Introduction to Complex Systems in Biology (and Beyond)

Description:

This course offers an overview and basic understanding of complex dynamic phenomena in biological, physical and social systems. The focus is on biological systems but with a comparative perspective that reveals the universal principles underlying complex behavior across different domains.

Students will learn to think about biological systems as complex adaptive systems, where interactions between molecules, cells, or organisms give rise to emergent properties that cannot be understood from studying individual components in isolation.

Course Structure:

Part I: Foundations of Complexity

What makes a system complex?
Networks and connectivity patterns
Dynamical systems in biology
Emergence and self-organization

Part II: Biological Applications

Gene regulatory networks
Metabolic networks
Neural networks and brain dynamics
Ecological networks and food webs
Evolution as a complex process

Part III: Comparative Perspectives

Social systems as complex adaptive systems
Physical systems and universality
Common patterns across domains
Modeling approaches and tools

Practical Components:

The course includes hands-on exercises using:

Network analysis tools
Computational modeling (agent-based models, differential equations)
Data analysis of real biological datasets
Case studies from current research

Assessment Components:

Presentation (40%): Research presentation on a selected complex biological system
Written Exam (60%): Theoretical knowledge and problem-solving

Prerequisites:

Basic biology background (cell biology, genetics, ecology)
Mathematical foundations (calculus, basic statistics)
Some programming experience helpful but not required

Target Audience:

This course is designed for students in:

Biology and life sciences
Bioinformatics and computational biology
Physics with biological interests
Systems science and related fields