This track focuses on the development and application of mathematical models to understand complex biological systems. Emphasis will be placed on modeling techniques that elucidate dynamics in cellular processes and population dynamics.

This session will explore advanced statistical techniques used in the analysis of biological data. Topics will include hypothesis testing, regression models, and multivariate analysis in the context of genomics and proteomics.

This track addresses computational approaches for modeling and analyzing genetic networks. Participants will discuss algorithms and tools for network reconstruction, inference, and visualization.

This session will highlight the integration of machine learning techniques in solving biological problems. Case studies will demonstrate the application of algorithms for predictive modeling and data classification.

This track will cover optimization methods applied to biological systems, including parameter estimation and model selection. Discussions will include the challenges and solutions in optimizing biological models for better predictive accuracy.

This session will focus on simulation techniques used to study biological phenomena. Participants will explore discrete and continuous simulation methods for modeling biological interactions and processes.