Heber Rocha, John Metzcar, Paul Macklin
The integration of data, such as multiplexed -omics data, histopathology slides, or cell viability and motility assays, into multiscale simulations is a complex and challenging problem. The available data is often high-dimensional, noisy, sparse, and variable in both time and space. On the modeling side, there are many parameters to consider, and numerous possible emergent outcomes may arise. The first step in this process is to map the data to model parameters. This is often a difficult task due to the complexity of the data and the large number of parameters that must be considered. Once the data has been mapped to the model parameters, it is crucial to confirm that the model can reproduce the observations with appropriate fidelity. This step is critical to ensuring that the model is accurate and reliable. In this mini-symposium, we explore new and emerging techniques to address these challenges. We examine the integration of qualitative and quantitative data at each level of the modeling process. Additionally, we discuss the impacts of parameter uncertainty in the quality of model predictions. Overall, this mini-symposium focuses on the challenges of integrating data into multiscale simulations and the techniques and strategies that can be used to overcome these challenges.