E7441 Scientific computing in biology and biomedicine

Faculty of Science
Spring 2025
Extent and Intensity
1/1/0. 2 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
In-person direct teaching
Teacher(s)
doc. Ing. Vlad Popovici, PhD (lecturer)
Guaranteed by
doc. Ing. Vlad Popovici, PhD
RECETOX – Faculty of Science
Contact Person: doc. Ing. Vlad Popovici, PhD
Supplier department: RECETOX – Faculty of Science
Timetable
Mon 17. 2. to Sat 24. 5. Mon 12:00–13:50 D29/347-RCX2
Prerequisites
Basic linear algebra, notions of optimization theory, numerical methods, Python and R programming
Course Enrolment Limitations
The course is offered to students of any study field.
Course objectives
At the end of the course, students should be able to:
-Understand the basics of numerical methods for linear algebra;
-Know and have experience in applying methods in computational statistics;
-Gain knowledge and experience of computer-intensive methods for data analysis;
-Know how to use parallel computation tools;
-Apply the theory in practice for solving problems in biological data analysis, using Python (and R)
Learning outcomes
After completing the course, a student will be able to:
-use the appropriate methods for solving various types of systems of linear equations
-identify the major sources of numerical instability and take steps for correcting
-solve numerically basic optimization problems;
-use Monte-Carlo methods for parameter estimation;
-exploit the parallelism for better use of computations resources;
-identify the suitable numerical routines for solving the given problem
Syllabus
  • Introduction: data representation; approximations and errors;
  • Systems of linear equations: triangular systems; Gauss elimination; norms and conditioning.
  • Linear least squares: normal equations; orthogonalizations
  • Eigendecompositions and singular values: eigenvalues, eigenvectors; singular value decomposition
  • Optimization: general topics; one-dimensional; multidimensional
  • Monte Carlo methods: random numbers; simulation, sampling and non-parametric statistics
  • Bootstrapping and resampling: bootstrap as an analytical tool; confidence intervals from bootstrapping
  • Parallel computing: levels of parallelism; platforms for computational biology; applications in computational biology
  • Support material:
  • KONG Q., SIAUW T., BAYEN A. (2020). Python programming and numerical methods. Academic Press. ISBN: 9780128195499
  • HEATH M.T. (2002). Scientific Computing. An introductory survey. McGraw-Hill, 2nd edition. ISBN: 0-07-239910-4
  • GENTLE J.E. (2005). Elements of Computational Statistics. Springer. ISBN:978-0387954899
Literature
    recommended literature
  • HEATH, Michael T. Scientific Computing. An introductory survey. 2nd. The McGraw-Hill Companies, Inc., 2002. ISBN 0-07-239910-4. info
Teaching methods
lectures; class discussion; hands-on computer exercises; homework
Assessment methods
continuous assessment throughout the semester; written and practical exam.
Language of instruction
English
Further Comments
Study Materials
The course is taught annually.
The course is also listed under the following terms Spring 2023, Spring 2024, Spring 2026.
  • Enrolment Statistics (recent)
  • Permalink: https://is.muni.cz/course/sci/spring2025/E7441