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Published April 2006 | public
Journal Article Open

Advanced methods and algorithms for biological networks analysis

Abstract

Modeling and analysis of complex biological networks presents a number of mathematical challenges. For the models to be useful from a biological standpoint, they must be systematically compared with data. Robustness is a key to biological understanding and proper feedback to guide experiments,including both the deterministic stability and performance properties of models in the presence of parametric uncertainties and their stochastic behavior in the presence of noise. In this paper, we present mathematical and algorithmic tools to address such questions for models that may be nonlinear, hybrid,and stochastic. These tools are rooted in solid mathematical theories, primarily from robust control and dynamical systems, but with important recent developments. They also have the potential for great practical relevance, which we explore through a series of biologically motivated examples.

Additional Information

© Copyright 2006 IEEE. Reprinted with permission. Manuscript received September 10, 2005; revised December 9, 2005. [Posted online: 2006-04-10] This work was supported in part by the Army Institute for Collaborative Biotechnologies, in part by the National Science Foundation under Award CCF-0326635 (ITR COLLAB: Theory and Software Infrastructure for a Scalable Systems Biology) and Award CCF-0326576, in part by the Air Force Office of Scientific Research (AFOSR) under Award FA9550-05-1-0032, Bio Inspired Networks, and in part by the Boeing Company.

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August 22, 2023
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