Center Seminars & Workshops
Events
Thomas Fai
(Brandeis University)
Mathematical models of organelle size control and scaling
Show/Hide Abstract
The size of the nucleus scales robustly with cell size so that the nuclear-to-cell size—the N/C ratio—is maintained during growth in many cell types. To address the fundamental question of how cells maintain the size of their organelles despite the constant turnover of proteins and biomolecules, we consider a model based on osmotic force balance, which predicts a stable nuclear-to-cell size ratio, in good agreement with experiments on the fission yeast Schizosaccharomyces pombe. We model the synthesis of macromolecules during growth using chemical kinetics and demonstrate how the N/C ratio is maintained in homeostasis. We compare the variance in the N/C ratio predicted by the model to that observed experimentally.
04:00 PM -
DRL 4C2
Giovanna Guidoboni
(University of Maine)
From the Blackboard to the Clinic: combining mechanism-driven models with machine learning for personalized medicine
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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