Cart (Loading....) | Create Account
Close category search window

Model discrimination of chemical reaction networks by linearization

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Georgiev, D. ; Dept. of Electr. Eng., Univ. of Washington, Seattle, WA, USA ; Fazel, M. ; Klavins, E.

Systems biologists are often faced with competing models for a given experimental system. Performing experiments can be time-consuming and expensive. Therefore, a method for designing experiments that, with high probability, discriminate between competing models is desired. In particular, biologists often employ models comprised of polynomial ordinary differential equations that arise from biochemical networks. Unfortunately, the model discrimination problem for such systems is computationally intractable. Here, we examine the linear discrimination problem: given two systems of linear differential equations with the same input and output spaces, and uncertain parameters, determine an input that is guaranteed to produce different outputs. In this context, we show that (1) if linearizations of the two nonlinear models can be discriminated, then so can the original nonlinear model; and (2) we show a class of systems for which the linear discrimination problem is convex. The approach is illustrated on a biochemical network with an unknown structure.

Published in:

American Control Conference (ACC), 2010

Date of Conference:

June 30 2010-July 2 2010

Need Help?

IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.