Separable Nonlinear Least-Squares Parameter Estimation for Complex Dynamic Systems

Itai Dattner, Harold Ship, Eberhard O. Voit

Research output: Contribution to journalArticlepeer-review

Abstract

Nonlinear dynamic models are widely used for characterizing 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, which renders the inference of parameter values for complex dynamic models challenging. 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 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.

Original languageEnglish
Article number6403641
JournalComplexity
Volume2020
DOIs
StatePublished - 2020

Bibliographical note

Publisher Copyright:
© 2020 Itai Dattner et al.

ASJC Scopus subject areas

  • Computer Science (all)
  • General

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