Admission requirements
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)).
Description
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; interactions between living organisms and drugs). Note that this also includes execution of (basic) mathematical calculations such as solving equations, hence a limited amount of calculus skills is required to pass for the course. 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).
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 or in which quantitative structure-activity relationship (QSAR) models are employed.
understand how machine learning can be used to generate new chemical structures.
formulate and interpret various types of dynamical models applied to biological networks and cell populations.
mathematically and computationally 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 in drug discovery and to interpret results from analyses with this aim.
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.
Timetable
You will find the timetables for all courses and degree programmes of Leiden University in the tool MyTimetable (login). Any teaching activities that you have successfully registered for in MyStudyMap will automatically be displayed in MyTimeTable. Any timetables that you add manually, will be saved and automatically displayed the next time you sign in.
MyTimetable allows you to integrate your timetable with your calendar apps such as Outlook, Google Calendar, Apple Calendar and other calendar apps on your smartphone. Any timetable changes will be automatically synced with your calendar. If you wish, you can also receive an email notification of the change. You can turn notifications on in ‘Settings’ (after login).
For more information on MyTimeTable, watch the video or go the the 'help-page' in MyTimetable.
PLEASE NOTE
Always check the detailed schedule on the Brightspace module of each Course 2-3 weeks before the start of the Course for group-specific meetings, (intermediate) deadlines, etc..
Mode of instruction
The course will use a combination of lectures, team-based learning activities, as well as pen & paper and computer exercises.
Assessment method
For the hands-on part of the course covering 'computational chemical biology', the students give an oral presentation, for which they are graded (making up 20% of the final grade). Moreover, a written exam will make up 80% of the final grade. In the written exam, a total of 50 points can be obtained for the three parts, i.e., 20 points for the part on 'dynamical modeling of cell behaviour in space and time’, 20 points for the part on 'population pharmacokinetic-pharmacodynamic modeling' and 10 points for the part on 'computational chemical biology'. For each of the three parts a minimum of half of the corresponding maximum number of points needs to be obtained to pass the course, i.e., 10 points for the part on 'dynamical modeling of cell behaviour in space and time’, 10 points for the part on 'population pharmacokinetic-pharmacodynamic modeling' and 5 points for the part on 'computational chemical biology'. The grade for the exam is subsequently determined by dividing the total number of obtained points by 5. 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.
Reading list
Literature will be provided during the course.
Registration
Registration for the lectures and exam via uSis is mandatory.
Contact
Dr. J.B. Beltman