期刊
PROCEEDINGS OF THE IEEE
卷 94, 期 4, 页码 832-853出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JPROC.2006.871776
关键词
biological networks; model invalidation; robust stability; sum of squares based software tools (SOSTOOLS); stochastic analysis
Modeling and analysis of complex biological networks presents a number of mathematical challenges. For the models to be useful,from a biological standpoint, then, 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.
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