Sumários

Model Implementation and Numerical Troubleshooting

6 Maio 2026, 13:30 • Rafael Neto Henriques

This session continued to support students with the practical implementation of their selected physiological models, focusing on programming challenges and numerical troubleshooting.

Structural Models – Focus on different Stochastic Structural Models

6 Maio 2026, 10:00 • Rafael Neto Henriques

Building on the introduction to Monte-Carlo simulations, this theoretical class delved deeper into Stochastic Structural Models. The session highlighted that stochastic (random) effects can be integrated into physiological models in multiple ways: within their transfer flows, their state variables, or a combination of both. To illustrate these different approaches, the class explored three specific examples:

1) Granulocyte-Macrophage Proliferation and Differentiation: Continuing from the previous session, this model was revisited to demonstrate how it could be mathematically rederived to calculate expected values, providing a contrast to the purely randomized simulation approach.

2) Insulin Control Model: A structural model based on the physiological hypothesis that insulin is stored inside pancreatic beta-cells in discrete packets, which possess a statistical distribution of sensitivities to glucose.

3) Markov Models: An introduction to using probabilistic state-transition models (Markov models) and their practical application in clinical decision-making processes.

Model Implementation and Numerical Troubleshooting

6 Maio 2026, 08:30 • Rafael Neto Henriques

This session continued to support students with the practical implementation of their selected physiological models, focusing on programming challenges and numerical troubleshooting.

Structural Models – Introduction to Monte-Carlo Simulations and stochastic structural models

5 Maio 2026, 11:00 • Rafael Neto Henriques

Before fully transitioning into stochastic structural models, this theoretical class provided a foundational introduction to Monte-Carlo simulations. The session began by explaining the computational generation of pseudorandom numbers across various statistical distributions. Students then explored the core applications of Monte-Carlo methods, specifically focusing on optimization, numerical integration, and sampling random variables based on specific probability density functions. These mathematical and computational principles were highlighted as essential prerequisites for the upcoming development and analysis of stochastic structural models. To bridge these computational techniques with physiological reality, the class concluded with an initial biological application: demonstrating how random variables can be utilized to model Granulocyte-Macrophage proliferation and differentiation.

Project discussion.

5 Maio 2026, 08:30 • Alexandre da Rocha Freire de Andrade

Project discussion.