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dc.contributor.authorMitsos, Alexander
dc.contributor.authorMelas, Ioannis N.
dc.contributor.authorMorris, Melody Kay
dc.contributor.authorSaez-Rodriguez, Julio
dc.contributor.authorLauffenburger, Douglas A.
dc.contributor.authorAlexopoulos, Leonidas G.
dc.date.accessioned2013-02-26T21:55:43Z
dc.date.available2013-02-26T21:55:43Z
dc.date.issued2012-11
dc.date.submitted2012-06
dc.identifier.issn1932-6203
dc.identifier.urihttp://hdl.handle.net/1721.1/77202
dc.description.abstractModeling of signal transduction pathways plays a major role in understanding cells' function and predicting cellular response. Mathematical formalisms based on a logic formalism are relatively simple but can describe how signals propagate from one protein to the next and have led to the construction of models that simulate the cells response to environmental or other perturbations. Constrained fuzzy logic was recently introduced to train models to cell specific data to result in quantitative pathway models of the specific cellular behavior. There are two major issues in this pathway optimization: i) excessive CPU time requirements and ii) loosely constrained optimization problem due to lack of data with respect to large signaling pathways. Herein, we address both issues: the former by reformulating the pathway optimization as a regular nonlinear optimization problem; and the latter by enhanced algorithms to pre/post-process the signaling network to remove parts that cannot be identified given the experimental conditions. As a case study, we tackle the construction of cell type specific pathways in normal and transformed hepatocytes using medium and large-scale functional phosphoproteomic datasets. The proposed Non Linear Programming (NLP) formulation allows for fast optimization of signaling topologies by combining the versatile nature of logic modeling with state of the art optimization algorithms.en_US
dc.description.sponsorshipNational Institutes of Health (U.S.) (Grant P50-GM068762)en_US
dc.description.sponsorshipNational Institutes of Health (U.S.) (Grant R24-DK090963)en_US
dc.description.sponsorshipUnited States. Army Research Office (Grant W911NF-09-0001)en_US
dc.description.sponsorshipGerman Research Foundation (Grant GSC 111)en_US
dc.language.isoen_US
dc.publisherPublic Library of Scienceen_US
dc.relation.isversionofhttp://dx.doi.org/10.1371/journal.pone.0050085en_US
dc.rightsCreative Commons Attributionen_US
dc.rights.urihttp://creativecommons.org/licenses/by/2.5/en_US
dc.sourcePLoSen_US
dc.titleNon Linear Programming (NLP) Formulation for Quantitative Modeling of Protein Signal Transduction Pathwaysen_US
dc.typeArticleen_US
dc.identifier.citationMitsos, Alexander et al. “Non Linear Programming (NLP) Formulation for Quantitative Modeling of Protein Signal Transduction Pathways.” Ed. Christopher V. Rao. PLoS ONE 7.11 (2012).en_US
dc.contributor.departmentMassachusetts Institute of Technology. Cell Decision Process Centeren_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Biological Engineeringen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Mechanical Engineeringen_US
dc.contributor.mitauthorMitsos, Alexander
dc.contributor.mitauthorMorris, Melody Kay
dc.contributor.mitauthorLauffenburger, Douglas A.
dc.relation.journalPLoS ONEen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dspace.orderedauthorsMitsos, Alexander; Melas, Ioannis N.; Morris, Melody K.; Saez-Rodriguez, Julio; Lauffenburger, Douglas A.; Alexopoulos, Leonidas G.en
dspace.mitauthor.errortrue
mit.licensePUBLISHER_CCen_US
mit.metadata.statusComplete


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