- To introduce and understand the emergence and development of key concepts in systems biology.
- To introduce fundamental knowledge and diverse approaches for exploring and regulating the operating principles of biological systems.
- To develop integrative thinking and analytical skills for understanding biological systems and to foster new perspectives on living organisms.

None

Research papers in the relevant fields

Lecture Schedule

Lecture by Dr. Jong Kyoung Kim (Week 1-8)
1. Week 1: Deep learning basics
2. Week 2: Deep learning models for DNA sequences
3. Week 3: Deep learning models for Protein sequences
4. Week 4: Deep learning models for RNA sequences
5. Week 5: Deep learning models for single-cell omics
6. Week 6-7: Virtual cells and digital twins for biological systems
7. Week 8: Midterm exam

Lecture by Dr. Jongmin Kim (Week 9-15)
8. (11/3) Modeling basics – chemical kinetics, enzyme kinetics
9. (11/5) Auto-regulation, Feedforward loop
10. (11/10) Robustness – bifunctional components, chemotaxis
11. (11/12) Fold-change detection – chemotaxis, IFFL
12. (11/17) Dynamic compensation
13. (11/19) Optimality – cost-benefit analysis, fitness function
14. (11/24) Neural Network – perceptron, DNA neural network
15. (11/26) Synthetic Biology I – biological oscillators
16. (12/1) Synthetic Biology II – memory circuits, engineered CAR-T cell
17. (12/3) Examples I – insulin-glucose circuit, diabetes
18. (12/8) Examples II – autoimmune disease, aging
19. (12/10) Examples III – inflammation/fibrosis, cancer
20. (12/15, 17) Student presentation
21. (12/22-24) Final term exam

This course is designed to help undergraduate students develop new perspectives on the life sciences by examining key discoveries that have contributed to the establishment of systems biology and by introducing and discussing how these discoveries have transformed the ways in which life scientists understand biological systems.
In the first half of the course, lectures will focus on deep learning methodologies for systems biology. In the second half, the course will introduce research topics and computational approaches related to systems biology modeling.