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Lecturer(s)
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Císař Petr, Ing. Ph.D.
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Urban Jan, Ing. Ph.D.
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Štys Dalibor, prof. RNDr. CSc.
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Jablonský Jiří, Mgr. Ph.D.
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Štysová Rychtáriková Renata, Ing. Bc. Ph.D.
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Course content
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The course is divided into four parts. 1) The first unit deals with the methodology of experimental data management, which covers the whole process of data life cycle. Students will learn the methodology of data management and on a specific example prepare a description of the experiment and test the data and metadata management system. 2) The second part of the course focuses on the theory of measurement and statistical processing of discrete data. The methodology of sampling of discrete data, its storage and representation in a computer and their subsequent processing using statistical methods and visualization tools will be explained. 3) The third part presents possibilities of using monitoring systems (camera systems) and microscopy for biological tasks. Basic principles of systems and microscopy will be explained and students will make their own measurements using light microscopes, very high resolution microscope, 3D monitoring systems, hyperspectral cameras and other camera systems. Basic methods of image signal analysis will be presented. 4) In the fourth part, students will learn about modeling, which can both simulate the behavior of the studied system, but also predict its behavior. Students will be explained methods of kinetic and stoichiometric modeling in biology. The construction and use of models will be explained on specific examples.
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Learning activities and teaching methods
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Monologic (reading, lecture, briefing), Laboratory, Practical training
- Semestral paper
- 40 hours per semester
- Class attendance
- 56 hours per semester
- Preparation for exam
- 20 hours per semester
- Preparation for credit
- 20 hours per semester
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Learning outcomes
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The course is designed as a multidisciplinary survey of acquisition, processing, analysis and management of biological data. Students will be acquainted with the possibilities of using monitoring systems for measurement, principles of measurement and storage of digital signal and methods of measurement data processing and simulation tools. Students who complete the course will be able to optimize the design of the experiment from the perspective of further automated data processing, describe the quality of the experiment in terms of repeatability and effectively collaborate from colleagues in other fields (signal analysis, modeling, information mining). All theoretical knowledge will be tested during the course on real examples in the equipment laboratory.
Students who complete the course will be able to optimize the design of the experiment from the perspective of further automated data processing, describe the quality of the experiment in terms of repeatability and effectively collaborate from colleagues in other fields (signal analysis, modeling, information mining).
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Prerequisites
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The prerequisite of the course is basic knowledge of probability and statistics. The methods explained in the course build on hypothesis testing (ANOVA, T-Test, F-Test), linear regressions and normal distribution. All concepts are re-discussed within the subject.
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Assessment methods and criteria
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Combined exam
Basic knowledge of statistics and probabilities. The subject assumes orientation in hypothesis testing, mean value and scattering, and awareness of normal distribution.
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Recommended literature
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