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Open Access Methodology article

Confidence from uncertainty - A multi-target drug screening method from robust control theory

Camilla Luni1, Jason E Shoemaker1, Kevin R Sanft2, Linda R Petzold2 and Francis J Doyle1*

Author Affiliations

1 Department of Chemical Engineering, University of California, Santa Barbara, CA 93106-5080, USA

2 Department of Computer Science, University of California, Santa Barbara, CA 93106-5070, USA

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BMC Systems Biology 2010, 4:161  doi:10.1186/1752-0509-4-161

Published: 24 November 2010

Abstract

Background

Robustness is a recognized feature of biological systems that evolved as a defence to environmental variability. Complex diseases such as diabetes, cancer, bacterial and viral infections, exploit the same mechanisms that allow for robust behaviour in healthy conditions to ensure their own continuance. Single drug therapies, while generally potent regulators of their specific protein/gene targets, often fail to counter the robustness of the disease in question. Multi-drug therapies offer a powerful means to restore disrupted biological networks, by targeting the subsystem of interest while preventing the diseased network from reconciling through available, redundant mechanisms. Modelling techniques are needed to manage the high number of combinatorial possibilities arising in multi-drug therapeutic design, and identify synergistic targets that are robust to system uncertainty.

Results

We present the application of a method from robust control theory, Structured Singular Value or μ- analysis, to identify highly effective multi-drug therapies by using robustness in the face of uncertainty as a new means of target discrimination. We illustrate the method by means of a case study of a negative feedback network motif subject to parametric uncertainty.

Conclusions

The paper contributes to the development of effective methods for drug screening in the context of network modelling affected by parametric uncertainty. The results have wide applicability for the analysis of different sources of uncertainty like noise experienced in the data, neglected dynamics, or intrinsic biological variability.