Separable nonlinear least-squares parameter estimation for complex dynamic systems

TL;DR

This paper introduces a separable nonlinear least-squares method for parameter estimation in complex biological dynamic systems, demonstrating improved efficiency and accuracy over traditional methods through extensive simulations.

Contribution

The paper presents a novel separable nonlinear least-squares optimization approach tailored for complex biological models, enhancing computational efficiency and accuracy.

Findings

The proposed method is at least as accurate as traditional nonlinear least-squares.

It generally offers superior performance in terms of convergence.

The approach significantly reduces computational time.

Abstract

Nonlinear dynamic models are widely used for characterizing functional forms of processes that govern complex biological pathway systems. Over the past decade, validation and further development of these models became possible due to data collected via high-throughput experiments using methods from molecular biology. While these data are very beneficial, they are typically incomplete and noisy, so that inferring parameter values for complex dynamic models is associated with serious computational challenges. Fortunately, many biological systems have embedded linear mathematical features, which may be exploited, thereby improving fits and leading to better convergence of optimization algorithms. In this paper, we explore options of inference for dynamic models using a novel method of {\it separable nonlinear least-squares optimization}, and compare its performance to the traditional nonlinear least-squares method. The numerical results from extensive simulations suggest that the proposed approach is at least as accurate as the traditional nonlinear least-squares, but usually superior, while also enjoying a substantial reduction in computational time.