Automated refinement and inference of analytical models for metabolic networks

TLDR

The reverse engineering of metabolic networks from experimental data is traditionally a labor‑intensive task requiring a priori systems knowledge. The study demonstrates an automated method that refines or constructs metabolic network models from time‑series data, compares it to dynamic flux estimation, and discusses its scalability for real‑time experiment design and control. The method selects among candidate models by designing experiments that reveal prediction disagreements, applies this to a nonlinear seven‑dimensional yeast glycolytic oscillation model, and refines or constructs models to match time‑series observations. The approach can recover full dynamical models from scratch, correct errors in approximated or overspecified models, remains robust to high noise, outperforms parametric regression and neural networks, and identifies invariant quantities that yield accurate kinetics and sensitivity coefficients.

Abstract

The reverse engineering of metabolic networks from experimental data is traditionally a labor-intensive task requiring a priori systems knowledge. Using a proven model as a test system, we demonstrate an automated method to simplify this process by modifying an existing or related model-–suggesting nonlinear terms and structural modifications–-or even constructing a new model that agrees with the system's time series observations. In certain cases, this method can identify the full dynamical model from scratch without prior knowledge or structural assumptions. The algorithm selects between multiple candidate models by designing experiments to make their predictions disagree. We performed computational experiments to analyze a nonlinear seven-dimensional model of yeast glycolytic oscillations. This approach corrected mistakes reliably in both approximated and overspecified models. The method performed well to high levels of noise for most states, could identify the correct model de novo, and make better predictions than ordinary parametric regression and neural network models. We identified an invariant quantity in the model, which accurately derived kinetics and the numerical sensitivity coefficients of the system. Finally, we compared the system to dynamic flux estimation and discussed the scaling and application of this methodology to automated experiment design and control in biological systems in real time.