AI for simulation of biological systems

AI systems that can describe and predict behavior of biological networks of cells, tissues and organisms will allow more accurate, faster and less expensive innovations in life sciences while at the same time ensuring predictable outcomes. In this project, multiple collaborators will combine machine learning and mechanistic modelling for the development of biological systems simulation and link these simulations with learning algorithms for testing and optimizing different designs. The University of Ottawa will focus on the metabolic modelling of quantitative lipidomics data to infer enzymatic disruptions in the lipidome. Queen's University will explore machine learning implementation for cell behaviour prediction and knowledge discovery. Carleton University will apply probabilistic generative model and Bayesian inference for automatic quantification of metabolic data. NRC experts will explore AI for design and simulation of biomolecules and exosomes.

Project team

Dr. Ting Hu

Dr. Ting Hu is an assistant professor in the School of Computing at Queen's University and is an expert in the application of machine learning in metabolomics analysis. Her research interest lies in developing explainable and interpretable machine-learning algorithms for biomedicine.

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Dr. James Green

Dr. James Green is a professor at Carleton University with expertise in computational genomics and proteomics. His research interests are in bioinformatics, prediction of protein structure, function, interaction, and post-translational modification.

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Dr. Dave Campbell

Dr. Dave Campbell is a full professor at Carleton University. His research relates to model relaxations, Bayesian sampling algorithms, and parameter estimation for models with complex likelihood features.

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Dr. Steffany Bennet

Dr. Steffany Bennet is a Canada Research Chair in neurolipidomics in the Faculty of Medicine, University of Ottawa. She researches new Alzheimer therapies to improve patient outcomes.  Dr. Bennet leads the Neural Regeneration Laboratory where researchers are working to understand and treat diseases involving defective lipid metabolism such as Alzheimer's disease, Parkinson's disease, epilepsy, and rare pediatric diseases.

Find out more about Dr. Bennet and her research lab.

Dr. Miroslava Cuperlovic-Culf

Dr. Miroslava Cuperlovic-Culf is a senior research officer and team lead at the National Research Council of Canada. Her research interest lies in the application of machine learning and data mining to life sciences with particular focus on the development of novel diagnostic and treatment methods and simulation methods for in silico medicine. Her unique training in both experimental and data sciences for molecular and high throughput data analysis allows her to work very productively with both experimentalists and clinicians, computer scientists and mathematicians.

Find out more about Dr. Miroslava Cuperlovic-Culf.


Highly qualified personnel (HQP) biographies

Chengyuan Sha

Is currently transiting from an undergraduate student at Queen's School of Computing to a Master of Science student for the AI for Design project. He will graduate from an interdisciplinary program, biomedical computing from Queen's University. He has training experiences in machine learning methodology design, software development, and applications in metabolomics.

Roy Wang

Is a postdoctoral fellow at Carleton University. He has a Bachelor's degree in Biomedical and Electrical Engineering from McMaster University, a Master's degree in Engineering Physics from McMaster University, and a PhD in Electrical Engineering from the University of Ottawa. He has hands-on experience designing optical imaging systems as well as applied math topics such as numerical optimization and Bayesian inference. Dr. Wang took on a few internships with electrical and optical engineering firms during his studies, and was employed as a machine learning researcher at a start-up after finishing school in 2018. He left the private sector in 2019 to work towards a research-intensive career on applying math and physics for biological or environmental applications.