Identifying Decisions in Biological Systems: Toward Understanding and Control

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.

Intro
My group's research focus: modeling the dynamics of systems of interacting elements
Connect within-cell networks to cell behavior through discrete dynamic modeling
The dynamic model is built from experimental data and is tested on experimental data
A parsimonious and informative modeling approach: discrete dynamics (logical modeling)
Model - experiment cycles involving our group
Integration of the interaction network and of the regulatory functions allows causal insight
Example: gradual commitment in the cell cycle Phase Switch
The concept of stable motif is preserved in continuous dynamical systems
Example: Boolean model of epithelial to mesenchymal transition (EMT)
Example: preventing C. albicans yeast-to-hyphal transition