis and probability theory; search and analyze information in open sources.

The purpose of the discipline is to get acquainted with the main applications of probabilistic ideas in systems studied in genetics, as well as to master the technical apparatus inherent in this field of knowledge. "Mathematical foundations of population genetics" is the basis for studying the discipline "Environmental and economic processes and their modeling".

1. S. Karlin Osnovy teorii sluchaynikh protsessov. Moskva: “Mir”, 1971. 2. P.Moran Statisticheskiye protsessy evolyutsionnoy teorii. Moskva: “Nauka”, 1973. 3. R. Fisher Geneticheskaya teoriya yestestvennogo otbora. Moskva – Izhevsk, 2011. 4. Durrett, Richard. Probability models for DNA sequence evolution. Springer, 2008. 5. Berestycki, Nathanaël. "Recent progress in coalescent theory." Ensaios Matematicos 16 (2009): 1-193. 6. B.V. Dovgay. Matematichní osnovi genetiki populyatsíy: Yelektronniy navchal'niy posíbnik.-2014. - 55 s. http://do.unicyb.kiev.ua/index.php/uk/2011-01-03-16-37-54?task=download&cid[0]=43

Lectures, practice work, independent work, test works.

Intermediate assessment: Maximum number of points that can be obtained by a student: 100 points: 1. Test work no. 1: 25/15 points. 2. Test work no. 2: 25/15 points. 3. Work in the audience:: 50/30 points. Final assessment (in the form of a test): Not provided

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