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

Overview

Explore the forefront of AI innovation with Eric Xing as he delves into the realm of digital organisms driven by artificial intelligence. Discover comprehensive foundation models designed for predicting and simulating biological processes across all scales.

Learn about the promising future of programming and manipulating biological systems, offering groundbreaking insights in the fields of Artificial Intelligence and Computer Science.

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

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Subjects

Computer Science