Efficient parametric inference for stochastic biological systems with measured variability.

Area of Science:

• Computational Biology
• Systems Biology
• Statistical Inference

Background:

• Biological systems exhibit significant cell-to-cell variability, which impacts function and offers insights into system dynamics.
• Inferring parameters of stochastic biological systems from observed mean and variance is crucial but often computationally intensive.

Purpose of the Study:

• To develop efficient parametric inference methods for stochastic biological systems.
• To improve the computational efficiency of approximate Bayesian computation (ABC) for analyzing biological variability.

Main Methods:

• Derivation and application of analytic likelihood forms for efficient inference when available.
• Implementation of an approximate Bayesian computation (ABC) approach for parametric inference.
• Proposal of a novel, computationally cheaper ABC method that prioritizes mean behavior analysis before variance simulation.

Main Results:

• Analytic likelihood forms enable highly efficient parameter inference in specific cases.
• The proposed ABC method significantly accelerates computation by efficiently exploring parameter space.
• Demonstrated substantial speed increases on both synthetic and experimental biological datasets.

Conclusions:

• Efficient inference of stochastic system parameters is achievable through analytic likelihoods or optimized ABC methods.
• The developed ABC approach offers a computationally advantageous alternative for analyzing biological variability.
• This work enhances the feasibility of using complex stochastic models in biological research.