Abstract: Biological systems are complex dynamic networks. In this talk, I will introduce GOBI (General Model-based Inference), a simple and scalable method for inferring regulatory networks from time-series data. GOBI can infer gene regulatory networks and ecological networks that cannot be obtained with previous causation detection methods (e.g., Granger, CCM, PCM). I will then introduce Density-PINN (Physics-Informed NeuralNetwork), a method for inferring the shape of the time-delay distribution of interactions in a network. The inferred shape of time-delay distribution can be used to identify the number of pathways that induce a signaling response against antibiotics, which solves the long-standing mystery, the major source of cell-to-cell heterogeneity in response to stress. Finally, I will talk how to infer the dynamic information from just network structure information, which can be used to identify the targets (nodes) perturbing the homeostasis of the systems.

[1] Jo H, Hong H, Hwang HJ, Chang W, Kim JK, Density Physics-Informed Neural Network identifies sources of cell heterogeneity in signal transduction under antibiotic stress, Cell Patterns (2024)

[2] Park SH, Ha S, Kim JK, A general model-based causal inference overcomes the curse of synchrony and indirect effect, Nature Communications (2023)

[3] Hirono Y, Moon SH, Hong H, Kim JK, Robust Perfect Adaptation of Reaction Fluxes Ensured by Network Topology, Arxiv (2023)

Event will be in-person and on Zoom: https://umich.zoom.us/j/98734707290