Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Bayesian inference for the MAPK/ERK pathway by considering the dependency of the kinetic parameters
Date
2008-01-01
Author
Purutçuoğlu Gazi, Vilda
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
208
views
0
downloads
Cite This
The MAPK/ERK pathway is one of the major signal transduction systems which regulates the cellular growth control of all eukaryotes like the cell proliferation and the apoptosis. Because of its importance in cellular lifecycle, it has been studied intensively, resulting in a number of qualitative descriptions of this regulatory mechanism. In this study we describe the MAPK/ERK pathway as an explicit set of reactions by combining different sources. Our reaction set takes into account the localization and different binding sites of the molecules in the cell by implementing the multiple parametrization. Then we estimate the model parameters of the network in a Bayesian setting via MCMC and data augmentation schemes. In the estimation we apply the Euler approximation, which is the discretized version of the diffusion technique. Additionally in inference of such a realistic and complex system we consider all possible kinds of dependencies coming from distinct stages of updates. To test the inference method we use the simulated data generated by the Gillespie algorithm. From the analysis it is clear that the sampler mixes well and partially is able to identify the dynamics of the MAPK/ERK pathway.
Subject Keywords
Statistics and Probability
,
Applied Mathematics
URI
https://hdl.handle.net/11511/37993
Journal
BAYESIAN ANALYSIS
DOI
https://doi.org/10.1214/08-ba332
Collections
Department of Statistics, Article
Suggestions
OpenMETU
Core
Long-tailed graphical model and frequentist inference of the model parameters for biological networks
AĞRAZ, MELİH; Purutçuoğlu Gazi, Vilda (Informa UK Limited, 2020-03-12)
The biological organism is a complex structure regulated by interactions of genes and proteins. Various linear and nonlinear models can define activations of these interactions. In this study, we have aimed to improve the Gaussian graphical model (GGM), which is one of the well-known probabilistic and parametric models describing steady-state activations of biological systems, and its inference based on the graphical lasso, shortly Glasso, method. Because, GGM with Glasso can have low accuracy when the syst...
Integration of topological measures for eliminating non-specific interactions in protein interaction networks
BAYIR, Murat Ali; GUNEY, Tacettin Dogacan; Can, Tolga (Elsevier BV, 2009-05-28)
High-throughput protein interaction assays aim to provide a comprehensive list of interactions that govern the biological processes in a cell. These large-scale sets of interactions, represented as protein-protein interaction networks, are often analyzed by computational methods for detailed biological interpretation. However, as a result of the tradeoff between speed and accuracy, the interactions reported by high-throughput techniques occasionally include non-specific (i.e., false-positive) interactions. ...
Exact and Approximate Stochastic Simulations of the MAPK Pathway and Comparisons of Simulations' Results
Purutçuoğlu Gazi, Vilda (2006-10-01)
The MAPK (mitogen-activated protein kinase) or its synonymous ERK (extracellular signal regulated kinase) pathway whose components are Ras, Raf, and MEK proteins with many biochemical links, is one of the major signalling systems involved in cellular growth control of eukaryotes including cell proliferation, transformation, differentiation, and apoptosis. In this study we describe the MAPK/ERK pathway via (quasi) biochemical reactions and then implement the pathway by a stochastic Markov process. A novelty ...
Cancer onset and progression: A genome-wide, nonlinear dynamical systems perspective on onconetworks
Qu, K.; Haidar, A. Abi; Fan, J.; Ensman, L.; Tuncay, Kağan; Jolly, M.; Ortoleva, P. (Elsevier BV, 2007-05-21)
It is hypothesized that the many human cell types corresponding to multiple states is supported by an underlying nonlinear dynamical system (NDS) of transcriptional regulatory network (TRN) processes. This hypothesis is validated for epithelial cells whose TRN is found to support an extremely complex array of states that we term a "bifurcation nexus", for which we introduce a quantitative measure of complexity. The TRN used is constructed and analyzed by integrating a database of TRN information, cDNA micro...
Stochastic modelling of the MAPK signalling pathway
Purutçuoğlu Gazi, Vilda (null; 2006-06-01)
The MAPK (mitogen-activated protein kinase) or its synonymous ERK (extracellular signal regulated kinase) pathway whose components are Ras, Raf, and MEK proteins with many biochemical links, is one of the major signalling systems involved in cellular growth control of eukaryotes including cell proliferation, transformation, differentiation, and apoptosis. Because of its diverse functionality, it is also activated in a variety of hormone activation and many illnesses, which have multi-complex gene structure ...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
V. Purutçuoğlu Gazi, “Bayesian inference for the MAPK/ERK pathway by considering the dependency of the kinetic parameters,”
BAYESIAN ANALYSIS
, pp. 851–886, 2008, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/37993.