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Course info
KBF / BISE
:
Course description
Department/Unit / Abbreviation
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KBF
/
BISE
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Academic Year
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2024/2025
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Academic Year
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2024/2025
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Title
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Biological Experiments In Silico
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Form of course completion
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Exam
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Form of course completion
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Exam
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Accredited / Credits
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Yes,
2
Cred.
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Type of completion
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Oral
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Type of completion
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Oral
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Time requirements
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Lecture
2
[Hours/Week]
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Course credit prior to examination
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No
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Course credit prior to examination
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No
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Automatic acceptance of credit before examination
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No
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Included in study average
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YES
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Language of instruction
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English
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Occ/max
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Automatic acceptance of credit before examination
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No
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Summer semester
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0 / -
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0 / -
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0 / -
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Included in study average
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YES
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Winter semester
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0 / -
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0 / -
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0 / -
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Repeated registration
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NO
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Repeated registration
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NO
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Timetable
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Yes
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Semester taught
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Summer semester
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Semester taught
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Summer semester
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Minimum (B + C) students
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not determined
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Optional course |
Yes
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Optional course
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Yes
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Language of instruction
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English
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Internship duration
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0
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No. of hours of on-premise lessons |
0
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Evaluation scale |
A|B|C|D|E|F |
Periodicity |
každý rok
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Periodicita upřesnění |
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Fundamental theoretical course |
No
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Fundamental course |
No
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Fundamental theoretical course |
No
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Evaluation scale |
A|B|C|D|E|F |
Substituted course
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None
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Preclusive courses
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KBF/BIS
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Prerequisite courses
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N/A
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Informally recommended courses
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N/A
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Courses depending on this Course
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N/A
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Histogram of students' grades over the years:
Graphic PNG
,
XLS
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Course objectives:
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Obtaining basic general knowledge about in silico biology, i.e., about studying of biological organisms and processes by means of computer modelling and simulations.
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Requirements on student
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Passing the oral examination.
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Content
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- Introduction (mathematics in biology, purpose of modelling, motivating example - AIDS/HIV research) - Basic mathematics (linear, exponential and other functions, differential equations) - Descriptive models and basic data analysis (linear and exponential regressions, ?2 method) - Estimation of model parameters (different methods) - Model validation and verification (Kullback-Liebler divergence, Occam's razor principle, Akike information criterion) - Chemical reaction kinetics and its modelling (types of reactions, reaction order, law of mass action, master equations, Markov chain) - Michaelis-Menten and Hill enzymatic kinetics - Transition state rate theory and rate of electron tunneling - Modelling of oscillating chemical/biological systems (phase space, criteria for oscillations, Lotka-Voltera model, Brusselator, Belousov-Zhabotinsky reaction, Oregonator, glycolysis in yeast, photosynthetic oscillations) - Metabolic control analysis (assumptions, control and response coefficients, elasticity, summation and connectivity theorems, biological examples) - Examples of complex models - modeling of photosynthetic processes (different models)
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Activities
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Fields of study
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Guarantors and lecturers
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Literature
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Recommended:
Otto, S. P., Day, T. A Biologist´s Guide to Mathematical Modeling in Ecology and Evolution. Princeton University Press, 2007.
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Recommended:
Roussel, M. R. A Life Scientist´s Guide to Physical Chemistry. Cambridge University Press, 2012.
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Recommended:
Ellner, S. P., Guckenheimer, J. Dynamic Models in Biology. Princeton University Press, 2006.
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Recommended:
Bisswanger, H. Enzyme Kinetics, 2nd Edition. Wiley-Vch, 2008.
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Recommended:
Britton, N. F. Essential Mathematical Biology. Springer, 2003.
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Recommended:
Rubin, A., Riznichenko, G. Mathematical Biophysics. Springer, 2014.
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Recommended:
Edelstein-Keshet, L. Mathematical Models in Biology. Siam, 2005.
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Recommended:
Haefner, J. W. Modeling Biological Systems, Principles and applications, 2nd Edition. Springer, 2005.
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Recommended:
Laisk A., Nedbal L., Govindjee, eds. Photosynthesis In Silico: Understanding Complexity from Molecules to Ecosystems. Springer, 2009.
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On-line library catalogues
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Time requirements
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All forms of study
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Activities
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Time requirements for activity [h]
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Attendace
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30
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Preparation for the Exam
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24
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Total
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54
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Prerequisites - other information about course preconditions |
- |
Competences acquired |
The student will be able to define, write and solve mathematical models of basic biological processes. |
Teaching methods |
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Assessment methods |
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