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Non Linear Programming (NLP) Formulation for Quantitative Modeling of Protein Signal Transduction Pathways

Modeling 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 constru...

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Autores principales: Mitsos, Alexander, Melas, Ioannis N., Morris, Melody K., Saez-Rodriguez, Julio, Lauffenburger, Douglas A., Alexopoulos, Leonidas G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3511450/
https://www.ncbi.nlm.nih.gov/pubmed/23226239
http://dx.doi.org/10.1371/journal.pone.0050085
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author Mitsos, Alexander
Melas, Ioannis N.
Morris, Melody K.
Saez-Rodriguez, Julio
Lauffenburger, Douglas A.
Alexopoulos, Leonidas G.
author_facet Mitsos, Alexander
Melas, Ioannis N.
Morris, Melody K.
Saez-Rodriguez, Julio
Lauffenburger, Douglas A.
Alexopoulos, Leonidas G.
author_sort Mitsos, Alexander
collection PubMed
description Modeling 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.
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spelling pubmed-35114502012-12-05 Non Linear Programming (NLP) Formulation for Quantitative Modeling of Protein Signal Transduction Pathways Mitsos, Alexander Melas, Ioannis N. Morris, Melody K. Saez-Rodriguez, Julio Lauffenburger, Douglas A. Alexopoulos, Leonidas G. PLoS One Research Article Modeling 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. Public Library of Science 2012-11-30 /pmc/articles/PMC3511450/ /pubmed/23226239 http://dx.doi.org/10.1371/journal.pone.0050085 Text en © 2012 Mitsos et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Mitsos, Alexander
Melas, Ioannis N.
Morris, Melody K.
Saez-Rodriguez, Julio
Lauffenburger, Douglas A.
Alexopoulos, Leonidas G.
Non Linear Programming (NLP) Formulation for Quantitative Modeling of Protein Signal Transduction Pathways
title Non Linear Programming (NLP) Formulation for Quantitative Modeling of Protein Signal Transduction Pathways
title_full Non Linear Programming (NLP) Formulation for Quantitative Modeling of Protein Signal Transduction Pathways
title_fullStr Non Linear Programming (NLP) Formulation for Quantitative Modeling of Protein Signal Transduction Pathways
title_full_unstemmed Non Linear Programming (NLP) Formulation for Quantitative Modeling of Protein Signal Transduction Pathways
title_short Non Linear Programming (NLP) Formulation for Quantitative Modeling of Protein Signal Transduction Pathways
title_sort non linear programming (nlp) formulation for quantitative modeling of protein signal transduction pathways
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3511450/
https://www.ncbi.nlm.nih.gov/pubmed/23226239
http://dx.doi.org/10.1371/journal.pone.0050085
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