Sumários
Final Paper Selection
1 Abril 2026, 13:30 • Rafael Neto Henriques
The final session of this block was dedicated to the definitive selection and approval of the project papers. Groups presented their top choices to the instructors for final vetting to ensure the models were of appropriate complexity and met all project guidelines. Instructors cross-checked selections across all classes to resolve any overlaps and ensure no two groups were working on the same paper. By the end of the class, all groups had their physiological models selected and were cleared to begin the algorithm implementation phase.
Structural Models - Static and Linear Models
1 Abril 2026, 10:00 • Rafael Neto Henriques
Transitioning from data-driven approaches, this theoretical class expanded on the practical application of Structural Models. The session focused on two fundamental classes of structural models: Static Models: Used to evaluate systems in a state of equilibrium or steady-state, where variables do not change over time (i.e., the rates of change are zero). The class explored how these models are crucial for analyzing baseline physiological relationships, such as steady-state pressure-flow dynamics or steady-state enzymatic reactions (e.g., Michaelis-Menten kinetics). Linear Dynamic Models: Described by linear differential equations, these models are utilized when a system's response is directly proportional to its input (following the principle of superposition). The class discussed how linear models often serve as highly practical approximations for more complex, non-linear physiological behavior, particularly when observing a system near its normal operating point. Examples covered in the class included: drug clearance, gas exchange between the alveolus of the lung and the blood capillary, the glucose-insulin minimal model, the Windkessel circulatory model, and leg rotation dynamics.
Final Paper Selection
1 Abril 2026, 08:30 • Rafael Neto Henriques
The final session of this block was dedicated to the definitive selection and approval of the project papers. Groups presented their top choices to the instructors for final vetting to ensure the models were of appropriate complexity and met all project guidelines. Instructors cross-checked selections across all classes to resolve any overlaps and ensure no two groups were working on the same paper
Data models - Input-Output Control Models
31 Março 2026, 11:00 • Rafael Neto Henriques
Concluding the module on Data Models, this theoretical class explored Input-Output Control Models. The session focused on mathematically describing the dynamic relationship between an explicitly manipulated input and a measured physiological output. To illustrate these concepts in practice, the following examples were explored: Pupil Control: An application focusing on the study of a natural physiological control loop, analyzing how the pupil dynamically adjusts to changes in light stimuli. Automated Drug Delivery: Designing the control logic for closed-loop systems like the artificial pancreas, where insulin infusion (the input) must be continuously calculated and adjusted based on real-time glucose monitoring (the output). The class also introduced the following fundamental concepts: impulse response, convolution, deconvolution and reconvolution.