Jitao David Zhang
This document and the course material are published under the Creative Commons Attribution-ShareAlike 4.0 International License. They were prepraed and accomplished by Jitao David Zhang in his personal capacity. The opinions expressed in this document and the course material are author's own and do not reflect the view of F. Hoffmann-La Roche Ltd.
- Drug discovery: an overview (20.09.2019)
- Biological sequence analysis (27.09)
- Protein structure and function (4.10.)
- Chemical structure representation and search (11.10)
- Molecular interaction and modelling (18.10.)
- Omics: genomics, transcriptomics, and proteomics (25.10.)
- PK/PD and PBPK modelling (1.11.)
- Bayesian modelling, machine learning, and causal inference (8.11.)
- Multiscale modelling of drug mechanism and safety (15.11)
- Guest speakers: Dr. Nicolas Frey and Dr. Lucy Hutchinson (titles to be announced) (22.11.)
- Dies academicus - no lecture (29.11.)
- Guest speakers: Dr. Kaspar Rufibach and Dr. Benjamin Ribba (titles to be announced) (6.12.)
- Student presentation (I) (13.12.)
- Student presentation (II) (20.12.)
Introduction to Applied Mathematics and Informatics In Drug Discovery (iAMIDD) is a course series designed for an audience of undergraduate and master students who are interested in understanding how applications of mathematics and informatics complement experimental approaches and impact drug discovery.
Interdisciplinary research is imperative to drug discovery and disease understanding in the industrial setting. Mathematics and informatics play a central role in orchestrating different fields of research and integrating available knowledge and information to enable decisions. The speed and quality of these decisions have direct impacts on life quality of patients and their families. Therefore I believe it is important to discuss industrial questions and practices with students to encourage scientific exchange and future innovation.
It is hoped that with an introduction to applied mathematics and informatics in drug discovery, especially the motivating questions, concepts and models, and relevant open-source software, the students are motivated to deepen their knowledge in relevant fields in order to solve open challenges in drug discovery.
This course is designed for all students who are interested in applied mathematics and informatics and their applications in modern drug discovery. Though no prerequisite courses are obligatory, elementary understanding of statistics, probability, calculus, and ordinary differential equations are helpful. High-school knowledge in physics, chemistry, and biology are required. Knowledge and proficiency in at least one programming language (preferably C/C++, Java, R, Python, or Julia) is very helpful to try real-world problems.
The course is designed to be part of the curriculum of applied mathematics for undergraduate and master students to give a high-level overview of applications of mathematics and informatics tools in drug discovery. Therefore, while key concepts and principles are introduced, almost all subjects can only be briefly and likely superficially touched. Intensive investigations into some subjects, for instance bioinformatics and computational biology, are planned as follow-up courses.
The course consists of element sessions (90 minutes each), a near-end-term presentation, and an final oral examination. Besides an introduction to drug discovery, the sessions will cover (1) bioinformatics and computational biology, (2) cheminformatics and computer-aided drug design, (3) mathematical modelling, and (4) statistics and machine learning. The sessions are so arranged that they roughly reflect the linear model of the drug discovery process, including target assessment, screening, lead identification and optimization, preclinical safety evaluation, PK/PD modelling and clinical trials prior to filing and regulatory approval.
Lecture notes and slides. Recommend reading (papers, book chapters, etc.) and media (e.g. YouTube videos) will be distributed.
We explore the drug-discovery process and study applications of mathematics and informatics with case studies. We examine how mathematics concepts and informatics tools are used to model complex systems at multiple levels - molecular level, cellular and omics level, and organ- and system-level - and how the multiscale modelling approach contributes to drug discovery.
Scores will be given by participation (20%), near-end-term presentation (30%), and end-term oral examination (50%).
I got great input and support from numerous colleagues to design and implement the course. Especially I would like to thank
- Martin Ebeling, Fabian Birzele, and Iakov Davydov for suggestions and help with the bioinformatics part.
- Lisa Sach-Peltason, Christian Kramer and Michael Reutlinger for teaching me a lot about cheminformatics and drug design.
- Manfred Kansy, Holger Fischer, and Matthias Nettekoven for leading me into the field of medicinal chemistry and pharmacology.
- Arne Rufer, Ken Wang, Norman Mazer, Neil Parrot Jones, Francois Mercier, Benjamin Ribba, Hans-Peter Grimm, and Nicolas Frey for selfless sharing of knowledge in mathematical modelling, PK/PD modelling, and clinical pharmacology and pharmacometrics.
- Prof. Markus von Kienlin, Andreas Bruns, Gonzalo Christian Duran Pacheco, Stanley Lasiz, Kasper Rufibach for statistics, biomarker and clinical development.
I also want to extend my thanks to Prof. Gianluca Crippa, Prof. Helmut Harbrecht , Prof. Jiří Černý, Dr. Jung Kyu Canci, Prof. Enno Lenzmann for the kind support for me to offer this course at the Department of Mathematics and Informatics, University of Basel. Philipp Mekler, Gang Mu, and many students provided great support and help to shape the lectures. I dedicate the lecture series to Clemens Broger, a great mentor of mine and many other colleagues, who stayed curious, courageous, passionate, and true to himself until the last day of his life.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
