Physiology (Thesis): Quantitative and Computational Physiology (M.Sc.) (45 credits)
Offered by: Physiology (Faculty of Medicine and Health Sciences)
Degree: Master of Science
Program credit weight: 45
Program Description
The M.Sc. 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 (42 credits)
| Course | Title | Credits |
|---|---|---|
| PHGY 601 | M.Sc. Proposal Seminar. | 1 |
M.Sc. Proposal Seminar. Terms offered: Fall 2026, Winter 2027 Seminar presentation to Supervisory Committee and students on the Master's thesis proposal. | ||
| PHGY 602 | Literature Search and Research Proposal. | 2 |
Literature Search and Research Proposal. Terms offered: Fall 2026 Independent work under the supervision of the thesis advisor including literature search and research leading to thesis proposal. | ||
| 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 607 | Laboratory Research 1. | 3 |
Laboratory Research 1. Terms offered: this course is not currently offered. Laboratory research leading to the thesis. | ||
| PHGY 608 | Laboratory Research 2. | 3 |
Laboratory Research 2. Terms offered: Fall 2026 Laboratory research leading to the thesis. | ||
| PHGY 620 | Progress in Research. | 3 |
Progress in Research. Terms offered: Fall 2026 Progress in research in preparation of thesis. | ||
| PHGY 621 | Thesis 1. | 12 |
Thesis 1. Terms offered: Fall 2026 Written and oral presentation of thesis proposal to the research Supervisory Committee. | ||
| PHGY 623 | M.Sc. Final Seminar. | 3 |
M.Sc. Final Seminar. Terms offered: this course is not currently offered. Final seminar presentation to students supervisory committee prior to thesis submission. | ||
| PHGY 690 | Thesis Research | 9 |
Thesis Research Terms offered: this course is not currently offered. Thesis preparation. | ||
| 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 520 | Information Theory and Coding. | 3 |
Information Theory and Coding. Terms offered: this course is not currently offered. Information sources and channel models. Source coding, data compression, and performance limits. Channel coding and performance limits. Multiple-Input Multiple- Output channels. An introduction to multi-user information theory. | ||
| 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. | ||