Stochastic processes in biology and social sciences 1000-135PSB
Lectures on theoretical foundations of stochastic analysis (Markov chains, Poisson process, birth and death processes, Master and Fokker-Planck equations will be integrated with concrete biological models on the micro level (gene expression and regulation, ion channels) and on the macro level (evolutionary game theory).
Course coordinators
Type of course
Learning outcomes
Knowledge and Competence
1. Student knows basic definitions, theorems, and properties of Markov chains, branching processes, Poisson process, and birth and death processes.
2. Student knows how to compute moments using generating functions.
3. Student knows basic mathematical models of gene expression and evolutionary game theory.
4. Student knows how to construct mathematical models based on biological and social texts.
Social Competence
Student is able to talk with biologists and economists about mathematics and with mathematicians about biology and social science.
Assessment criteria
Tests: 30%
Project: 30%
Written Exam: 40%
Bibliography
1. G. R. Grimmett i D. R. Stirzaker, Probability and Random Processes, Oxford University Press, 1982
2. Ch. Mazza i M. Benaim, Stochastic Dynamics for Systems Biology, Chapman and Hall/CRC Mathematical and Computational Biology, 2014.