Non Linear Programming (NLP) Formulation for Quantitative Modeling of Protein Signal Transduction Pathways
Name
Mitsos-2012-Non Linear Programmi.pdf
Size
1.27 MB
Format
Adobe PDF
Checksum (MD5)
ed9e464114213244ee4f319dcc34b5cd
Author(s)
Mitsos, Alexander
Melas, Ioannis N.
Morris, Melody Kay
Saez-Rodriguez, Julio
Lauffenburger, Douglas A.
Alexopoulos, Leonidas G.
Date Issued
November 2012
Journal
PLoS ONE
Publisher
Public Library of Science
Citation
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).
Version
Final published version
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Cell Decision Process Center
Massachusetts Institute of Technology. Department of Biological Engineering
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1371/journal.pone.0050085
