Physiology: Quantitative and Computational Physiology (Ph.D.)
Offered by: Physiology (Faculty of Medicine and Health Sciences)
Degree: Doctor of Philosophy
Program Description
The Ph.D. in Physiology; Quantitative and Computational Physiology focuses on the concepts, language, approaches, and limitations of quantitative and computational physiology. The thesis must focus on quantitative and computational physiology.
Required Courses (14 credits)
| Course | Title | Credits |
|---|---|---|
| PHGY 604 | Responsible Conduct in Research. | 0 |
Responsible Conduct in Research. Terms offered: Fall 2026 This course provides students with information on the following areas: 1) an ethics overview; 2) scientific conduct and misconduct; 3) research authorship and peer review; and 4) research on human and animal subjects. | ||
| PHGY 701 | Ph.D. Comprehensive Examination. | 0 |
Ph.D. Comprehensive Examination. Terms offered: this course is not currently offered. The Ph.D. comprehensive exam will be completed between 12-18 months of commencing the program and is designed to ensure that the student's research encompasses the i) acquisition of a comprehensive knowledge of scientific literature; ii) the development of experimental skills and technical expertise with a deep understanding of the experimental design thus iii) ensuring a high degree of scholarship in the thesis submission. | ||
| PHGY 703 | Ph.D. Progress Seminar 1. | 1 |
Ph.D. Progress Seminar 1. Terms offered: Fall 2026 The Progress Seminar is a "work in progress" seminar on what the student has accomplished to date. Following completion of the comprehensive exam, the seminar should be presented to the student's supervisory committee as a formal presentation of approximately 30 minutes followed by a question and discussion period. | ||
| PHGY 704 | Ph.D. Progress Seminar 2. | 1 |
Ph.D. Progress Seminar 2. Terms offered: this course is not currently offered. A "work in progress" seminar, intended as a report on student progress following the Thesis Proposal Seminar. | ||
| PHGY 720 | Ph.D. Seminar Course 1. | 1 |
Ph.D. Seminar Course 1. Terms offered: Fall 2026 Required for Ph.D. students. Coordinated in conjunction with the weekly Departmental seminar series, students will meet for one hour before each seminar to critically discuss papers on the subject of the weekly seminar. Students will take turns introducing the papers and leading discussions on an overview of the research topic, some of the methodologies, results and conclusions. | ||
| PHGY 721 | Ph.D. Seminar Course 2. | 1 |
Ph.D. Seminar Course 2. Terms offered: Fall 2026, Winter 2027 Required for Ph.D. students. Coordinated in conjunction with the weekly Departmental seminar series, students will meet for one hour before each seminar to critically discuss papers on the subject of the weekly seminar. Students will take turns introducing the papers and leading discussions on an overview of the research topic, some of the methodologies, results and conclusions. | ||
| PHGY 722 | Ph.D. Seminar Course 3. | 1 |
Ph.D. Seminar Course 3. Terms offered: Fall 2026, Winter 2027 Required for Ph.D. students. Coordinated in conjunction with the weekly Departmental seminar series, students will meet for one hour before each seminar to critically discuss papers on the subject of the weekly seminar. Students will take turns introducing the papers and leading discussions on an overview of the research topic, some of the methodologies, results and conclusions. | ||
| PHGY 723 | Ph.D. Seminar Course 4. | 1 |
Ph.D. Seminar Course 4. Terms offered: Fall 2026, Winter 2027 Required for Ph.D. students. Coordinated in conjunction with the weekly Departmental seminar series, students will meet for one hour before each seminar to critically discuss papers on the subject of the weekly seminar. Students will take turns introducing the papers and leading discussions on an overview of the research topic, some of the methodologies, results and conclusions. | ||
| PHGY 724 | Ph.D. Seminar Course 5. | 1 |
Ph.D. Seminar Course 5. Terms offered: Fall 2026, Winter 2027 Required for Ph.D. students. Coordinated in conjunction with the weekly Departmental seminar series, students will meet for one hour before each seminar to critically discuss papers on the subject of the weekly seminar. Students will take turns introducing the papers and leading discussions on an overview of the research topic, some of the methodologies, results and conclusions. | ||
| PHGY 725 | Ph.D. Seminar Course 6. | 1 |
Ph.D. Seminar Course 6. Terms offered: Fall 2026, Winter 2027 Required for Ph.D. students. Coordinated in conjunction with the weekly Departmental seminar series, students will meet for one hour before each seminar to critically discuss papers on the subject of the weekly seminar. Students will take turns introducing the papers and leading discussions on an overview of the research topic, some of the methodologies, results and conclusions. | ||
| QLSC 600D1 | Foundations of Quantitative Life Sciences. | 3 |
Foundations of Quantitative Life Sciences. Terms offered: Fall 2026 Provides an overview of important problems in the life sciences and introduces students to the latest computational, mathematical, and statistical approaches involved in their solution. Includes a survey of modern technologies for biological data acquisition and promotes a common language to communicate across the biological, physical, mathematical, and computational sciences. Topics will include bioinformatics and computational genomics, nonlinear dynamics in biological systems, linear and nonlinear models of biological signals, biophysical imaging technology, emergent behaviour in biophysical networks, and ecosystem dynamics and modeling. | ||
| QLSC 600D2 | Foundations of Quantitative Life Sciences. | 3 |
Foundations of Quantitative Life Sciences. Terms offered: Winter 2027 Provides an overview of important problems in the life sciences and introduces students to the latest computational, mathematical, and statistical approaches involved in their solution. Includes a survey of modern technologies for biological data acquisition and promotes a common language to communicate across the biological, physical, mathematical, and computational sciences. Topics will include bioinformatics and computational genomics, nonlinear dynamics in biological systems, linear and nonlinear models of biological signals, biophysical imaging technology, emergent behaviour in biophysical networks, and ecosystem dynamics and modeling. | ||
Complementary Courses (3-4 credits)
| Course | Title | Credits |
|---|---|---|
| BMDE 502 | BME Modelling and Identification. | 3 |
BME Modelling and Identification. Terms offered: Winter 2027 Methodologies in systems or distributed multidimensional processes. System themes include parametric vs. non-parametric system representations; linear/non-linear; noise, transients and time variation; mapping from continuous to discrete models; and relevant identification approaches in continuous and discrete time formulations. | ||
| BMDE 519 | Biomedical Signals and Systems. | 3 |
Biomedical Signals and Systems. Terms offered: Fall 2026 An introduction to the theoretical framework, experimental techniques and analysis procedures available for the quantitative analysis of physiological systems and signals. Lectures plus laboratory work using the Biomedical Engineering computer system. Topics include: amplitude and frequency structure of signals, filtering, sampling, correlation functions, time and frequency-domain descriptions of systems. | ||
| COMP 551 | Applied Machine Learning. | 4 |
Applied Machine Learning. Terms offered: Fall 2026, Winter 2027 Selected topics in machine learning and data mining, including clustering, neural networks, support vector machines, decision trees. Methods include feature selection and dimensionality reduction, error estimation and empirical validation, algorithm design and parallelization, and handling of large data sets. Emphasis on good methods and practices for deployment of real systems. | ||
| COMP 552 | Combinatorial Optimization. | 4 |
Combinatorial Optimization. Terms offered: this course is not currently offered. Algorithmic and structural approaches in combinatorial optimization with a focus upon theory and applications. Topics include: polyhedral methods, network optimization, the ellipsoid method, graph algorithms, matroid theory and submodular functions. | ||
| COMP 558 | Fundamentals of Computer Vision. | 4 |
Fundamentals of Computer Vision. Terms offered: Fall 2026 Image filtering, edge detection, image features and histograms, image segmentation, image motion and tracking, projective geometry, camera calibration, homographies, epipolar geometry and stereo, point clouds and 3D registration. Applications in computer graphics and robotics. | ||
| COMP 564 | Advanced Computational Biology Methods and Research. | 3 |
Advanced Computational Biology Methods and Research. Terms offered: this course is not currently offered. Fundamental concepts and techniques in computational structural biology, system biology. Techniques include dynamic programming algorithms for RNA structure analysis, molecular dynamics and machine learning techniques for protein structure prediction, and graphical models for gene regulatory and protein-protein interaction networks analysis. Practical sessions with state-of-the-art software. | ||
| COMP 680 | Mining Biological Sequences. | 4 |
Mining Biological Sequences. Terms offered: this course is not currently offered. Advanced algorithms for the annotation of biological sequences. Algorithms and heuristics for pair-wise and multiple sequence alignment. Gene-finding with hidden Markov models and variants. Motifs discovery techniques: over representation and phylogenetic footprinting approaches. RNA secondary structure prediction. Detection of repetitive elements. Representation and annotation of protein domains. | ||
| ECSE 509 | Probability and Random Signals 2. | 3 |
Probability and Random Signals 2. Terms offered: Fall 2026 Multivariate Gaussian distributions; finite-dimensional mean-square estimation (multivariate case); principal components; introduction to random processes; weak stationarity: correlation functions, spectra, linear processing and estimation; Poisson processes and Markov chains: state processes, invariant distributions; stochastic simulation. | ||
| ECSE 512 | Digital Signal Processing 1. | 3 |
Digital Signal Processing 1. Terms offered: Fall 2026 Review of discrete-time transforms, sampling and quantization, frequency analysis. Structures for IIR and FIR filters, coefficient quantization, roundoff noise. The DFT, its properties, frequency analysis and filtering using DFT methods, the FFT and its implementation. Multirate processing, subsampling and interpolation, oversampling techniques. | ||
| ECSE 626 | Statistical Computer Vision. | 4 |
Statistical Computer Vision. Terms offered: Fall 2026 An overview of statistical and machine learning techniques as applied to computer vision problems, including: stereo vision, motion estimation, object and face recognition, image registration and segmentation. Topics include regularization, probabilistic inference, information theory, Gaussian Mixture Models, Markov-Chain Monte Carlo methods, importance sampling, Markov random fields, principal and independent components analysis, probabilistic deep learning methods including variational models, Bayesian deep learning. | ||
| MATH 545 | Introduction to Time Series Analysis. | 4 |
Introduction to Time Series Analysis. Terms offered: Winter 2027 Stationary processes; estimation and forecasting of ARMA models; non-stationary and seasonal models; state-space models; financial time series models; multivariate time series models; introduction to spectral analysis; long memory models. | ||
| MATH 547 | Stochastic Processes. | 4 |
Stochastic Processes. Terms offered: Winter 2027 Conditional probability and conditional expectation, generating functions. Branching processes and random walk. Markov chains:transition matrices, classification of states, ergodic theorem, examples. Birth and death processes, queueing theory. | ||
| MATH 680 | Computation Intensive Statistics. | 4 |
Computation Intensive Statistics. Terms offered: Fall 2026 General introduction to computational methods in statistics; optimization methods; EM algorithm; random number generation and simulations; bootstrap, jackknife, cross-validation, resampling and permutation; Monte Carlo methods: Markov chain Monte Carlo and sequential Monte Carlo; computation in the R language. | ||
| NEUR 503 | Computational Neuroscience. | 3 |
Computational Neuroscience. Terms offered: Winter 2027 A survey of computational methods commonly used to model brain function, including mathematical modeling to describe the relationship between neuronal activity and perception, action, and cognition. Mathematical basis for vision, motor control and attention. Data relevant to brain processes and models explaining these data, using engineering, statistics and artificial intelligence. | ||
| PHYS 559 | Advanced Statistical Mechanics. | 3 |
Advanced Statistical Mechanics. Terms offered: Winter 2027 Scattering and structure factors. Review of thermodynamics and statistical mechanics; correlation functions (static); mean field theory; critical phenomena; broken symmetry; fluctuations, roughening. | ||