15 March 2024
Niall Mangan
Department of Engineering Science and Applied Mathematics
Northwestern University
Building models for biological, chemical, and physical systems has traditionally relied on domain-specific intuition about which interactions and features most strongly influence a system. Alternatively, machine-learning methods are adept at finding novel patterns in large data sets and building predictive models but can be challenging to interpret in terms of or integrate with existing knowledge. Our group balances traditional modeling with data-driven methods and optimization to get the best of both worlds. Sparse optimization strategies, recently developed for, and applied to, dynamical systems, can scan and select a subset of terms from a library that best describes data, automatically interfering potential model structures from a broad but well-defined class. I will discuss my group's application and development of data-driven methods for model selection to 1) recover chaotic systems models from data with hidden variables and 2) discover models for metabolic and temperature regulation in hibernating mammals. I'll briefly discuss current preliminary work and roadblocks in developing new methods for model selection of biological metabolic and regulatory networks.