Bayesian inference of biochemical kinetic parameters using the linear noise approximation.

Area of Science:

• Systems Biology
• Computational Biology
• Biophysics

Background:

• Fluorescent and luminescent gene reporters enable dynamic, single-cell quantification of molecular species.
• Mathematical modeling of these reporters requires robust statistical methods for parameter calibration.
• Stochasticity and noise in biochemical systems necessitate specialized inference methods for dynamic models.

Purpose of the Study:

• To present a computationally efficient algorithm for estimating biochemical kinetic parameters from gene reporter data.
• To develop a method for calibrating multivariate dynamical models of gene reporter systems.
• To address the need for effective statistical inference in stochastic biochemical modeling.

Main Methods:

• Utilizes the linear noise approximation to model biochemical reactions as stochastic dynamic systems.
• Derives an explicit likelihood function for computationally efficient parameter estimation.
• Employs a Bayesian framework with Markov chain Monte Carlo for model inference.

Main Results:

• The proposed algorithm facilitates efficient parameter estimation for stochastic gene reporter models.
• An explicit likelihood function allows for streamlined computational analysis.
• Bayesian inference using Markov chain Monte Carlo is effectively implemented.

Conclusions:

• The method avoids computationally costly data augmentation required by diffusion approximation techniques.
• The approach accommodates unobserved variables and measurement errors in the model.
• The proposed methodology offers a valuable alternative for analyzing gene reporter data, validated with simulated and experimental datasets.