CRC

CRC performs nonlinear model calibration and robustness analysis using omics data by estimating the probability density function (pdf) of model parameters conditioned on experimental measurements.

Key Features:

• Bayesian framework: Employs a Bayesian approach for parameter estimation in nonlinear models.
• Iterative sampling algorithm: Uses an iterative algorithm that samples a proposal distribution.
• Multiple objective functions: Defines multiple objective functions tailored to each observable within the biological system.
• Posterior pdf estimation: Estimates the probability density function (pdf) of model parameters conditioned on experimental measurements.
• Robustness analysis: Quantifies the influence of individual parameters on the behavior of observables.
• Comparative efficiency: Reports reduced computational cost relative to Profile Likelihood (PL), Approximate Bayesian Computation Sequential Monte Carlo (ABC-SMC), and Delayed Rejection Adaptive Metropolis (DRAM).
• Omics data integration: Calibrates models using omics data as experimental input.
• Application to ODE models: Applied to three Ordinary Differential Equations (ODE) models for method evaluation.
• Implementation: Provided as a set of Matlab functions (R2018).

Scientific Applications:

• Nonlinear model calibration: Calibration of nonlinear dynamic models against experimental omics measurements.
• Robustness assessment: Assessing parameter influence on observables to analyze robustness in systems biology models.
• Benchmarking inference methods: Comparing calibration performance and computational cost versus PL, ABC-SMC, and DRAM on ODE models.

Methodology:

Uses an iterative algorithm that samples a proposal distribution and defines multiple objective functions per observable to estimate parameter pdfs conditioned on experimental measurements and quantify parameter influence.