This is an archived page of the course 2019. Visit here to view the page of the current course.

Welcome to the website for Introduction to Applied Mathematics and Informatics In Drug Discovery, the course series running at the Department of Mathematics and Informatics, University of Basel.

The course in autumn semester 2019 has been finished. The next course will take place in autumn semester 2020.

Time and place

The lecture takes place on Fridays between 12:15 and 14:00 in Seminarraum 5.002, Department of Mathematics and Informatics, University of Basel, Spiegelgasse 1, 4051 Basel.

Course material, including lecture notes, slides, and reading material, are shared on the course’s web site, AMIDD.ch, unless otherwise specified in the course.

Syllabus

1. Drug discovery: an overview (20.09.2019)
   • Handout Course info
   • Slides
   • Handout Package insert demo
   • After-read: Principles of early drug discovery by Hughes et al.
2. The central dogma and Vemurafenib (27.09)
   • Pre-read: Wikipedia pages Central dogma of molecular biology and Sequence analysis
   • In- and after-read: Bollag et al., 2010
   • Handout
3. Biological sequence analysis (4.10.)
   • Slides
4. From protein structure to screening (11.10)
   • Slides
   • In- and after-read: Tsai et al., PNAS, 2008
5. Screening and drug design (18.10.)
   • Slides
   • Optional after-read: Mathematical techniques in structural biology, by J.R. Quine
6. From molecular modelling to network analysis (25.10.)
   • Slides
   • In-depth read for interested students: Computational Methods in Drug Discovery by Silwoski et al.
7. Omics and cellular modelling (1.11.)
   • Slides
8. PK/PD and PBPK modelling (8.11.)
   • Only board is used for today’s lecture
   • Recommended read for all, Introduction to PK/PD modelling, by Mortensen et al, DTU Informatics, 2008.
   • Recommended read for mathematicians about Numerical Transforms, by Ronald N. Bracewell, Nature 248 (4956), 697-704, 1990.
9. Population modelling and clinical trial (15.11.)
   • Only board is used for today’s lecture
   • Recommended read: Basic concepts in Population Modelling, Simulation, and Model-based Drug Development, by Mould and Upton, CPT: Pharmacometrics & Systems Pharmacology (2013).
   • Interested students are encouraged to also read the follow-up pieces. In-depth discussions on PK modelling can be found in Part 2. Read Part 3 for more information on PD population modeling.
10. Guest speakers: Dr. Lucy Hutchinson and Dr. Nicolas Frey (22.11.)
    • Flyer
    • Talk by Dr. Lucy Hutchinson (12:15-13:00): Mathematical modeling in academia and industry
    • Talk by Dr. Nicolas Frey (13:15-14:00): Introduction to Clinical Pharmacometrics, or About the Role of Mathematical Modeling and Simulation in Clinical Drug Development
11. Dies academicus - no lecture (29.11.)
12. Guest speakers: Dr. Kaspar Rufibach and Dr. Benjamin Ribba (6.12)
    • Flyer
    • Talk by Dr. Kaspar Rufibach (12:15-13:00): Interim look into pivotal clinical trials - why it matters and how to do it
    • Talk by Dr. Benjamin Ribba (13:15-14:00): The mathematics behind medicine prediction
13. Student presentation (I) (13.12.)
    • Paper #1
      • The chemical basis of morphogenesis by Alan Turing, Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 1950.
      • Presentation by Group #1: Slides
    • Paper #2
      • How were new medicines discovered? by Swinney and Anthony, Nature Reviews Drug Discovery, 2011.
      • Presentation by Group #2: Slides
    • Paper #3
      • Opportunities and challenges in phenotypic drug discovery: an industrial perspective by Moffat et al, Nature Reviews Drug Discovery, 2017.
      • Presentation by Group #3: Slides.The presentation slides are also available online in the Prezi format, at this link.
14. Student presentation (II) (20.12.)
    • Paper #4
      • A quantitative description of membrane current and its application to conduction and excitation in nerve by Hodgkin and Huxley, The Journal of Physiology, 1952.
      • Presentation by Group #4: Slides.
    • Paper #5
      • Key factors in the rising cost of new drug discovey and development by Dickson and Gagnon, Nature Reviews Drug Discovery, 2004.
      • Presentation by Group #5: Slides
    • Paper #6
      • Applications of machine learning in drug discovery and development by Vamathevan et al., Nature Reviews Drug Discovery, 2019.
      • Presentation by Group #6: Slides

Assessment

The final note is given by participation (20%), presentation (30%), and an oral examination (50%).

The oral examination (20 min) will be about concepts that we learned together, and about explaining mathematical concepts (or concepts in your domain of experts) to a layman - your lecturer.

Further information

• We focus on interdisciplinary research with mathematics as the language and informatics as the tool.

• I do not offer exercise hour yet. Pre-reading and post-reading articles, as well as videos, are shared and recommended.

• Both slides and board are used for the course. Slides and notes are shared.

Further questions or suggestions?

Please contact the lecturer, Jitao David Zhang, at jitao-david.zhang@unibas.ch.