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 2021.
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.
Table of content
- 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?
- Topics that we shall discuss
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) be aware that the lecture will take place virtually on Zoom, 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.
Due to the coronavirus pandemic, the MCBDD course in 2021 will take place online with Zoom. The meeting link is shared with registered participants via Email.
Finally, to make the course useful and valuable for you, I invite you to fill the pre-course survey. Your opinions help to shape the course.
Lectures take place on Fridays between 12:15 and 14:00 on Zoom. 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, https://www.MCBDD.ch, under the Creative Commons Attribution-ShareAlike 4.0 Interactional License unless otherwise specified.
All Zoom sessions are recorded and distributed among attendees.
The final note is given by offline activities (50%) and project work (50%).
For end-term project work, participants will form teams of two, and work on either option of the project work:
- Option 1: Write a target (or screening) proposal for a disease of your choice, using publicly available data and your analysis to support your arguments.
- Option 2: Write a Rmarkdown/Jupyter report analysing data from the Drug Central database, raising your own scientific questions about drug-target associations and answering them with analysis.
Once the project report is submitted, it will peer-reviewed by another group, which give comments and suggestions.
- Students assigning themselves in groups of two with Doodle: by June 1st (Monday);
- Submission deadline: June 21th (Monday);
- Peer review submission deadline: July 5th (Monday).
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 on 05.03.2021.
- Slides of lecture #1: we covered all slides up to slide #19. The reproducible research part will be covered later.
- Recording: passcode is shared by email.
- Offline activities (see slide page 26)
- Please fill the anonymous post-lecture survey. All post-lecture surveys are optional, however I encourage you to fill them because your feedback is important for me to constantly improve the course and to address your burning questions.
- Required reading: Emmerich et al., Nature Reviews Drug Discovery, 2021
- Optional reading: Jones et al., Science, 2021
Module I: What are drug targets and where to find them?
This module consists of two lectures: (1) what makes a good drug target (12.03.2021), and (2) how to identify, assess, and validate drug targets? (19.03.2021).
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.
- Slides of Module I
- Offline activities of Module I. Please finish this and further offline activities before semester ends to get credits.
- Lecture #2 (12.03.2021)
- Lecture #3 (19.03.2021)
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 (26.03.2021), and (2) molecular phenotypic screening based on gene expression (09.04.2021). We will have no lecture on 02.04.2021, the Good Friday.
- Slides of Module II
- Offline activities of Module II: The explanation can be found in the slides (slide number #26). Please finish the activities before the end of semester to get credits.
- Lecture #4 (26.03.2021)
- Lecture #5 (09.04.2021)
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 (16.04.2021), and (2) antibodies, multi-target drugs, and gene- and cell-therapies (23.04.2021).
- Slides of Module III
- Offline activities of Module III: read three pieces about RNA therapies and
vaccinations, and build your own opinions about them. You do not need to
submit your opinions, however, you are welcome to raise any questions that you
may have via emails or anonymous surveys.
- Levin, Arthur A. 2019. “Treating Disease at the RNA Level with Oligonucleotides.” New England Journal of Medicine 380 (1): 57–70. Link;
- Blog post Oligonucleotides and their discontents by Derek Loewe in In The Pipeline, Link;
- Article The next act for messenger RNA could be bigger than covid vaccines, published in MIT Technology Review 2021, by Antonio Regalado, Link.
- Lecture #6 (16.04.2021)
- Lecture #7 (23.04.2021)
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 (30.04.2021), and explore the potential of proteomics to infer mode of action (21.05.2021).
- Slides of Module IV
- Lecture #8 (30.04.2021)
- Lecture #9 (07.05.2021): guest lectures by three computational biologists
working in drug discovery: Dr. Fabian Birzele, Dr. Petra Schwalie, and Dr.
- Anonymous post-lecture survey
- Presentation by Dr. Fabian Birzele: “Groundhog day” and what we can learn from that… (slides)
- Presentation by Dr. Petra Schwalie: scRNA-Seq analysis in Cancer Immunotherapy Pharma Research (slides)
- Presentation by Dr. Tony Kam-Thong: Time Series and Dynamics Systems Analyses in Drug Discovery (slides)
- We will have no lecture on 14.05.2021, the Ascension Day (Auffahrt).
- Lecture #10 (21.05.2021)
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.
- If you are new to the topic of single-cell RNA sequencing, please follow the PBMC tutorial of Scanpy (python) or the PBMC tutorial of Seurat (R). They both describe the analysis of a peripheral blood mononuclear cell (PBMC) dataset.
- If you have experience with such data already, checkout the NBIS workshop on single-cell sequencing data analysis to cover advanced topics such as spatial transcriptomics and trajectory inference.
Module V: For which patients will the drug work and how does it work, really?
We consider the problem of biomarker identification and causal inference in Module V. We will learn about how mathematical and computational biology contributes to biomarker identification (28.05.2021), and consider how to model disease progression and drug’s action using integrated modelling with knowledge, data, and software (04.06.2021).
- Slides of Module V
- Lecture #11 (28.05.2021)
- Lecture #12 (04.06.2021)
Topics that we shall discuss
We mainly discuss following topics from biology
- Genetics and population genetics
- Genomics and comparative genomics
- Transcriptomics and proteomics
- Chemical biology
- Functional genomics
- 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
- Machine learning, deep learning, and Gaussian Process
- Mathematical modelling
In case you have further questions, comments, and suggestions about the course, please contact the lecturer, Jitao David Zhang, at firstname.lastname@example.org.