Summary of co​urse content

Living organisms constitute of thousands of genes, proteins, and metabolites. These molecules communicate via specialized cellular networks, such as, immune signaling or energy metabolism. To understand the regulatory and response properties of such networks, mathematical, computational, and statistical methods have been recently adopted to model and predict their dynamics. In this course, the student will first learn to conceptualize biochemical reactions, and next using these construct dynamic response models mimicking actual experimental observations.

Aims and objectives

• To introduce and equip students with cross-disciplinary learning in biomedical data analysis
• To provide hands on experience in modelling biological networks in deterministic and stochastic scenarios
• To appreciate both the simplicity and complexity of cellular behaviors

Syllabus

• Basics of biological networks
• Deterministic versus stochastic cellular responses
• Boolean Logic Models
• Linear ODE models
• Non-linear ODE models
• Stochastic models
• COPASI modelling of linear, non-linear and stochastic networks
• Basics of Genetic algorithm (GA)
• Parameter fitting in COPASI using GA
• Application on Toll-like receptor signaling (deterministic)
• Application on transcriptional regulation (stochastic)

Assessment