Title: Nonlinear-RANSAC parameter optimisation for dynamic molecular systems and signalling pathways
Authors: Mingon Kang; Liping Tang; Jean Gao
Addresses: Department of Computer Science, Kennesaw State University, Marietta, GA, USA ' Department of Bioengineering, University of Texas at Arlington, Arlington, TX, USA ' Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX, USA
Abstract: Vigorous mathematical modelling and accurate parameter estimation of the models are indispensable for building reliable models that represent dynamic characteristics of the biological systems. A challenging task in modelling complex biological systems is the accurate estimation of the large number of unknown parameters in the mathematical modelling. To tackle this problem, we develop a data-driven optimisation method, nonlinear RANS AC, based on linear RANdom SAmple Consensus (a.k.a. RANSAC). Conventional RANSAC method is sound and simple, but it is oriented from linear system models. Our proposed nonlinear RANSAC extends its capability to nonlinear systems, while preserving the strengths of RANSAC. We applied nonlinear RANSAC to the dynamic molecular systems of phagocyte transmigration and signalling pathways. The parameters of mathematical equations for the phagocyte transmigration system were estimated by the proposed nonlinear RANSAC and compared the performance with ordinary least squares. Nonlinear RANSAC was also applied to signalling pathways, where mathematical equations are formulated using ordinary differential equations that represent molecular interactions between two biological components.
Keywords: nonlinear RANSAC; parameter estimation; dynamic molecular systems; signalling pathway.
International Journal of Data Mining and Bioinformatics, 2017 Vol.18 No.2, pp.164 - 178
Received: 11 May 2017
Accepted: 19 May 2017
Published online: 29 Aug 2017 *