Global parameter identification of stochastic reaction networks from single trajectories

TL;DR

This paper introduces a novel method combining Gaussian Adaptation and exact stochastic simulation algorithms to infer unknown parameters of stochastic biochemical networks from single trajectories, enabling insights into cell variability and system dynamics.

Contribution

The paper presents a new approach for parameter inference from single stochastic trajectories using adaptive Monte Carlo sampling and exact simulation algorithms, improving robustness and providing parameter space volume estimates.

Findings

Effective parameter inference from single trajectories demonstrated on reaction networks.

Method provides estimates of the physical volume of the biological compartment.

Benchmarking shows robustness during steady state and transient phases.

Abstract

We consider the problem of inferring the unknown parameters of a stochastic biochemical network model from a single measured time-course of the concentration of some of the involved species. Such measurements are available, e.g., from live-cell fluorescence microscopy in image-based systems biology. In addition, fluctuation time-courses from, e.g., fluorescence correlation spectroscopy provide additional information about the system dynamics that can be used to more robustly infer parameters than when considering only mean concentrations. Estimating model parameters from a single experimental trajectory enables single-cell measurements and quantification of cell--cell variability. We propose a novel combination of an adaptive Monte Carlo sampler, called Gaussian Adaptation, and efficient exact stochastic simulation algorithms that allows parameter identification from single stochastic trajectories. We benchmark the proposed method on a linear and a non-linear reaction network at steady state and during transient phases. In addition, we demonstrate that the present method also provides an ellipsoidal volume estimate of the viable part of parameter space and is able to estimate the physical volume of the compartment in which the observed reactions take place.