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A Scalable Formulation for Engineering Combination Therapies for Evolutionary Dynamics of Disease

Jonsson, Vanessa; Rantzer, Anders LU and Murray, Richard M. (2014) American Control Conference, 2014
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 l1 and l2 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.
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organization
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Contribution to conference
publication status
published
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conference name
American Control Conference, 2014
external identifiers
  • scopus:84905695086
project
LCCC
language
English
LU publication?
yes
id
cda2b482-da78-417b-8866-82069077427a (old id 4933540)
date added to LUP
2015-01-14 09:59:40
date last changed
2017-08-27 06:09:12
@misc{cda2b482-da78-417b-8866-82069077427a,
  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 l1 and l2 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.},
  author       = {Jonsson, Vanessa and Rantzer, Anders and Murray, Richard M.},
  language     = {eng},
  title        = {A Scalable Formulation for Engineering Combination Therapies for Evolutionary Dynamics of Disease},
  year         = {2014},
}