Nonlinear-RANSAC parameter optimisation for dynamic molecular systems and signalling pathways. (2017)

• Record Type: Journal Article
• Authors: Kang, Mingon; Tang, Liping; Gao, Jean
• Is Part Of: International journal of data mining and bioinformatics. Volume 18:Number 2(2017)
• Page Start: 164
• Page End: 178
• Publication Date: 2017
• Subjects: nonlinear RANSAC -- parameter estimation -- dynamic molecular systems -- signalling pathway; Data mining -- Periodicals; Bioinformatics -- Periodicals; 006.312
• Languages: English
• ISSNs: 1748-5673

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.