| Statuss(Neaktīvs) | Izdruka | Arhīvs(0) | Studiju plāns Vecais plāns | Kursu katalogs | Vēsture |
| Course title | Modelling of Biological Processes I |
| Course code | Mate5013 |
| Credit points (ECTS) | 3 |
| Total Hours in Course | 81 |
| Number of hours for lectures | 16 |
| Number of hours for laboratory classes | 16 |
| Independent study hours | 49 |
| Responsible Unit | Institute of Mathematics and Physics |
| Course developers | |
| Dr. math., asoc. prof. Svetlana Atslēga Dr. sc. ing., asoc. prof. Tatjana Rubina |
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| There is no prerequisite knowledge required for this course | |
| Course abstract | |
| The aim of this course is to accumulate the knowledge of the mathematical models creation, their applications, realization possibilities in the program Matlab, application and development of the programming skills in the decision of different problems using mathematical modelling. Within the course it is supposed to consider examples of different mathematical models, which describe the processes occurring in the nature and living systems. | |
| Learning outcomes and their assessment | |
| • knowledge of approximation and interpolation of functions, finite difference, spline interpolation, cubic splines, mathematical modeling, random variables, distributions, Monte Carlo method, biochemical systems modeling principles;
• skills to evaluate algebraic expressions, plot the graphs of functions using software Matlab, approximate and interpolate, define a random sample, construct a distribution, formulate a mathematical model of a given situation, solve the model, interpret the solution, use Monte Carlo method; • competence: of mathematical thinking, of handle symbols and formal mathematics language, of mathematical problem formulating and solving, of reasoning, of modeling, of aids and tools and of communication. |
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| Compulsory reading | |
| 1. Kronbergs E., Rivža P., Bože Dz. Augstākā matemātika II daļa. Rīga: Zvaigzne, 1988. 527 lpp.
2. Lorencs A. Statistisku datu ieguve un analīze. Rīga: Latvijas Universitāte, 2003. 60 lpp. 3. Ануфриев И., Смирнов А., Смирнова Е. MATLAB 7. Санкт-Петербург: БХВ-Петербург, 2005. 1104 с. |
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| Further reading | |
| 1. Christian P. Robert, G. Casella. Monte Carlo statistical methods. Springer, 2004. 645 p.
2. Said Elnashaie, Frank Uhlig. Numerical techniques for Chemical and biological engineers using MATLAB. A simple bifurcation approach. Springer, 2007. 590 p. 3. Самарский А.А., Гулин А.В. Численные методы: Учеб. пособие для вузов. Москва: Наука, 1989. 432 с. |
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