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CENTER for mODELING INTEGRATED METABOLIC SYSTEMS

Striving to develop mechanistic, mathematical models to simulate cellular metabolism in various tissues and organs and to integrate these components in whole-body models.

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modeling integrated metabolic systems

WELCOME to MIMS

Background

The Center for Modeling Integrated Metabolic Systems (MIMS) combines mathematical modeling, computer simulation, and in vivo experimentation to quantify relationships between cellular metabolism and physiological responses of tissue-organ systems and the whole body. The MIMS Center was inspired by Dr. Marco E. Cabrera (deceased), who together with Prof. Gerald M. Saidel, co-directed this Center. It was established in 2002 with a $11.8 million grant (P50-GM066309) from NIGMS of the National Institutes of Health as a Center of Excellence in Complex Biomedical Systems (later Systems Biology). The MIMS Center involves multi-disciplinary research teams from Case Western Reserve University, Case Medical Center of University Hospitals of Cleveland, and Cleveland Clinic.

Modeling, Simulation, and Experimental Validation

The primary aim of the MIMS Center is to develop mechanistic, mathematical models to simulate cellular metabolism in various tissues and organs (i.e., skeletal muscle, heart, brain, and adipose tissue) and to integrate these components in whole-body models. These biologically and physiologically based computational models incorporate cellular metabolic reactions and transport processes of a large number of chemical species. Model parameters quantitatively characterize metabolic pathways and regulatory mechanisms under normal and abnormal conditions including obesity and hypoxia as well as in disease states including type-2 diabetes, cystic fibrosis, and chronic kidney disease. The large-scale, complex mathematical models are solved numerically using sophisticated computational algorithms to simulate and analyze experimental responses to physiological and metabolic changes. Model parameters are optimally estimated by minimizing differences between model simulated outputs and experimental data using large-scale, nonlinear optimization algorithms. Experimentally validated models are used to predict the effects of altering metabolic processes with disease states, pharmacological agents, diet, and physical training.