Lectures:
1. System definition, basic division, basic concepts, feedback, system description, mathematical apparatus.
2. Modeling biological systems, model building procedure, Models of static systems, models of dynamic systems, stochastic models of biological systems, basic attributes of systems.
3. Human organism as a system - basic properties of the organism, principles of homeostatic control, control mechanisms, stimuli, structure of biological system, receptors, homeostatic regulation, thermoregulation
4. Neurophysiology - nervous system. Information transmission and processing in biological systems. Nervous, hormonal and humoral levels of their control.
5. Heart. Cardiac activity and the importance of regulatory mechanisms during stress. The vascular system. Importance of the vasculature for circulation, possibilities and importance of its regulation.
6. Regulation of heart rate, Stabilization of blood pressure
7. Respiratory system. Control of the respiratory system. Lung function and its regulation in extreme conditions. Regulation of breathing
8. Water regulation, glycaemic control, pharmacokinetics
9. Introduction to machine learning: defining the areas of supervised and unsupervised learning.
10. Principles of machine learning system design, tuning, data annotation and hyperparameters of learning.
11. Methods for objective evaluation of machine learning performance: accuracy parameters and loss functions.
12.Examples of using machine learning methods for signal content recognition: signal detection and classification methods.
13. Examples of using machine learning methods for biological image analysis: semantic segmentation and convolutional neural networks.
14. Unsupervised learning methods for biological signal and image processing.
Laboratory exercises:
1. Introduction to the theory of ordinary differential equations: first and second order ODRs and their systems, general and partial solutions, Cauchy problem and Laplace transform.
2. Solution of ordinary differential equations in Simulink. Numerical solvers of differential equations in MATLAB.
3. Analytical solution and simulation of population models.
4. Pharmacokinetics: one-compartment and two-compartment drug passage model.
5. Analysis and simulation of heart rate dependence on exercise.
6. Modeling of renal function during blood pressure stabilization.
7. Pulmonary compartment: model of gas concentration in alveoli and other tissues.
8. Model of gastric acidity regulation.
9. Machine learning model preparation: model design, hyperparameter setting, annotated data generation.
10. Design of selected machine learning models for general examples of significant event detection, classification and regression.
11. Methods for detecting significant events from biological signals using machine learning.
12. Methods for classification of biological signals using machine learning.
13. Semantic segmentation methods for automatic object detection from medical images.
14. Convolutional neural networks for medical image classification.
1. System definition, basic division, basic concepts, feedback, system description, mathematical apparatus.
2. Modeling biological systems, model building procedure, Models of static systems, models of dynamic systems, stochastic models of biological systems, basic attributes of systems.
3. Human organism as a system - basic properties of the organism, principles of homeostatic control, control mechanisms, stimuli, structure of biological system, receptors, homeostatic regulation, thermoregulation
4. Neurophysiology - nervous system. Information transmission and processing in biological systems. Nervous, hormonal and humoral levels of their control.
5. Heart. Cardiac activity and the importance of regulatory mechanisms during stress. The vascular system. Importance of the vasculature for circulation, possibilities and importance of its regulation.
6. Regulation of heart rate, Stabilization of blood pressure
7. Respiratory system. Control of the respiratory system. Lung function and its regulation in extreme conditions. Regulation of breathing
8. Water regulation, glycaemic control, pharmacokinetics
9. Introduction to machine learning: defining the areas of supervised and unsupervised learning.
10. Principles of machine learning system design, tuning, data annotation and hyperparameters of learning.
11. Methods for objective evaluation of machine learning performance: accuracy parameters and loss functions.
12.Examples of using machine learning methods for signal content recognition: signal detection and classification methods.
13. Examples of using machine learning methods for biological image analysis: semantic segmentation and convolutional neural networks.
14. Unsupervised learning methods for biological signal and image processing.
Laboratory exercises:
1. Introduction to the theory of ordinary differential equations: first and second order ODRs and their systems, general and partial solutions, Cauchy problem and Laplace transform.
2. Solution of ordinary differential equations in Simulink. Numerical solvers of differential equations in MATLAB.
3. Analytical solution and simulation of population models.
4. Pharmacokinetics: one-compartment and two-compartment drug passage model.
5. Analysis and simulation of heart rate dependence on exercise.
6. Modeling of renal function during blood pressure stabilization.
7. Pulmonary compartment: model of gas concentration in alveoli and other tissues.
8. Model of gastric acidity regulation.
9. Machine learning model preparation: model design, hyperparameter setting, annotated data generation.
10. Design of selected machine learning models for general examples of significant event detection, classification and regression.
11. Methods for detecting significant events from biological signals using machine learning.
12. Methods for classification of biological signals using machine learning.
13. Semantic segmentation methods for automatic object detection from medical images.
14. Convolutional neural networks for medical image classification.