TY - JOUR
T1 - Separable Nonlinear Least-Squares Parameter Estimation for Complex Dynamic Systems
AU - Dattner, Itai
AU - Ship, Harold
AU - Voit, Eberhard O.
N1 - Publisher Copyright: © 2020 Itai Dattner et al.
PY - 2020/4/2
Y1 - 2020/4/2
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85083519926&partnerID=8YFLogxK
U2 - https://doi.org/10.1155/2020/6403641
DO - https://doi.org/10.1155/2020/6403641
M3 - Article
C2 - 34113070
SN - 1076-2787
VL - 2020
JO - Complexity
JF - Complexity
M1 - 6403641
ER -