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.
|Original language||English (US)|
|Journal||BMC Systems Biology|
|State||Published - Nov 24 2010|
Bibliographical noteFunding Information:
CL, JES, and FJD were supported by Pfizer Inc. through Contract No. DFP01, and the Institute for Collaborative Biotechnologies through Grant No. W911NF-09-D-0001 from the U.S. Army Research Office. KRS and LRP were funded by Pfizer Inc. and the Institute for Collaborative Biotechnologies under Grant No. DFR3A-8-447850-23002 from the Army Research Office. LRP was also supported by Grant R01EB007511 from the National Institute of Biomedical Imaging and Bioengineering and DOE Contract No. DE-FG02-04ER25621. KRS was also supported by a National Science Foundation Graduate Research Fellowship.