Center Seminars & Workshops
Events
Sebastien Roch
(University of Wisconsin, Madison)
04:00 PM -
Online
Débora Princepe
(The Abdus Salam International Centre for Theoretical Physics)
Nuclear compensatory evolution driven by mito-nuclear incompatibilities
Show/Hide Abstract
The mitochondrial DNA (mtDNA) plays a fundamental role in cell respiration and the maintenance of eukaryotic life. However, respiration also requires proteins encoded in the nuclear DNA (nDNA), leading to an intricate interaction and co-evolution of these two genetic sequences. Maintaining compatibility between proteins from the nucleus and mitochondria is essential for proper cell function. Understanding how this compatibility persists over time, despite the susceptibility of mtDNA to accumulate harmful mutations, is of great importance. Mechanisms like purifying selection in mtDNA and compensatory mutations in nDNA have been proposed and scrutinized. In this talk, I will present an individual-based model that elucidates the timing and mechanisms of these processes. We show that not only the mtDNA mutation rate plays a role in the process but also the strength of mito-nuclear selection and the initial degree of incompatibility.
02:00 PM -
DRL 4C2
Thomas Fai
(Brandeis University)
-
Online
Giovanna Guidoboni
(University of Maine)
From the Blackboard to the Clinic: combining mechanism-driven models with machine learning for personalized medicine
Show/Hide Abstract
Machine Learning (ML) aims at extracting information and knowledge from data. ML is naturally
interdisciplinary, as it bridges fundamental techniques of data analysis, typically developed by
mathematicians, statisticians and computer scientists, with the needs of actionable insights that
are specific to the particular application domain.
Mechanism-driven models are based on the principles of physics and physiology and allow for
identification of cause-to-effect relationships among interplaying factors in a complex system.
While invaluable for causality, mechanism-driven models are often based on simplifying
assumptions to make them tractable for analysis and simulation; however, this often brings into
question their relevance beyond theoretical explorations.
The combination of mechanism-driven and data-driven models allows us to harness the
advantages of both approaches, as mechanism-driven models excel at interpretability but
suffer from a lack of scalability, while data-driven models are excellent at scale but suffer in
terms of generalizability and insights for hypothesis generation. This combined, integrative
approach represents the pillar of the interdisciplinary approach to data science that will be
discussed in this talk, with applications spanning from glaucoma research to cardiovascular
monitoring and physiology of the lower urinary tract (LUT).
04:00 PM -
Online