Efficient inference of parsimonious phenomenological models of cellular dynamics using S-systems and alternating regression

TL;DR

This paper introduces an efficient method combining S-systems and adaptive regression to infer simple yet nonlinear models of cellular dynamics, demonstrated on yeast glycolysis data.

Contribution

It presents a novel adaptive approach that maintains computational efficiency while capturing nonlinear cellular dynamics using S-systems.

Findings

High predictive accuracy on yeast glycolysis data

Models with complexity adapted to inference difficulty

Fast inference with minimal computational resources

Abstract

The nonlinearity of dynamics in systems biology makes it hard to infer them from experimental data. Simple linear models are computationally efficient, but cannot incorporate these important nonlinearities. An adaptive method based on the S-system formalism, which is a sensible representation of nonlinear mass-action kinetics typically found in cellular dynamics, maintains the efficiency of linear regression. We combine this approach with adaptive model selection to obtain efficient and parsimonious representations of cellular dynamics. The approach is tested by inferring the dynamics of yeast glycolysis from simulated data. With little computing time, it produces dynamical models with high predictive power and with structural complexity adapted to the difficulty of the inference problem.