
Contact & links
2601 Chemin de la Canardière
Québec (Québec)
G1J 2G3
Canada
Simon Hardy
PhD, P.eng.
Associate professor, Département d’informatique et de génie logiciel & Département de biochimie, microbiologie et bio-informatique, Université Laval
Investigator, CERVO Brain Research Centre
Research axis:
Cellular and Molecular Neurosciences
Simulation
Cell signaling
Development of mathematical models to understand and analyze complex biological systems
Professor Simon Hardy develops mathematical and computational models based on experimental data that allow us to analyze the complex dynamics of biological systems.
In collaboration with biologists, Dr. Hardy’s team is developing theoretical models of neurons integrating physiological, biochemical and electrical current measurements in neural networks.
The models developed can predict the system’s response in different situations, how the normal behavior of the system can be disrupted to lead to a disorder or disease, and determine the interventions that could restore it to a state of health.
In my group, we build realistic models of biological systems and develop analysis computational methods to study their control mechanisms.
Our mathematical and computational models are built from experimental data and allow for the exploration of the complex dynamics of biological systems. We use concepts from control theory to explain the mechanisms of cell regulation, signal transduction in particular. This helps us understand the response of systems, how a system behavior can be altered to become pathological and which interventions will restore it to a normal behavior. We develop the dynamic graph methodology and use formal methods as tools to discover new knowledge in cell biology.
We work in collaboration with biologists to integrate modeling and simulation to the «wet lab» experimental process to generate new hypotheses. We also plan to develop a new generation of theoretical models of neurons that will combine electrophysiology, biochemistry and ionic fluxes.
Associate professor – Université Laval, 2017 – present
Assistant professor – Université Laval, 2011 – 2017
Postdoctoral fellow – Systems Biology Center New York, Mount Sinai School of Medicine, Iyengar Lab 2007 – 2011
Short-term postdoctoral fellow – National Center for Biological Sciences, India, Bhalla Lab 2010
PhD – Computer engineering, École Polytechnique de Montréal
MASc – Computer engineering, École Polytechnique de Montréal
BEng – Computer engineering, École Polytechnique de Montréal
[prix-par-tag]
Publications
Simon Gamache-Poirier; Alexia Souvane; William Leclerc; Catherine Villeneuve; Simon V Hardy
An algorithm for the transformation of the Petri net models of biological signaling networks into influence graphs Journal Article
In: Biosystems, vol. 250, pp. 105415, 2025, ISSN: 1872-8324.
@article{pmid39986345,
title = {An algorithm for the transformation of the Petri net models of biological signaling networks into influence graphs},
author = {Simon Gamache-Poirier and Alexia Souvane and William Leclerc and Catherine Villeneuve and Simon V Hardy},
doi = {10.1016/j.biosystems.2025.105415},
issn = {1872-8324},
year = {2025},
date = {2025-04-01},
journal = {Biosystems},
volume = {250},
pages = {105415},
abstract = {A common depiction for biological signaling networks is the influence graph in which the activation and inhibition effects between molecular species are shown with vertices and arcs connecting them. Another formalism for reaction-based models is the Petri nets which has a graphical representation and a mathematical notation that enables structural analysis and quantitative simulation. In this paper, we present an algorithm based on Petri nets topological features for the transformation of the computational model of a biological signaling network into an annotated influence graph. We also show the transformation of the Petri nets model of the beta-adrenergic receptor activating the PKA-MAPK signaling network into its representation as an influence graph.},
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Aymeric Ferreira; Vlad-Stefan Constantinescu; Sarah Malvaut; Armen Saghatelyan; Simon V Hardy
In: Commun Biol, vol. 7, no. 1, pp. 420, 2024, ISSN: 2399-3642.
@article{pmid38582915,
title = {Distinct forms of structural plasticity of adult-born interneuron spines in the mouse olfactory bulb induced by different odor learning paradigms},
author = {Aymeric Ferreira and Vlad-Stefan Constantinescu and Sarah Malvaut and Armen Saghatelyan and Simon V Hardy},
doi = {10.1038/s42003-024-06115-7},
issn = {2399-3642},
year = {2024},
date = {2024-04-01},
journal = {Commun Biol},
volume = {7},
number = {1},
pages = {420},
abstract = {The morpho-functional properties of neural networks constantly adapt in response to environmental stimuli. The olfactory bulb is particularly prone to constant reshaping of neural networks because of ongoing neurogenesis. It remains unclear whether the complexity of distinct odor-induced learning paradigms and sensory stimulation induces different forms of structural plasticity. In the present study, we automatically reconstructed spines in 3D from confocal images and performed unsupervised clustering based on morphometric features. We show that while sensory deprivation decreased the spine density of adult-born neurons without affecting the morphometric properties of these spines, simple and complex odor learning paradigms triggered distinct forms of structural plasticity. A simple odor learning task affected the morphometric properties of the spines, whereas a complex odor learning task induced changes in spine density. Our work reveals distinct forms of structural plasticity in the olfactory bulb tailored to the complexity of odor-learning paradigms and sensory inputs.},
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Aymeric Ferreira; Cedric Bressan; Simon V Hardy; Armen Saghatelyan
Deciphering heterogeneous populations of migrating cells based on the computational assessment of their dynamic properties Journal Article
In: Stem Cell Reports, vol. 17, no. 4, pp. 911–923, 2022, ISSN: 2213-6711.
@article{pmid35303437,
title = {Deciphering heterogeneous populations of migrating cells based on the computational assessment of their dynamic properties},
author = {Aymeric Ferreira and Cedric Bressan and Simon V Hardy and Armen Saghatelyan},
doi = {10.1016/j.stemcr.2022.02.011},
issn = {2213-6711},
year = {2022},
date = {2022-04-01},
journal = {Stem Cell Reports},
volume = {17},
number = {4},
pages = {911--923},
abstract = {Neuronal migration is a highly dynamic process, and multiple cell movement metrics can be extracted from time-lapse imaging datasets. However, these parameters alone are often insufficient to evaluate the heterogeneity of neuroblast populations. We developed an analytical pipeline based on reducing the dimensions of the dataset by principal component analysis (PCA) and determining sub-populations using k-means, supported by the elbow criterion method and validated by a decision tree algorithm. We showed that neuroblasts derived from the same adult neural stem cell (NSC) lineage as well as across different lineages are heterogeneous and can be sub-divided into different clusters based on their dynamic properties. Interestingly, we also observed overlapping clusters for neuroblasts derived from different NSC lineages. We further showed that genetic perturbations or environmental stimuli affect the migratory properties of neuroblasts in a sub-cluster-specific manner. Our data thus provide a framework for assessing the heterogeneity of migrating neuroblasts.},
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Michel Lavoie; Blanche Saint-Béat; Jan Strauss; Sébastien Guérin; Antoine Allard; Simon V Hardy; Angela Falciatore; Johann Lavaud
Genome-Scale Metabolic Reconstruction and Perturbation Analysis of the Polar Diatom Predicts High Metabolic Robustness Journal Article
In: Biology (Basel), vol. 9, no. 2, 2020, ISSN: 2079-7737.
@article{pmid32079178,
title = {Genome-Scale Metabolic Reconstruction and Perturbation Analysis of the Polar Diatom Predicts High Metabolic Robustness},
author = {Michel Lavoie and Blanche Saint-Béat and Jan Strauss and Sébastien Guérin and Antoine Allard and Simon V Hardy and Angela Falciatore and Johann Lavaud},
doi = {10.3390/biology9020030},
issn = {2079-7737},
year = {2020},
date = {2020-02-01},
journal = {Biology (Basel)},
volume = {9},
number = {2},
abstract = {Diatoms are major primary producers in polar environments where they can actively grow under extremely variable conditions. Integrative modeling using a genome-scale model (GSM) is a powerful approach to decipher the complex interactions between components of diatom metabolism and can provide insights into metabolic mechanisms underlying their evolutionary success in polar ecosystems. We developed the first GSM for a polar diatom, , which enabled us to study its metabolic robustness using sensitivity analysis. We find that the predicted growth rate was robust to changes in all model parameters (i.e., cell biochemical composition) except the carbon uptake rate. Constraints on total cellular carbon buffer the effect of changes in the input parameters on reaction fluxes and growth rate. We also show that single reaction deletion of 20% to 32% of active (nonzero flux) reactions and single gene deletion of 44% to 55% of genes associated with active reactions affected the growth rate, as well as the production fluxes of total protein, lipid, carbohydrate, DNA, RNA, and pigments by less than 1%, which was due to the activation of compensatory reactions (e.g., analogous enzymes and alternative pathways) with more highly connected metabolites involved in the reactions that were robust to deletion. Interestingly, including highly divergent alleles unique for increased its metabolic robustness to cellular perturbations even more. Overall, our results underscore the high robustness of metabolism in , a feature that likely helps to maintain cell homeostasis under polar conditions.},
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Evren U Azeloglu; Simon V Hardy; Narat John Eungdamrong; Yibang Chen; Gomathi Jayaraman; Peter Y Chuang; Wei Fang; Huabao Xiong; Susana R Neves; Mohit R Jain; Hong Li; Avi Ma’ayan; Ronald E Gordon; John Cijiang He; Ravi Iyengar
Interconnected network motifs control podocyte morphology and kidney function Journal Article
In: Sci Signal, vol. 7, no. 311, pp. ra12, 2014, ISSN: 1937-9145.
@article{pmid24497609,
title = {Interconnected network motifs control podocyte morphology and kidney function},
author = {Evren U Azeloglu and Simon V Hardy and Narat John Eungdamrong and Yibang Chen and Gomathi Jayaraman and Peter Y Chuang and Wei Fang and Huabao Xiong and Susana R Neves and Mohit R Jain and Hong Li and Avi Ma'ayan and Ronald E Gordon and John Cijiang He and Ravi Iyengar},
doi = {10.1126/scisignal.2004621},
issn = {1937-9145},
year = {2014},
date = {2014-02-01},
journal = {Sci Signal},
volume = {7},
number = {311},
pages = {ra12},
abstract = {Podocytes are kidney cells with specialized morphology that is required for glomerular filtration. Diseases, such as diabetes, or drug exposure that causes disruption of the podocyte foot process morphology results in kidney pathophysiology. Proteomic analysis of glomeruli isolated from rats with puromycin-induced kidney disease and control rats indicated that protein kinase A (PKA), which is activated by adenosine 3',5'-monophosphate (cAMP), is a key regulator of podocyte morphology and function. In podocytes, cAMP signaling activates cAMP response element-binding protein (CREB) to enhance expression of the gene encoding a differentiation marker, synaptopodin, a protein that associates with actin and promotes its bundling. We constructed and experimentally verified a β-adrenergic receptor-driven network with multiple feedback and feedforward motifs that controls CREB activity. To determine how the motifs interacted to regulate gene expression, we mapped multicompartment dynamical models, including information about protein subcellular localization, onto the network topology using Petri net formalisms. These computational analyses indicated that the juxtaposition of multiple feedback and feedforward motifs enabled the prolonged CREB activation necessary for synaptopodin expression and actin bundling. Drug-induced modulation of these motifs in diseased rats led to recovery of normal morphology and physiological function in vivo. Thus, analysis of regulatory motifs using network dynamics can provide insights into pathophysiology that enable predictions for drug intervention strategies to treat kidney disease.},
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