Premier mercredi du mois (12h - 13h heure de l'est)/First Wednesday of the month (12pm - 1pm Eastern)
Les séminaires de biologie quantitative et computationnelle réunissent les chercheurs en biologie, médecine, pharmacie, informatique, mathématiques et statistique dans le but de favoriser les échanges et d'établir des collaborations. Ils sont organisés par le groupe de biologie computationnelle au Département de mathématiques et de statistique à l'Université de Montréal.
The Seminar Series on Quantitative and Computational Biology is an initiative of the Computational Biology group at the Department of Mathematics and Statistics at the University of Montreal that serves to bring together researchers in Biology, Medicine, Pharmacy, Informatics, Mathematics, and Statistics to share new results and establish collaborations.
Pour être ajouté à notre liste d'envoi, SVP envoyez-nous un courriel.
To be added to our mailing list, please send us an email.
HORAIRE/SCHEDULE
Informing treatment regimen design of immunotherapies for HIV
Elsje Pienaar
Immunomodulatory drugs could be part of a functional cure for Human Immunodeficiency Virus (HIV), but immunotherapies have so far proven ineffective. This is in part due to an incomplete understanding of the systems level host immune responses to immunotherapy. Non-human primate (NHP) data have shown that N-803, an interleukin-15 (IL-15) superagonist, can transiently reduce viral loads, but that efficacy wanes over time as more doses are given. This talk will describe our use of mathematical models of N-803 treatment in SIV-infected NHPs to estimate the contributions of three key mechanisms to treatment outcomes: 1) drug tolerance, 2) immune regulation and 3) viral escape. These models are then applied to inform treatment regimen design, advancing the potential impact of immunotherapy in HIV.
Mapping the uncharted genome at single-molecule resolution
Martin Smith
Less than 2% of the human genome codes for proteins, yet over 70% is transcribed into RNA. The great diversity of non-protein coding RNA transcripts (ncRNAs) and their roles in normal development and disease are confounded by our poor understanding of their biological functions. Given that over 80% of known genetic variants associated with human diseases ocurr outside of protein-coding regions of the genome, methods for the systematic annotation and classification of ncRNAs are essential to better understand the molecular protagonists of complex diseases. I will present how we are tackling this challenge using comparative genomics, machine learning and the latest single molecule sequencing technologies.
The pathway to a unified phylodynamic model of the HIV reservoir
Daniel Reeves
The reservoir of latently infected cells is the primary barrier to cure of HIV. These cells persist for decades despite potent antiretrovirals that suppress virus to undetectable levels. We have previously found that the reservoir is mostly clonal. Paradoxically, host immune cells perpetuate HIV by dividing, and their faithful copying mechanisms create HIV clones in numbers far beyond what would be expected by error prone viral replication. Here I will discuss the ecology of HIV clonality during ART, how ecology shifts over time before and during ART, as well as how and when the reservoir is created. These modeling results pave the way for a unified model of both viral and evolutionary dynamics for HIV and the reservoir.
General regression methods for respondent-driven sampling data
Mamadou Yauck
Respondent-Driven Sampling (RDS) is a variant of link-tracing sampling techniques that aim to recruit hard-to-reach populations by leveraging individuals' social relationships. As such, an RDS sample has a graphical component which represents a partially observed network of unknown structure. Moreover, it is common to observe homophily, or the tendency to form connections with individuals who share similar traits. Currently, there is a lack of principled guidance on multivariate modeling strategies for RDS to address homophilic covariates and the dependence between observations within the network. In this work, we propose a methodology for general regression techniques using RDS data. This is used to study the socio-demographic predictors of HIV treatment optimism (about the value of antiretroviral therapy) among gay, bisexual and other men who have sex with men, recruited into an RDS study in Montreal, Canada.
Silent cancer agents: the ecology and evolution of oncoviruses
Carmen Lia Murall
Globally, 1 in 10 cancers is caused by a virus. Yet the vast majority of oncovirus infections do not progress to cancer or mortality. It is unclear, why these viruses, even with their potent oncogenes, are not more virulent. Under what conditions do they drive cancer? As we develop vaccines and treatments against oncoviruses, we create novel selective pressures and environments for which these oncoviruses can evolve. I will present my work into general oncovirus oncogenicity and I will discuss advancements in our understanding of the ecology and evolution of human papillomaviruses. Papillomaviruses are the best understood family of human oncoviruses and the global use of multi-strain vaccines against them is allowing us to study vaccine-driven evolution potential in human populations.
In vivo genomics approaches to uncover functions of lncRNA loci
Martin Sauvageau
Most of our genome is transcribed, generating tens of thousands of long noncoding RNAs, several of which were found to play important regulatory roles. In addition to being expressed with exquisite tissue specificity, evidence point to lncRNA loci as risk factors frequently deregulated or mutated in a wide variety of diseases. Despite this, we still have a poor understanding of how they may affect transcriptional programs and signaling pathways and to what extent they contribute to the establishment or progression of diseases. We have identified multiple lncRNA loci with a developmental role and revealed the multi-layered complexity of some of them in regulating gene expression. Here, I will present our efforts to characterize the role and mechanism of action of lncRNAs by combining human genetics data and animal models with biochemical, genomics and computational approaches. I will also discuss the challenges in dissecting their roles.
Modelling and policy in the COVID-19 pandemic
Caroline Colijn
I will describe several mathematical models related to COVID-19 transmission and their use and application in public health. First I will describe how we have modelled social distancing and its impact using Bayesian estimation and briefly remark on the uses of this modelling structure for short-term forecasting of the impact of higher-transmission variants of concern. Next I will describe modelling vaccination rollout and the advantages of vaccinating essential workers -- defined as those with unavoidable high contact levels at work -- earlier in the vaccination program in Canada.
CONFÉRENCIERS/
SPEAKERS
ELSJE PIENAAR Purdue University
MARTIN SMITH Université de Montréal
DANIEL REEVES Fred Hutchison Cancer Research Centre
ERICA MOODIE McGill University
CARMEN LIA MURALL CNRS Montpellier
MARTIN SAUVAGEAU IRCM
