Explore network science and dynamic modeling techniques for understanding emergent properties of biological systems in this 36-minute lecture. Dive into the concept of cell types as attractors in a dynamical system of interacting molecules, and discover network patterns determining these attractors. Learn about network-based discrete dynamic modeling for synthesizing causal interaction information into predictive, mechanistic models. Examine the connection between network structure and dynamics, focusing on stable motifs and their role in system decision-making. Investigate how controlling stable motifs can guide biological systems into desired attractors, with potential applications in therapeutic strategies. Gain insights into the pystablemotifs software library for efficient Boolean system attractor analysis and control. Explore real-world examples, including oncogenic signaling, cell cycle phase switches, epithelial-to-mesenchymal transition, and C. albicans yeast-to-hyphal transition prevention. Read more