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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | en_US |
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Public Library of Science
2013
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| Online Access: | http://hdl.handle.net/1721.1/77202 |
| _version_ | 1811085325928235008 |
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| author | Mitsos, Alexander Melas, Ioannis N. Morris, Melody Kay Saez-Rodriguez, Julio Lauffenburger, Douglas A. Alexopoulos, Leonidas G. |
| author2 | Massachusetts Institute of Technology. Cell Decision Process Center |
| author_facet | Massachusetts Institute of Technology. Cell Decision Process Center Mitsos, Alexander Melas, Ioannis N. Morris, Melody Kay Saez-Rodriguez, Julio Lauffenburger, Douglas A. Alexopoulos, Leonidas G. |
| author_sort | Mitsos, Alexander |
| collection | MIT |
| 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. |
| first_indexed | 2024-09-23T13:07:08Z |
| format | Article |
| id | mit-1721.1/77202 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T13:07:08Z |
| publishDate | 2013 |
| publisher | Public Library of Science |
| record_format | dspace |
| spelling | mit-1721.1/772022022-10-01T13:10:30Z Non Linear Programming (NLP) Formulation for Quantitative Modeling of Protein Signal Transduction Pathways Mitsos, Alexander Melas, Ioannis N. Morris, Melody Kay Saez-Rodriguez, Julio Lauffenburger, Douglas A. Alexopoulos, Leonidas G. Massachusetts Institute of Technology. Cell Decision Process Center Massachusetts Institute of Technology. Department of Biological Engineering Massachusetts Institute of Technology. Department of Mechanical Engineering Mitsos, Alexander Morris, Melody Kay Lauffenburger, Douglas A. 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. National Institutes of Health (U.S.) (Grant P50-GM068762) National Institutes of Health (U.S.) (Grant R24-DK090963) United States. Army Research Office (Grant W911NF-09-0001) German Research Foundation (Grant GSC 111) 2013-02-26T21:55:43Z 2013-02-26T21:55:43Z 2012-11 2012-06 Article http://purl.org/eprint/type/JournalArticle 1932-6203 http://hdl.handle.net/1721.1/77202 Mitsos, 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 http://dx.doi.org/10.1371/journal.pone.0050085 PLoS ONE Creative Commons Attribution http://creativecommons.org/licenses/by/2.5/ application/pdf Public Library of Science PLoS |
| spellingShingle | Mitsos, Alexander Melas, Ioannis N. Morris, Melody Kay 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 |
| url | http://hdl.handle.net/1721.1/77202 |
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