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

216
views

0
downloads

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

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.