On finding and using identifiable parameter combinations in nonlinear dynamic systems biology models and COMBOS: a
Nicolette Meshkat1, Christine Er-zhen Kuo1, Joseph DiStefano1
1Biocybernetics Laboratory, Departments of Computer Science and Medicine and Computational and Systems Biology Interdepartmental Program, University of California Los Angeles, Los Angeles, California, United States of America.
Plos One
|October 29, 2014
Summary
Biomodelers face parameter identifiability challenges. New algorithms and the COMBOS web app identify which model parameters are quantifiable and reveal relationships among unquantifiable ones for better experimental design.
Area of Science:
- Systems Biology
- Computational Biology
- Mathematical Modeling
Background:
- Parameter identifiability is crucial for quantifying biomodel parameters from data.
- Structural identifiability (SI) determines if parameters can be uniquely estimated.
- Unidentifiable parameters often possess algebraic relationships, offering insights for further quantification.
Purpose of the Study:
- To develop and implement novel algorithms for solving the structural identifiability problem in ordinary differential equation (ODE) models.
- To provide a user-friendly web application, COMBOS, for accessible SI analysis.
- To reveal explicit algebraic relationships among structurally unidentifiable parameters.
Main Methods:
- Development of novel algorithms based on Gröbner bases and algebraic transformations.
- Implementation of these algorithms into the COMBOS web application.
- Validation using ODE systems biology models of moderate complexity, including examples like p53 and hormone regulation models.
Main Results:
- COMBOS identifies uniquely and non-uniquely structurally identifiable parameters.
- The application reveals combinations of parameters that are not individually identifiable.
- For non-uniquely identifiable parameters, COMBOS provides the maximum number of possible solutions.
Conclusions:
- The COMBOS web application effectively addresses structural identifiability issues in ODE biomodels.
- It provides valuable information on parameter relationships, aiding in the design of new experiments.
- COMBOS facilitates both research and instructional use in systems biology modeling.
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