The Maud Menten Institute: 2024-2027
The primary purpose of the Maud Menten Institute (MMI) is to provide a collaborative research platform for mathematical biologists at PIMS sites to promote interactions with life science experts and
decision makers in government, industry and NGOs. The secondary purpose of the Maud Menten Institute is to develop the required training program for the non-academic pipeline in mathematical biology. This program will allow new generations of mathematical biologists to impact biological fields inside and outside of academia and will explicitly establish mathematical biology as a powerful tool of investigation in biology.
Visit the Maud Menten Institute Website
PRN Leadership Structure
Co-directors
- Mark Lewis (Professor, Kennedy Chair in Mathematical Biology, Department of Math/Stat and Department of Biology, University of Victoria)
- Stéphanie Portet (Professor, Department of Mathematics, University of Manitoba)
Co-applicants
- Dan Coombs (University of British Columbia)
- Mark Lewis (University of Victoria)
- Ailene MacPherson (Simon Fraser University)
- Stéphanie Portet (University of Manitoba)
- Rebecca Tyson (University of British Columbia - Okanagan)
- Hao Wang (University of Alberta)
Scientific Committee
In addition to the co-applicants listed above:
- Ivana Bozic (University of Washington)
- Elena Braverman (University of Calgary)
- Martin Krkosek (Salmon Coast Society Board Member)
- James McVittie (University of Regina)
- Marc Roussel (University of Lethbridge)
- Chris Soteros (University of Saskatchewan)
- Amanda Slaunwhite (BC Centre for Disease Control)
External Scientific Committee Members
- Adriana Dawes (Ohio State University)
- Alan Hastings (University of California Davis)
- Philip Maini (University of Oxford)
- Pauline van den Driessche (University of Victoria)
PRN Associated Network Wide Courses
Topics in Mathematical Biology: biological image data and shape analysis
Instructor: Khanh Dao Duc (University of British Columbia)
Date: Sep 3, 2024 — Dec 6, 2024
Abstract
Advances in imaging techniques have enabled the access to 3D shapes present in a variety of biological structures: organs, cells, organelles, and proteins. Since biological shapes are related to physiological functions, biological studies are poised to leverage such data, asking a common statistical question: how can we build mathematical and statistical descriptions of biological morphologies and their variations? In this course, we will review recent attempts to use advanced mathematical concepts to formalize and study shape heterogeneity, covering a wide range of imaging methods and applications. The main mathematical focus will be on basics of image processing (segmentation, skeletonization, meshing), Diffeomorphisms and metrics over shape space, optimal transport theory with application for image analysis, manifold learning, with some other concepts covered in specific applications (e.g. quasiconformal mapping theory for shape representation, 3D reconstruction in Fourier space…). Students will be encourage to work in groups to present research papers and do a small project to pass the course. This course will also build on the recent BIRS workshop, Joint Mathematics Meetings, and the upcoming SIAM workshops (LSI 2024, SIMODS 2024) on this topic, with some participants to these events invited to contribute to this course and present their research.