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Computational Biomedical Research



In order to get a grip on the enormous complexity of biological systems, mathematical and computational methods are becoming increasingly popular. This is relevant for the understanding of biological systems and to predict how drugs can influence these systems. In this course, students will get acquainted with such mathematical and computational methods and how they can be applied to data from various applications (including networks of molecular interactions within cells; behavior of cell populations; migration of cells; interactions between living organisms and drugs). Moreover, there is a bio- and cheminformatic component. Herein students learn to computationally analyze protein sequences as well as ‘small molecules’, and ultimately model interactions between them.

Course Objectives

After the course, the student will be able to:

  • explain which type of research questions can be considered using cheminformatics, bioinformatics, and structure based drug discovery.

  • explain methods that are typically used in cheminformatics, bioinformatics and structure based drug discovery (e.g., descriptors, machine learning approaches, crystal structure and homology models).

  • recall a number of public databases that are relevant for computational drug discovery.

  • explain limitations in cheminformatics, bioinformatics or structure based drug discovery.

  • interpret results from studies in which small molecules are docked to crystal structures and in which quantitative structure-activity relationship (QSAR) models are employed.

  • create a simple quantitative structure-activity relationship (QSAR) model in a cheminformatics workflow tool.

  • perform simple docking of a small molecule to a crystal structure of a protein.

  • formulate and interpret dynamical models applied to biological networks and cell populations.

  • analyse dynamical models with respect to their short- and long-term behaviour and how these depend on system parameters.

  • explain how dynamical models can be exploited to find the best targets for therapy and to interpret results from such analyses.

  • explain different approaches to simulate spatial effects in biomedical applications.

  • explain the relationships between drug delivery, pharmacokinetics (PK) and pharmacodynamics (PD).

  • develop structural population models to describe the relationships between drug delivery, PK and PD.

  • explain how covariates can be used to (partially) explain inter-individual variability in population PK and PD models.

  • identify and quantify inter-individual variability and covariate relationships.

  • explain how model-based simulations can be used to optimize and individualize drug dosing regimen.

Reading list

Literature will be provided during the course.


Dr. J.B. Beltman

Mode of instruction

The course will use a combination of lectures (typically 2 hours per day, usually in the morning) and pen & paper or computer exercises (typically 4 hours per day, usually in the afternoon).

Assessment method

For the hands-on part of the course covering bio- and cheminformatics, the students give an oral presentation, for which they are graded (making up 10% of the final grade). Moreover, a written exam will make up 90% of the final grade. Presence and active participation during the working groups (mainly computer assignments) and discussions is mandatory.

Admission requirements & Registration

This course is mandatory for and restricted to students who do the Minor ‘Computational approach to Disease Signaling and Drug Targets’ (CADSDT; the entire Minor or only Part 1). The same admission criteria apply to this course as for the Minor CADSDT (see Appendix 3 of the Education and Exam regulation BSc Programmes (OER)). Registration for the lectures and exam via uSis is mandatory.