Data-driven methods for biological modeling in industry
Wednesday, June 16 at 09:30am (PDT)Wednesday, June 16 at 05:30pm (BST)Thursday, June 17 01:30am (KST)
Kevin Flores (North Carolina State University, USA)
This minisymposium highlights recent advances in data-driven mathematical modeling for biology in industry, including parameter estimation, uncertainty quantification, machine learning, and image analysis. In particular, the emphasis is on the development of methods for overcoming practical challenges encountered with real-world data from industrial applications, such as high levels of observation error, model bias, and intra- as well as inter-individual or experimental heterogeneity. Topics include optimization of clinical dose regimens, optimal sample collection, forecasting, hypothesis testing and/or model selection, and the integration of heterogeneous sources of data from multiple scales and data acquisition platforms.