title: Partial inhibition and bilevel optimization in flux balance analysis.
abstract: BACKGROUND
  Within Flux Balance Analysis, the investigation of complex subtasks, such as finding the optimal perturbation of the network or finding an optimal combination of drugs, often requires to set up a bilevel optimization problem. In order to keep the linearity and convexity of these nested optimization problems, an ON/OFF description of the effect of the perturbation (i.e. Boolean variable) is normally used. This restriction may not be realistic when one wants, for instance, to describe the partial inhibition of a reaction induced by a drug.
  
  
  RESULTS
  In this paper we present a formulation of the bilevel optimization which overcomes the oversimplified ON/OFF modeling while preserving the linear nature of the problem. A case study is considered: the search of the best multi-drug treatment which modulates an objective reaction and has the minimal perturbation on the whole network. The drug inhibition is described and modulated through a convex combination of a fixed number of Boolean variables. The results obtained from the application of the algorithm to the core metabolism of E.coli highlight the possibility of finding a broader spectrum of drug combinations compared to a simple ON/OFF modeling.
  
  
  CONCLUSIONS
  The method we have presented is capable of treating partial inhibition inside a bilevel optimization, without loosing the linearity property, and with reasonable computational performances also on large metabolic networks. The more fine-graded representation of the perturbation allows to enlarge the repertoire of synergistic combination of drugs for tasks such as selective perturbation of cellular metabolism. This may encourage the use of the approach also for other cases in which a more realistic modeling is required.
authors.affiliation: SISSA (International School for Advanced Studies) Functional Analysis Dept, - Via Bonomea 265 - 34136, Trieste, Italy. altafini@sissa.it.
authors.forename: Giuseppe
authors.initials: G
authors.lastname: Facchetti
authors.forename: Claudio
authors.initials: C
authors.lastname: Altafini
mesh_terms: D019747:Aldose-Ketose Isomerases / Q000037:antagonists & inhibitors / Q000378:metabolism; D000465:Algorithms; D003198:Computer Simulation; D004338:Drug Combinations; D004347:Drug Interactions; D004926:Escherichia coli / Q000201:enzymology / Q000378:metabolism; D005969:Glutamate Dehydrogenase / Q000037:antagonists & inhibitors / Q000378:metabolism; D006801:Humans; D060847:Metabolic Engineering / Q000379:methods*; D064688:Metabolic Flux Analysis / Q000379:methods*; D016571:Neural Networks, Computer; D012984:Software; D060388:Support Vector Machine; D014174:Transketolase / Q000037:antagonists & inhibitors / Q000378:metabolism
chemical_list: D004338:Drug Combinations; D005969:Glutamate Dehydrogenase; D014174:Transketolase; D019747:Aldose-Ketose Isomerases; C022548:ribosephosphate isomerase