Here in the Systems Imaging and Modeling Lab (SIMLab) our objective is to characterize the phenotype changes in the pathophysiological states, particularly in oncological, infectious disease, and more recently regenerative medicine applications in order to develop strategies to better diagnose and treat these conditions.

Our approach to these ends are to Measure, Model, and siMulate (Validate),

To these ends, we build mathematical models for analysis and simulation based approaches in order to integrate multi-omic data in conjunction with clinical imaging data in order to predict physiologically meaningful phenotypes.

 

Some philosophical premises towards these ends,

• Life begins (and ends) with bio(-physical-)chemistry, so the focus of our efforts build up from metabolism
• Understanding how individual molecular components make up cells, tissues, and organs is critical to developing predictive, biologically meaningful models
• Physicochemical and engineering based approaches are critical for building the models and subsequent data analysis
• The technical feasibility of measurements determines how we analyze the systems and the type of conclusions that can be draw, so data-driven modeling is needed in order to develop these models

 

We are greatly appreciative of the funding sources to enable this research,