Related Experiment Video
Updated: Feb 12, 2026

Watershed Planning within a Quantitative Scenario Analysis Framework
Published on: July 24, 2016
Linear-In-Flux-Expressions Methodology: Toward a Robust Mathematical Framework for Quantitative Systems Pharmacology
Sean T McQuade1, Ruth E Abrams2, Jeffrey S Barrett2
1Center for Computational and Integrative Biology, Rutgers University-Camden, Camden, NJ, USA.
Abstract:
Quantitative Systems Pharmacology (QSP) modeling is increasingly used as a quantitative tool for advancing mechanistic hypotheses on the mechanism of action of a drug, and its pharmacological effect in relevant disease phenotypes, to enable linking the right drug to the right patient. Application of QSP models relies on creation of virtual populations for simulating scenarios of interest. Creation of virtual populations requires 2 important steps, namely, identification of a subset of model parameters that can be associated with a phenotype of disease and development of a sampling strategy from identified distributions of these parameters. We improve on existing sampling methodologies by providing a means of representing the structural relationship across model parameters and describing propagation of variability in the model. This gives a robust, systematic method for creating a virtual population. We have developed the Linear-In-Flux-Expressions (LIFE) method to simulate variability in patient pharmacokinetics and pharmacodynamics using relationships between parameters at baseline to create a virtual population. We demonstrate the importance of this methodology on a model of cholesterol metabolism. The LIFE methodology brings us a step closer toward improved QSP simulators through enhanced capture of the observed variability in drug and disease clinical data.
Related Concept Videos
Fundamental Mathematical Principles in Pharmacokinetics: Mathematical Expressions and Units
One significant application of mathematics in pharmacokinetics is the characterization of drug distribution through the volume of distribution...
Systems of Linear Equations in Two Variables
Linear time-invariant Systems
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be...
Mathematical Induction
Electric Flux
Mathematical Modeling: Problem Solving

