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

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...

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Bibliographic Details
Published in:BMC systems biology Vol. 4; no. 1; p. 161
Main Authors: Luni, Camilla, Shoemaker, Jason E, Sanft, Kevin R, Petzold, Linda R, Doyle, Francis J
Format: Journal Article
Language:English
Published: London BioMed Central 24.11.2010
BioMed Central Ltd
Subjects:
Algorithms
Antibiotics
Behavior
Bioinformatics
Biomedical and Life Sciences
Cellular and Medical Topics
Computational Biology/Bioinformatics
Computer science
Confidence
Diabetes
Drug Evaluation, Preclinical - methods
Drug therapy
Feedback, Physiological
Genes
Health aspects
Life Sciences
Mandatory drug testing
Methodology
Methodology Article
Methods
Models, Biological
Ordinary differential equations
Physiological
Physiological aspects
Signal transduction
Simulation and Modeling
Studies
Systems Biology
Systems Biology - methods
Uncertainty
United States
Stochastic Simulation Algorithm
Robust Performance
System Biology Markup Language
Nominal Performance
Local Sensitivity Analysis
ISSN:1752-0509, 1752-0509
Online Access:Get full text
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Summary: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.
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ISSN:1752-0509
1752-0509
DOI:10.1186/1752-0509-4-161