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Distribution-based sensitivity metric for highly variable biochemical systems.

H P Mirsky1, S R Taylor, R A Harvey

  • 1University of California - Santa Barbara, Program in Biomolecular Science and Engineering, Santa Barbara, USAColby College, Department of Computer Science, Waterville, USAUniversity of California - Santa Barbara, Department of Chemical Engineering, Santa Barbara, USAETH Zurich, Department of Biosystems Science and Engineering and Swiss Institute of Bioinformatics, Basel, SwitzerlandUniversity of California - Santa Barbara, Department of Chemical Engineering, Program in Biomolecular Science and Engineering, Santa Barbara, USA.

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Summary
This summary is machine-generated.

This study introduces a new sensitivity analysis method applicable to both deterministic and stochastic models. The research reveals circadian clocks are fragile to global parameters but robust to local ones.

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Biophysics

Background:

  • Classical sensitivity analysis is standard for deterministic biochemical networks.
  • Intracellular systems, often large and complex, necessitate stochastic modeling.
  • Existing stochastic sensitivity methods differ from deterministic ones, hindering direct comparison.

Purpose of the Study:

  • To introduce a unified distribution-based sensitivity analysis methodology.
  • To apply this method to deterministic and stochastic models of the mouse circadian clock.
  • To compare sensitivity measurements between deterministic and stochastic model variants.

Main Methods:

  • Developed a distribution-based sensitivity measure applicable to both deterministic and stochastic models.
  • Applied the method to a mathematical model of the mouse circadian clock (73 ODEs).
  • Utilized the sensitivity measure for model reduction and noise effect evaluation.

Main Results:

  • Sensitivity analysis showed high conservation of rank-order sensitivity across model variants.
  • The circadian clock demonstrated fragility to global parameters and robustness to local parameters.
  • Model reduction from 73 to 18 ODEs was achieved using the sensitivity measure.

Conclusions:

  • The new method enables consistent sensitivity analysis across deterministic and stochastic models.
  • Circadian clock robustness is linked to parameter specificity (local vs. global).
  • The methodology aids in model simplification and understanding noise impacts on biological clocks.