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DESCRIPTION:Integrating large- to high- dimension markers in mechanistic mo
 dels\n\nMelanie Prague\, Université de Bordeaux\n	Tuesday April 14\, 12-1pm
 \n	Zoom Link: https://mcgill.zoom.us/j/87078928687\n	In Person: 550 Sherbroo
 ke\, Room 189\n	\n	Abstract: Mechanistic models are widely used to describe 
 and explain biological processes over time. However\, they typically rely 
 on a limited number of observable compartments and sparse longitudinal dat
 a. As a result\, these models are often either too simple to capture compl
 ex biological phenomena or they face identifiability issues\, particularly
  when considering interindividual variability in the form of nonlinear mix
 ed-effects models based on systems of differential equations. In parallel\
 , with ongoing technological advances\, longitudinal high-throughput data 
 (e.g.\, -omics\, including transcriptomics and proteomics data) are increa
 singly available in various contexts and could bring valuable information 
 into mechanistic models to better capture underlying biological processes.
  However\, integrating such high-dimensional data to inform the dynamics r
 emains a major challenge\, both mathematically and for broader interpretat
 ion in public health applications.\n\nIn this talk\, I will present two co
 mplementary approaches for integrating large- to high-dimensional biomarke
 rs into mechanistic models. The first approach\, called lasso-SAMBA\, addr
 esses robust covariate selection in ODE-based non-linear mixed-effect mode
 ls. It extends the original SAMBA algorithm (which is an iterative model-b
 uilding algorithm that fastly and sequentially identifies relevant covaria
 tes on parameters while estimating model using the SAEM algorithm) by repl
 acing stepwise inclusion with Lasso regression combined with stability sel
 ection\, ensuring a more reliable identification of relevant covariates wh
 ile preserving the monotonic decrease of the information criterion. The se
 cond approach uses observed -omics data to infer the dynamics of unobserve
 d immune compartments. It relies on an iterative algorithm that alternates
  between a regularization step\, which identifies the most informative bio
 markers through penalized likelihood derivatives\, and a mechanistic infer
 ence step\, where population parameters are estimated using the SAEM algor
 ithm in Monolix. This framework enables the selection of biomarkers whose 
 temporal patterns best reflect the latent compartments in our model.\n\nTo
 gether\, these methods provide powerful tools for integrating and selectin
 g high-dimensional biological data in mechanistic modeling. They will be i
 llustrated on exemples of immune dynamics after vaccination for Varicella-
 Zoster virus and immune-viral dynamics after SARS-CoV-2 infection.\n
DTSTART:20260414T160000Z
DTEND:20260414T170000Z
SUMMARY:QLS Seminar Series - Melanie Prague
URL:/qls/channels/event/qls-seminar-series-melanie-pra
 gue-371937
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