The development of our general methodology has been rooted in the rigorous mathematical framework of the Volterra-Wiener approach to nonlinear system analysis and its various extensions over the last 35 years [9-16]. This methodological framework was selected because: (a) it is mathematically rigorous and allows practical identification of the system dynamics from input-output data without requiring prior knowledge of the internal organization of the system;(b) it is applicable to a very broad class of nonlinear dynamic biomedical systems (i.e. those with finite memory);(c) it yields models that represent the system function under broad (natural) operating conditions and allows predictions for arbitrary input signals (within the experimental range of frequencies/amplitudes);(d) it is robust in the presence of noise commonly found in biomedical time-series data. This remarkable set of attributes has been validated over the last 35 years. The development of practical methods that achieve high estimation accuracy with modest experimental and computational requirements has been achieved for single-input/single-output and multi-input/multi-output (MIMO) systems [14-60], although certain challenges remain with regard to compactness and interpretability of large-scale MIMO models, as well as the modeling of multi-variable nested-loop configurations. These issues represent the next generation of modeling challenges (as formulated in the Specific Aims) and attain rising importance as we begin to recognize the potential utility of these models for clinical purposes.

National Institute of Health (NIH)
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Biotechnology Resource Grants (P41)
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Special Emphasis Panel (ZEB1-OSR-C)
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University of Southern California
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