Nonlinear-RANSAC parameter optimisation for dynamic molecular systems and signalling pathways
by Mingon Kang; Liping Tang; Jean Gao
International Journal of Data Mining and Bioinformatics (IJDMB), Vol. 18, No. 2, 2017

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.

Online publication date: Tue, 29-Aug-2017

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Data Mining and Bioinformatics (IJDMB):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email