PřF:M81B0 Mathematical models in biology - Course Information
M81B0 Mathematical models in biology
Faculty of Sciencespring 2018
- Extent and Intensity
- 2/0/0. 2 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: k (colloquium).
- Teacher(s)
- doc. RNDr. Petr Lánský, DrSc. (lecturer)
- Guaranteed by
- doc. RNDr. Petr Lánský, DrSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science - Timetable
- Thu 12:00–13:50 M4,01024
- Prerequisites
- Mathematical analysis I. and II., Fundamentals of mathematics, Probability and Statistics
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- Mathematical Biology (programme PřF, N-BI)
- Mathematical Biology (programme PřF, N-EXB)
- Course objectives
- The course provides basic information on the applications of mathematical models in different fields related to biology, like neuroscience, medicine, biophysics and so on. It helps to understand the contemporary trends in research, which could not be performed without sophisticated numerical procedures and such branches of science as information theory, neural networks or biocybernetics. Each lecture is supplemented by a review of mathematical procedures in use.
- Learning outcomes
- After passing the course, the student will be able:
to define and interpret the basic notions used in the theory of formal (mathematical) models and to explain their mutual context;
to formulate relevant mathematical theorems and statements and to explain methods of their proofs;
to use effective techniques utilized in the theory of formal (mathematical) models;
to apply acquired pieces of knowledge for the solution of specific problems including problems of applicative character. - Syllabus
- The list is modified with respect to actual research 1) Biochemical reactions. 2) Integrate-and-fire neural models 3) Action potential 4) Applications of point process theory 5) Information coding 6) Sensory systems. 7) Logical neuron 8) Pharmacokinetics 9) Pharmacodynamics. 10) Stochastic resonance 11) Dissolution 12) Simulation of stochastic systems.
- Literature
- Teaching methods
- Lectures and Discussion
- Assessment methods
- Active discussion during lectures, cooperation during classes. To conclude the term, one has to prove understanding the topics, to be able to create new concepts and this has to be shown in the homeworks.
- Language of instruction
- Czech
- Further Comments
- Study Materials
The course is taught annually.
- Enrolment Statistics (spring 2018, recent)
- Permalink: https://is.muni.cz/course/sci/spring2018/M81B0