Mathematical Biology seminar Niall Mangan Northwestern University, NITMB "Data-driven model discovery meets mechanistic modeling for biological systems" Thursday (Special Day), February 19 1-2pm in LCB 219 (Special Place) Abstract: Building models for biological systems has traditionally relied on domain-specific intuition about which interactions and features most strongly influence a system. We have collaborated with experimentalists to build systems models that can explain and guide experiment. 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. I will discuss my group’s development and application of data-driven methods for model selection to 1) discover models for metabolic and temperature regulation in hibernating mammals, and 2) challenges in discovering models for regulatory systems with typical experimental time-series data.