Beijing Institute of Mathematical Sciences and Applications

Ruize Gao , Liyan Han , Zhen Li , Fei Long , Dongbo Shi , Ke Tang , Qi Zhang

Ang Dong

Friday, October 17, 2025 3:00 PM - 4:00 PM

A3-2-303

Zoom 435 529 7909 (BIMSA)

Networks are fundamental to understanding complex systems, characterized by many underlying entities and their intricate interactions. We contextualize evolutionary game theory and community ecology theory to explain how the dynamic change of an entity is determined by its own strategy and the strategies of its interacting counterparts. We derive a system of nonlinear mixed ordinary differential equations (nMODEs) to quantify the contributions of these two types of strategies and encode them into informative, dynamic, omnidirectional, and personalized networks (idopNetworks). We implement multi-task learning (MTL) into the matrix representation of linearized nMODEs to jointly choose a subset of the most significant entities (acting as predictors) for all entities each viewed as a response. In going beyond existing networking practice, idopNetworks can capture all-around interacting links, nonlinearities, and emergent properties of a complex system, which, to a larger extent, approximate the intricate and multifaceted nature of complex systems. We apply our model to learn gene regulatory idopNetworks from transcriptional data, identifying previously-unknown regulatory roles of several genes in mediating malaria infection. We perform computer simulation to validate the statistical relevance of the model. Our model provides a new insight of machine learning to analyze, model, and interpret complex data in a non-Euclidean space.

Dr.Dong received his education in Biotechnology and Silviculture at Zhejiang A&F University in 2018, then earned his Ph.D. at Beijing Forestry University in 2023. He is broadly interested in the role that network theory plays in paradigm shift in the life sciences, his current research lies at the area of statistical modelling and computer programming. He is working on how to fully utilize existing data to build a network which can analysis the detailed regulatory relationship between genes during the complicated biological process and how to make this idea come true. He also uses computer simulation that allows exploration and verification of design model parameters in multiple scenarios, and helps to further improve and understand the model as well as the complex systems.