Paper Info
Title
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 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.
Year
DOI
Venue
2014
10.1109/ACC.2014.6859452
American Control Conference
Keywords
DocType
ISSN
control system synthesis,diseases,evolutionary computation,feedback,linear programming,medical control systems,optimal control,patient treatment,stability,ℓ1 regularization term,ℓ2 regularization term,hiv neutralizing antibody therapy combinations,engineering combination therapies,escape mutants,evolutionary dynamics stabilization,generic disease model,linear program,optimal controller synthesis,optimization problems,positive systems,scalable iterative algorithm,small gain feedback strategies,biomedical,large scale systems
Conference
0743-1619
Citations 
PageRank 
References 
11
0.83
6
Authors
3
Name
Order
Citations
PageRank
Vanessa Jonsson1131.21
Rantzer, Anders21759215.97
Richard M. Murray3123221223.70