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Modeling of complex biological systems

General data

Course ID: 1000-719bMSB
Erasmus code / ISCED: (unknown) / (unknown)
Course title: Modeling of complex biological systems
Name in Polish: Modelowanie złożonych systemów biologicznych
Organizational unit: Faculty of Mathematics, Informatics, and Mechanics
Course groups: (in Polish) Przedmioty 4EU+ (z oferty jednostek dydaktycznych)
Obligatory courses for 2nd stage Bioinformatics
ECTS credit allocation (and other scores): 6.00 Basic information on ECTS credits allocation principles:
  • the annual hourly workload of the student’s work required to achieve the expected learning outcomes for a given stage is 1500-1800h, corresponding to 60 ECTS;
  • the student’s weekly hourly workload is 45 h;
  • 1 ECTS point corresponds to 25-30 hours of student work needed to achieve the assumed learning outcomes;
  • weekly student workload necessary to achieve the assumed learning outcomes allows to obtain 1.5 ECTS;
  • work required to pass the course, which has been assigned 3 ECTS, constitutes 10% of the semester student load.
Language: English
Type of course:

obligatory courses

Short description:

Models and inferences in computational molecular biology, focusing on how statistics and machine learning methods are used to understand complex systems.

Full description:

We examine modern challenges in modeling and understanding complex biological systems through data. High-throughput molecular measurements have necessitated development and application of statistics and machine learning, giving rise to computational biology. Microarray and sequencing technologies enable us to quantify how complex systems are responding to and influenced by experimental and external conditions. It may lead to better understanding fundamental organizational principles and functionalities of molecules and cells. Lately, there have been interesting developments in single cell analyses, spatial genomics, imaging and others that involve higher resolutions, scales, and complexities.

In this course, we study exploratory data analysis, statistical learning, and neural networks that are specifically designed for such biological studies. Good understanding of statistics and programming are prerequisites. Students will program in R and Python, read primary literature weekly, and complete data analysis projects.


An Introduction to Statistical Learning with Applications in R

by Gareth James, Daniela Witten, Trevor Hastie and Rob Tibshirani

The Elements of Statistical Learning: Data Mining, Inference, and Prediction.

by Trevor Hastie, Robert Tibshirani, Jerome Friedman

Deep Learning with Python

by Francois Chollet

Students will be asked to read selected original research and review papers.

Learning outcomes:

At the end of the course, the students will:

- know major developments in computational biology and computational models for select biological systems,

- be able to analyze selected biological data underlying complex systems,

- be able to read and write scientific reports.

Assessment methods and assessment criteria:

Participation, Homeworks, Project Report, Presentation.

Classes in period "Summer semester 2023/24" (future)

Time span: 2024-02-19 - 2024-06-16

Selected timetable range:
Navigate to timetable
Type of class:
Classes, 30 hours more information
Lecture, 30 hours more information
Coordinators: Neo Christopher Chung
Group instructors: Neo Christopher Chung
Students list: (inaccessible to you)
Examination: Examination
Course descriptions are protected by copyright.
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