Published June 2014
| public
Book Section - Chapter
A scalable formulation for engineering combination therapies for evolutionary dynamics of disease
Abstract
It has been shown that optimal controller synthesis for positive systems can be formulated as a linear program. Leveraging these results, we propose a scalable iterative algorithm for the systematic design of sparse, small gain feedback strategies that stabilize the evolutionary dynamics of a generic disease model. We achieve the desired feedback structure by augmenting the optimization problems with ℓ_1 and ℓ_2 regularization terms, and illustrate our method on an example inspired by an experimental study aimed at finding appropriate HIV neutralizing antibody therapy combinations in the presence of escape mutants.
Additional Information
© 2014 AACC. We would like to thank David Baltimore and Pamela Bjorkman for discussions regarding using antibody therapy for HIV treatment, and research scientist Anthony West for information on antibody neutralization parameters. We would like to thank Nikolai Matni for the review of the technical content. A. Rantzer gratefully acknowledges support of the LCCC Linnaeus Center and the eLLIIT Excellence Center at Lund University.Additional details
- Eprint ID
- 55941
- DOI
- 10.1109/ACC.2014.6859452
- Resolver ID
- CaltechAUTHORS:20150320-090407457
- Lund University
- Created
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2015-03-20Created from EPrint's datestamp field
- Updated
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2021-11-10Created from EPrint's last_modified field