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| Semester | Herbstsemester 2022 |
| Angebotsmuster | Jedes Herbstsemester |
| Dozierende | Jitao David Zhang (jitao-david.zhang@unibas.ch, BeurteilerIn) |
| Inhalt | Applied mathematics and computer science are indispensable in modern drug discovery to enable decisions that have direct impacts on lives. This introductory course will offer a practitioner’s review of mathematical concepts, informatics tools, and industrial approaches in relevant fields, especially bioinformatics, molecular modelling, cheminformatics, mathematical modelling, experiment design and statistical inference, and machine learning. It is hoped that the students are exposed to the interdisciplinary and multiscale modelling nature of drug discovery, and are motivated to deepen their knowledge in relevant fields in future study and practice, to be able to solve open challenges in drug discovery. |
| Lernziele | 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, organ- and system-level, and population level - and how the multiscale modelling approach contributes to drug discovery. |
| Literatur | Lecture notes and slides. Recommend reading (papers, book chapters, etc.) and media (e.g. YouTube videos) will be distributed. |
| Weblink | www.amidd.ch |
| Teilnahmebedingungen | Students of natrual sciences, including biology, physics, chemistry, pharmacy, and medical students are as much welcome as students of mathematics and computer sciences. 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. |
| Anmeldung zur Lehrveranstaltung | Due to the coronavirus pandemic, the course in fall semester 2021 will take place online by Zoom. To attend, join the meeting with this link: https://unibas.zoom.us/j/68803401669. The passcode required to join the meeting is shared among registered students with emails. |
| Unterrichtssprache | Englisch |
| Einsatz digitaler Medien | kein spezifischer Einsatz |
| HörerInnen willkommen |
| Intervall | Wochentag | Zeit | Raum |
|---|---|---|---|
| wöchentlich | Freitag | 12.15-14.00 | Spiegelgasse 5, Seminarraum 05.002 |
| Datum | Zeit | Raum |
|---|---|---|
| Freitag 23.09.2022 | 12.15-14.00 Uhr | Spiegelgasse 5, Seminarraum 05.002 |
| Freitag 30.09.2022 | 12.15-14.00 Uhr | Spiegelgasse 5, Seminarraum 05.002 |
| Freitag 07.10.2022 | 12.15-14.00 Uhr | Spiegelgasse 5, Seminarraum 05.002 |
| Freitag 14.10.2022 | 12.15-14.00 Uhr | Spiegelgasse 5, Seminarraum 05.002 |
| Freitag 21.10.2022 | 12.15-14.00 Uhr | Spiegelgasse 5, Seminarraum 05.002 |
| Freitag 28.10.2022 | 12.15-14.00 Uhr | Spiegelgasse 5, Seminarraum 05.002 |
| Freitag 04.11.2022 | 12.15-14.00 Uhr | Spiegelgasse 5, Seminarraum 05.002 |
| Freitag 11.11.2022 | 12.15-14.00 Uhr | Spiegelgasse 5, Seminarraum 05.002 |
| Freitag 18.11.2022 | 12.15-14.00 Uhr | Kollegienhaus, Hörsaal 120 |
| Freitag 25.11.2022 | 12.15-14.00 Uhr | Dies Academicus |
| Freitag 02.12.2022 | 12.15-14.00 Uhr | Spiegelgasse 5, Seminarraum 05.002 |
| Freitag 09.12.2022 | 12.15-14.00 Uhr | Spiegelgasse 5, Seminarraum 05.002 |
| Freitag 16.12.2022 | 12.15-14.00 Uhr | Spiegelgasse 5, Seminarraum 05.002 |
| Freitag 23.12.2022 | 12.15-14.00 Uhr | Spiegelgasse 5, Seminarraum 05.002 |
| Module |
Modul: Angewandte Mathematik (Bachelorstudium: Mathematik) Modul: Applications and Related Topics (Bachelorstudium: Computer Science) Modul: Applications and Related Topics (Bachelor Studienfach: Computer Science) Modul: Electives in Data Science (Masterstudium: Data Science) |
| Leistungsüberprüfung | Lehrveranst.-begleitend |
| Hinweise zur Leistungsüberprüfung | Scores will be given in scale 1-6 by 0.5, by participation (20%), near-end-term presentation (30%), and end-term project work (50%). |
| An-/Abmeldung zur Leistungsüberprüfung | Anm.: Belegen Lehrveranstaltung; Abm.: stornieren |
| Wiederholungsprüfung | keine Wiederholungsprüfung |
| Skala | 1-6 0,5 |
| Wiederholtes Belegen | beliebig wiederholbar |
| Zuständige Fakultät | Philosophisch-Naturwissenschaftliche Fakultät, studiendekanat-philnat@unibas.ch |
| Anbietende Organisationseinheit | Fachbereich Mathematik |