SMTK20001U Advanced Physiological Modelling

Volume 2025/2026
Education

MSc Programme of Biomedical Engineering - Restricted elective in the Technological Specialization

MSc Quantitative Biology and Disease Modelling - Restricted elective in the Technological Specialization

Open to other students with adequate prerequisites

Content

The course aims to provide overview of current research topics in mathematical modeling. The course will give students the opportunity to develop mathematical and computer modeling skills.

The course entails two parts: lectures followed by an associated problem sessions and research projects in groups. In the lectures, the students are familiarized with advanced tools of physiological modeling including statistical, machine learning, and mechanism-based modelling. Research projects lasts from the first day until the exam and is carried out in groups under the supervision of a researcher. The research projects are the basis for a written report that the exam is based on.

The course occupies the entire Wednesday with lectures from 8-12 and group work from 12-17.

To complete the course, moderate experience with programming and general knowledge of human physiology are prerequisites.

Learning Outcome

After participating in the course, the student must demonstrate the following abilities at a sufficient level:

Knowledge

  • Understanding of mathematical/physics concepts (dynamical systems, oscillation theory, network theory, statistics, survival analysis, machine learning);

  • Understanding of computational algorithms behind Matlab/Python/R scripts related to biosimulations;

  • Understanding of topology and dynamics of biological networks and their relevance to normal and pathological conditions;

  • Understanding of the assumptions and simplifications are needed to build a model;

  • Understanding of underlying signaling mechanisms and corresponding control parameters in a model related to a certain disease.

Skills

  • To review scientific literature on physiological modeling and related data analysis, critically assess the use of methods and obtained results;

  • To perform theoretical and computational analysis of dynamical patterns of physiological models and reveal parameters responsible for pathological conditions;

  • To optimize/develop computation algorithms to perform biosimulations;

  • To convey a physiological problem solving into a report and a Power Point presentation.

Competence

  • To formulate data-driven and mechanism-based models and choose appropriate computational methods to study them;

  • To interpret statistical and dynamical features of biomedical systems in term of biology and mathematics;

  • To integrate fundamental knowledge from physics, mathematics,and biology to provide better understanding of regulatory mechanisms at different levels of physiological organization.

Lecture notes and scientific papers

Lectures, class/home assignments, project work
Please, note that this is 10 ECTS course (2.5 ECTS is for the course certificate + 7.5 ECTS for the examination).
  • Category
  • Hours
  • Lectures
  • 24
  • Preparation
  • 170
  • Exercises
  • 24
  • Project work
  • 56
  • Exam
  • 0,5
  • Total
  • 274,5
Oral
Peer feedback (Students give each other feedback)
Credit
2,5 ECTS
Type of assessment
Requirement to attend classes
Type of assessment details
Satisfactory completion of all written and oral assignments through the course and approval of the final written report.
Aid
All aids allowed except Generative AI
Marking scale
passed/not passed
Censorship form
No external censorship
Criteria for exam assesment

To obtain the course certificate the student must be able 

Knowledge

  • To understand underlying mathematical/physics concepts; 
  • To understand computational algorithms used for modeling;
  • To understand biological background of the models.

Skills

  • To discuss modeling approaches depending on the purpose of study;
  • To reproduce, modify and simulate models from scientific literature;
  • To discuss reproducibility and comparability of relevant computational models;
  • To apply mathematical/physical concept to a specific biomedical problem;
  • To formulate a model and discuss its possible simplification/extension and dynamics;
  • To evelop algorithms to perform simulations;
  • to improve scientific writings in English language.

 

Credit
7,5 ECTS
Type of assessment
Written assignment
Oral examination, 30 min.
Type of assessment details
Oral exam includes a presentation of the project on the basis of the written report and discussion of biological, mathematical, and computational aspects of the project.
Exam registration requirements

Achieved course attestation by handing-in of project assignment and assessment/criticism of the received assignment.

Aid
No aids allowed
Marking scale
7-point grading scale
Censorship form
No external censorship
One internal examiner
Exam period

https://healthsciences.ku.dk/education/for-students/exam-schedule/biomedical-engineering/

Re-exam

https://healthsciences.ku.dk/education/for-students/exam-schedule/biomedical-engineering/

Criteria for exam assesment

In order to obtain the grade 12, the student must demonstrate the following abilities at a sufficient level:

Knowledge

  • Understanding of physiology and regulatory mechanisms related to normal and pathological conditions;
  • Understanding of mathematical/​physics concepts underlying modeling approaches;
  • Understanding of computational algorithms behind the scripts related to biosimulations;
  • Understanding of assumptions and simplifications needed to build a reliable model;
  • Understanding of dynamical/statistical features of the model and their relevance to physiological states.

Skills

  • To review scientific literature on physiological modeling and related data analysis; critically assess the use of methods and obtained results;
  • To discuss reproducibility and comparability of relevant computational models and approaches;
  • To perform theoretical and computational analysis of dynamical patterns of physiological models and reveal parameters responsible for pathological conditions;
  • To optimize/develop computation algorithms to perform biosimulations;
  • To convey results into a scientifically written report and a Power Point presentation.