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Related Experiment Videos

DISTING: A web application for fast algorithmic computation of alternative indistinguishable linear compartmental

Natalie R Davidson1, Keith R Godfrey2, Faisal Alquaddoomi1

  • 1Biocybernetics Laboratory, Computer Science Department, University of California Los Angeles, CA 90095, U.S.A.

Computer Methods and Programs in Biomedicine
|April 11, 2017
PubMed
Summary

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DISTING is a new web application that identifies alternative, input-output indistinguishable compartmental models. It simplifies complex analysis, making it easier to find structurally identifiable models.

Area of Science:

  • Pharmacokinetics and Pharmacodynamics
  • Systems Biology
  • Mathematical Modeling

Background:

  • Assessing structural identifiability of linear compartmental models is crucial for reliable analysis.
  • Existing computational tools primarily focus on identifiability, not indistinguishability.
  • Input-output indistinguishability poses a challenge in model selection and interpretation.

Purpose of the Study:

  • Introduce DISTING, a novel web application for computing structurally identifiable linear compartmental models that are input-output indistinguishable.
  • Provide a user-friendly interface for assessing model indistinguishability, a capability not previously available in computer packages.
  • Illustrate the application of DISTING using examples of varying complexity.

Main Methods:

Keywords:
Compartmental modelingModel selectionParallel computingStructural identifiabilitySymbolic algebra

Related Experiment Videos

  • Utilizes advanced geometric and algebraic properties of linear compartmental models.
  • Features a user-friendly graphical model interface.
  • Incorporates novel computational tools for accelerated analysis, including Jacobian matrix reduction and submatrix rank reduction.

    • Main Results:

    • DISTING successfully identified multiple input-output indistinguishable models for 2, 3, and 4-compartment systems.
    • A 2-compartment model yielded two indistinguishable models.
    • A 4-compartment model revealed five indistinguishable models, highlighting the impact of unobserved compartments.

    Conclusions:

    • DISTING is freely available online, offering a significant advancement in compartmental modeling analysis.
    • The application simplifies complex algebraic analysis, previously performed manually.
    • Facilitates the identification of alternative, indistinguishable models, improving model selection and understanding.