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Mathematical and Computational Biology In Drug Discovery

University of Basel/ Spring Semester 2022/ Fridays 12:15-14:00

Welcome to the web page for Mathematical and Computational Biology in Drug Discovery, the course series running at the Department of Mathematics and Computer Science, University of Basel in the spring semester 2022.

The course is open to all students who wish to learn about principles and techniques of mathematical and computational biology as well as their applications in drug discovery.

Find administrative details about the lecture in the course directory of University of Basel.

Table of content


To prepare for the course, please (1) check that you have acquired required prior knowledge, (2) make sure that you can commit enough time to the course, (3) note that lectures in 2022 will be in person, and (4) fill the pre-course survey.

You should foremost be familiar with the content covered by the course series Introduction to Applied Mathematics and Informatics In Drug Discovery that run in fall semesters.

The MCBDD course on average requires about 4-6 hours’ time each week for reading assignments or programming tasks.

Finally, to make the course useful and valuable for you, I invite you again to fill the pre-course survey. Your opinions help to shape the course.



Lectures take place on Fridays between 12:15 and 14:00 in Seminarraum 05.002 in Spiegelgasse 5, near Schifflände, 4070 Basel. See Syllabus for the topics we plan to cover.

Course material and licensing

Course material, including lecture notes, slides, and reading material, is shared on the course’s web site,, under the Creative Commons Attribution-ShareAlike 4.0 Interactional License unless otherwise specified.


The final note is given by offline activities (50%) and project work (50%).

For end-term project work, participants will self-organize and form teams of two. In exceptional cases where forming a team is not feasible (i.e. due to remote learning), an individual contribution is possible.

The teams work on either option of the project work of their choice:

Once the project report is submitted, it will peer-reviewed by another group, which give comments and suggestions.


Notes for the course are given when both project report and peer review are submitted.


Module Zero: Introduction

Module I is an introduction to mathematical and computational biology in drug discovery.

Module I: What are drug targets and where to find them?

This module consists of two lectures: (1) what makes a good drug target , and (2) how to identify, assess, and validate drug targets?

Prior to attending the courses, you can refresh your knowledge in the central dogma of molecular biology and in the human genome by watching the animation film From DNA to protein - 3D by yourgenome, and the film mRNA processing and the spliceosome by WEHI that combines an artist’s impression and simulation.

Module II: What can we do if there are no good targets?

Module II discusses about alternatives to target-based drug discovery, in particular phenotypic drug discovery. It includes two lectures: (1) phenotypic screening with chemogenomic libraries, and (2) molecular phenotypic screening based on gene expression.

Module III: What kind of drug should we develop?

Module III considers modality selection from a computational point of view. It includes two lectures: (1) novel small molecules and antisense oligonucleotides, and (2) antibodies, multi-target drugs, and gene- and cell-therapies.

Please submit your answers, together with your code on GitHub or other software repo, to this Google Form for Module III offline activity by June 18th, 2022.

Module IV: What efficacy and safety profiles can we expect?

Module IV focuses on MoA inference for safety and efficacy profiles of drug candidates. We will mainly computational analysis and impact of single-cell omics data, and explore the potential of proteomics to infer mode of action.

The offline activities of Module IV is to do single-cell RNA-seq analysis yourself with either Python or R. This activity is optional: it does not count to the final grade. I recommend you doing it because you can get first-hand experience analysing high-dimensional, sparse, and noisy biological data. See slides for links to tutorials and courses.

Module V: For which patients will the drug work and how does it work, really?

We consider the problem of causal inference in Module V.

There is no offline activity of Module V.

Topics that we shall discuss

We mainly discuss following topics from biology

We mainly discuss applications of following mathematical and computational topics:


In case you have further questions, comments, and suggestions about the course, please contact the lecturer, Jitao David Zhang, at