Premier mercredi du mois (12h - 13h heure de l'est)/First Wednesday of the month (12pm - 1pm Eastern)

Séminaire de biologie quantitative et computationnelle

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.

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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.

Martin Sauvageau

Caroline Colijn



ELSJE PIENAAR Purdue University


MARTIN SMITH Université de Montréal


DANIEL REEVES Fred Hutchison Cancer Research Centre


ERICA MOODIE McGill University






Les séminaires seront tenus de façon virtuelle pour le moment.

Seminars will be held virtually for the time being.

Les séminaires sont tenus chaque 1er mercredi du mois de 12h - 13h heure de l'est.

Seminars are held every 1st Wednesday of the month from 12pm - 1pm Eastern.