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Learning Opportunity: Biomathematics


Course Information

Objective(s) of a study program:
To prepare specialists of biomathematics (masters in mathematical modelling) who have appropriate knowledge and skills for creating mathematical models of complicated biosystems, for analyzing data coming form this kind of systems, for using various problem-oriented informational technologies. The biosystems modelling masters will be able to use their knowledge and skills for formulating and solving real-life problems in biology, biotechnology, pharmacy, medicine and ecology.
Learning outcomes:
Knowledge and its applications:
A1 Knowing the principles of mathematical modeling and possibilities of its application in biology and medicine;
A2 Knowing and understanding the specifics of modelling and analysis of molecular systems;
A3 Knowing and understanding statistical data analysis methods for biology and medicine, and survival analysis methods;
A4 Knowing and understanding human physiological systems and biophysics;

Abilities of doing research:
B1 Being able to analyze various real world processes, to create, improve and assess mathematical models for solving problems in mathematics and biology;
B2 Being able to choose and apply appropriate mathematical models for given biosystems;
B3 Being able to find and synthesize information during the whole problem solving process;
B4 Being able to analyze, interpret, compare and assess the results of modeling, select the optimal solutions, formulate and justify the conclusions, make forecasts;

Special abilities:
C1 Being able to construct models of complicated biological systems, to create software for implementing and assessing those models;
C2 Being able to use scientific literature for assessing the modeling results, for solving optimality and other problems;
C3 Being able to learn new mathematical methods, to create and implement new algorithms;
C4 Being able to justify their decisions in theory and practice;

Social abilities:
D1 Taking responsibility for the quality of one‘s own work and for the work of employees under the management of the graduate. Acting according to the professional ethics;
D2 Being able to work in an interdisciplinary team, to generate new ideas;
D3 Being able to communicate the generalized information about the research to the specialists of other domains;

Personal abilities:
E1 Understanding the effects of decisions taken for the society and the environment;
E2 Understanding the importance of life-long learning and being ready for it.
E3 Effectively working and comunicating at the international and national level.

Activities of teaching and learning:
Lectures, laboratory works, practical works and individual works, individual and group consultations, analysis of literature sources, seminars, discussions, debates, search, reflection, problem solving, case study, error analysis, theoretical modeling, written works, project preparations, presentations, demonstration, individual as well as team work.
Methods of assessment of learning achievements:
The criterion proportional cumulative grading ten-point scale to assess knowledge, abilities and skills is applied. The learning outcomes of the program and its modules are evaluated during intermediate semester reporting (studies, midterm works, projects and their reports, laboratory work reports and oral defending, etc.). The final grade consists of intermediate evaluations and final exam grades.
Study subjects (modules), practical training:
60 credits are dedicated for the study direction (mathematics), 12 credits are for biostatistics and biological data analysis, 18 credits are for deeper training in mathematics or biosciences. Master project is 30 credits. Total: 120 credits.

Optional courses:
During the first semester students will deepen their knowledge in biology (at the levels of cells, tisues and organisms) and biophysics or, alternatively, in mathematics.
Students will have a possibility to choose which mathematical aspects of biosystems analysis they want to study additionally (Alternatives 1 and 2).
Distinctive features of a study program:

Reference Data

Education Level:
Thematic area:
Teaching Language:
Study Type:
Access requirements: 

Minimum education - Higher education university type



Provider Information

Provider Name: 
Kaunas University of Technology
Provider Type: 
Provider Contact Info: 

Provider phone number: +370-37-300000 Provider email: ktu@ktu.lt Provider URL: http://ktu.edu/

Qualifications Awarded

Reference Data

Qualification Awarded:
Master in mathematical modeling
Awarding body:

Kaunas University of Technology

Awarding body contact info:

Provider phone number: +370-37-300000 Provider email: ktu@ktu.lt Provider URL: http://ktu.edu/

Course Locations

Reference Data

Course address:

K. Donelaičio g. 73, Kaunas