Toward AI-Driven Digital Organisms: Multiscale Foundation Models for Predicting, Simulating, and Programming Biology at All Levels

Overview

Dive into Eric Xing's exploration of AI-driven digital organisms, foundation models for predicting and simulating biology at all scales, and the future of programming biological systems.

Syllabus

• Introduction to AI-Driven Digital Organisms
Overview of Digital Organisms
Historical Context and Advances in AI and Biology
Key Concepts: Multiscale Modeling and Foundation Models
• AI Foundation Models in Biology
Definition and Types of Foundation Models
Application of Foundation Models in Biological Research
Case Studies and Current Research
• Predictive Models for Biological Systems
Introduction to Predictive Modeling in Biology
Techniques for Modeling Complex Biological Processes
Challenges and Opportunities
• Simulation of Biological Systems
Tools and Techniques for Simulating Biological Processes
Examples of Biological Simulations at Different Scales
Limitations and Future Directions
• Programming Biological Systems
Overview of Synthetic Biology and Bioengineering
Programming Languages and Frameworks for Biology
Ethical Considerations in Programming Life
• Multiscale Modeling Techniques
Introduction to Multiscale Modeling
Integrating Data Across Biological Scales
Case Studies: From Molecular to Organismal Level
• Future of AI in Biological Systems
Emerging Trends and Future Perspectives
The Role of AI in Personalized Medicine and Biotechnology
Potential Impacts on Society and the Environment
• Practical Workshops
Hands-On Sessions with Biological Data Sets
Experimentation with Simulation and Prediction Tools
Project-Based Learning: Design a Digital Organism
• Final Project
Develop a Proposal for an AI-Driven Model or Simulation
Presentations and Peer Review
Feedback and Evaluation
• Conclusion and Course Wrap-Up
Key Takeaways and Lessons Learned
Discussion on Future Research Directions
Closing Remarks and Networking Opportunities

Subjects

Computer Science