Dynamics-informed characterization of molecular, cellular, and population mechanisms

Date

Sep 22, 2022

Time

10:00 AM - 11:00 AM

Speaker

Leonor Saiz

Affiliation

University of California, Davis

Language

en

Main Topic

Biologie

Host

tba

Description

A fundamental question in understanding biological systems is how to translate the quantification of their observed behavior into the characterization of the underlying mechanisms. This process is challenged at multiple levels by diverse intrinsic and extrinsic factors, ranging from unavoidable fluctuations that mask the reporter signals to the inherent system heterogeneity to multiple sequential responses mixed over time. I will discuss the approaches we have used to overcome these challenges in the characterization of the molecular interactions underlying the disaggregation of pathological amyloid structures [1], the cellular processes involved in sustaining gene regulation through the cell cycle [2], and the determinants of regime changes in infection transmission [3]. We rely on the integration of multiple approaches, including molecular mechanics, dynamical systems, stochastic processes, and machine learning. References: 1. A. Franco et al., All-or-none amyloid disassembly via chaperone-triggered fibril unzipping favors clearance of α-synuclein toxic species, Proc. Natl. Acad. Sci. USA 118, e2105548118 (2021). 2. C. Chang et al., Robustness of DNA Looping Across Multiple Cell Divisions in Individual Bacteria, Proc. Natl. Acad. Sci. USA 119, e2200061119 (2022). 3. J.M.G Vilar and L. Saiz, Dynamics-informed deconvolutional neural networks for super-resolution identification of regime changes in epidemiological time series, preprint (2022).

Last modified: Sep 23, 2022, 12:08:03 AM