Welcome to the home 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 2025.
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
- Is the course the right one for me?
- Pre-course survey
- Overview
- Assessment
- Syllabus
- Module Zero: Introduction
- Module I: What are drug targets and where to find them?
- Module II: What can we do if there are no good targets?
- Module III: What kind of drug should we develop?
- Module IV: What efficacy and safety profiles can we expect?
- Module V: For which patients will the drug work and how does it work, really?
- Contact
Is the course the right one for me?
Here are a few unsolicited tips that hopefully help you to determine whether the course is a good choice for you.
- In order to get the most of this course, you are expected to be interested in mathematical and computational methods. With mathematical and computational methods we mean a variety of modeling techniques, such as mechanistic models, statistical models, and causal models, which can be used to describe human biology and body-drug interactions. The course focuses on their applications in drug discovery and development, almost exclusively using real-world examples.
- The course is highly interdisciplinary. You are expected to be familiar with the content covered by the course series Introduction to Applied Mathematics and Informatics In Drug Discovery that run in fall semesters.
- With regard to time: the course takes 2 hours per week and runs only in person. No virtual options are available, and no recordings are provided. Besides the time in classroom, you may need another 2-4 hours’ time every week for reading assignments or programming tasks, depending your proficiency and the depth you wish to go with regard to the tasks.
If you are not sure yet, you are welcome to come over in the first class and try yourself whether it fits you.
Pre-course survey
If you determine to take the course, please fill the pre-course survey. It helps me to adapt the course to your needs.
Overview
Time
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.
Topics that we shall discuss
We mainly discuss following topics from biology
- Genetics and population genetics
- Functional genomics
- Transcriptomics and proteomics
- Chemical biology
- Antisense oligonucleotides and antibodies
- Pharmacokinetics and pharmacodynamics
We mainly discuss applications of following mathematical and computational topics:
- Reproducible research tools, such as git, Snakemake/NextFlow, conda, etc.
- Linear models and generalised linear models
- Inference methods, such as the Expectation-Maximization (EM) algorithm, Hidden Markov Models (HMMs), clustering algorithms, Monte-Carlo methods, and variational methods
- Generative models
- Gene network inference
- Deep-neural networks
- Machine learning, deep learning, and Gaussian Process
- Mathematical modelling
Course material and licensing
Course material, including lecture notes, slides, and reading material, is shared on the course’s web site, https://www.MCBDD.ch, under the Creative Commons Attribution-ShareAlike 4.0 Interactional License unless otherwise specified.
Assessment
The final grade is given by participation (50%) and offline activities (50%).
Syllabus
Module Zero: Introduction
Module Zero is an introduction to mathematical and computational biology in drug discovery. The slides can be found here.
Offline activity:
- Fill the pre-course survey if you have not done so.
- Read How GLP-1 went from being a hard-to-handle hormone to a blockbuster success. What surprises you most?
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.
The slides can be found here. Please fill out the anonymous survey for Module I.
The offline activity contains two parts: (1) reading the paper by Minikel et al., (2) writing code to better understand the relationship between specificity, sensitivity, and prevalence. They are described in the slides #50-#51. Please submit your replies to offline activities by March 14th, 2025 to this Google Form.
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.
The slides can be found here.
The offline activity is about using your favourite programming languages to query APIs (application programming interface) of chemical and biological databases. See slide #26 for the details of the tasks. In order to get the credits, please store your implementation in a GitHub/GitLab repository, and share with me the link to your repository via this Google Form by April 18th, Friday.
In addition to the offline activity, one additional exercise is to understand what factor analysis by using a real-world example to explain how it works. The goal is to understand when it is appropriate to use factor analysis, and how it reassembles and differs from the principal component analysis (PCA) that we addressed in the course. There is no need to submit the outcome of this exercise.
Last but not least, I invite all participants to fill the anonymous survey about Module II. If you have suggestions, questions, or criticisms, you can safely express them there.
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.
- The slides can be found here.
- I invite all participants to fill out an anonymous survey about Module III. It only takes a few minutes, and your input will greatly help to improve the course.
- The offline activity of Module III is to read a draft of review about leveraging protein turnover for drug discovery. The manuscript is distributed via E-mail to course participants. Please fill the Google Form in order to confirm that you finished the task and to share your questions, criticism, and suggestions. The task is due by May 9th, 2025 (Friday).
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 explore the difference between causal inference and statistical modelling, as well as computational analysis and impact of single-cell omics data.
The slides can be found here.
Module V: For which patients will the drug work and how does it work, really?
In module V, we will consider entry-into human and clinical studies from the perspective of PK/PD modelling, biomarker, and causal inference.
The slides can be found here.
Contact
In case you have further questions, comments, and suggestions about the course, please contact the lecturer, Jitao David Zhang, at jitao-david.zhang@unibas.ch.