The Modeling and Simulation Unit will provide a broad array of quatitative techniques employed in various aspects of translational research. These will include but not be limited to the following approaches:

• Allometric Modeling : The practice of scaling or normalizing physiological variables by deriving a relationship with an appropriate body size variable. Allometric models are used regularly in modeling complex biological phenomena where the actual mechanisms underlying the model behavior are too complex to describe in detail, but there is a need to be able to make predictions.
• PK/PD Modeling : The union of pharmacokinetic (PK) and pharmacodynamic (PD) modeling seeks to describe and predict the time course of drug effects under physiological and pathological conditions.
• Physiologic-based PK (PBPK) Modeling : The overlay of drug specific data onto an essentially independent structural model comprising the tissues and organs of the body with each perfused by and connected via the vascular system. The independent physiological data comprise, among others, tissue structure, tissue volume, tissue composition, and associated blood flows—all anatomically correct. As the model structure is essentially common to all mammalian species, the approach can facilitate interspecies scaling.
• Mechanistic Modeling : A representation of the physical, biological, or mechanistic theory governing the system; in contrast to an empirical model.
• Population-based PK/PD (Nonlinear, mixed effects) Modeling : An approach that describes the typical relationships between physiology (both normal and disease altered) and pharmacokinetics, t he interindividual variability in these relationships, and residual intraindividual variability.
• Bayesian Forecasting : Bayesian regression to estimate "individualized" model parameters, using population-based model values and measured or observed responses (one or more samples) from each patient. These individualized parameters can then used to predict the subsequent responses over time.
• Disease Progression Modeling : A model approach to describe, explain, and predict the changes in disease status as a function of time incorporating functions of natural disease progression (e.g. as characterized by specific biomarkers) as well as drug action.
• Clinical Trial Simulation : Monte Carlo based simulation technique integrating models of drug (input/output), covariate distribution and trial design used for sensitivity analyses and scenario testing.
• Discrete-event Simulation : An approach to study complex systems by computing the times that would be associated with real events in actual situations. DES can be employed to evaluate clinical trial design efficiency or any clinical situation in which building up models to observe the time based (or dynamic) behavior of a system is required.