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.
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).
explain limitations (either caused by lack of data or by lack of confidence) 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.
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) and the impact of different levels of variability on drug exposure and response.
develop structural population models to describe and quantify the relationships between drug delivery, PK and PD, and the levels of variability in the population.
explain how covariates can be used to (partially) explain inter-individual variability in population PK and PD models.
explain how model-based simulations can be used to optimize and individualize drug dosing regimen.
Literature will be provided during the course.
Dr. J.B. Beltman
Mode of instruction
The course will use a combination of lectures, team-based learning activities, as well as pen & paper and computer exercises.
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. In the written exam, a total of 60 points can be obtained with equal distribution for the three parts, i.e., 20 points for each part. The grade for the exam is subsequently determined by dividing the total number of obtained points by 6.
Presence and active participation for the hands-on work during the course part on ‘dynamical modeling of cell behaviour in space and time’ is mandatory; it will be monitored and leads to a maximum of 2 points out of the 20 possible points on this part of the written exam.
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.