From data to mechanisms: advancement in modeling in cell and developmental biology

Organizers:

Keisha Cook, Anna Nelson (Clemson University), Alessandra Bonfanti (Politecnico di Milano) Giulia Celora (University of Oxford) Kelsey Gasior (University of Notre Dame) Qixuan Wang (University of California, Riverside)

Description:

In many cell and developmental processes, both modeling and data analytic approaches are necessary in order to generate useful modeling predictions to guide the design of further experiments for both validating and improving biological insight. There is an increased understanding that the application of machine learning methods can also be used to enhance common data-driven modeling techniques, including parameter and equation inference, classification, and sensitivity analysis. The speakers in this session will discuss how differential equation models, stochastic models, and methods from machine learning can be combined to address questions related to cell growth, intracellular transport, cell differentiation, cell migration, and tissue development. The speakers will highlight current research progress and challenges associated with combining modeling and inference approaches in cell and developmental biology.

Diversity Statement:

The goal of this minisymposium is to create a welcoming space for early career mathematicians in cell and developmental biology to meet other participants from diverse backgrounds; gender, race, ethnicity, and socioeconomic status. We will promote open discussions after each talk, where each attendee will contribute their personal insights on these topics. The organizers will ensure an inclusive and supportive environment for participants.