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Markov random field modeling of the spatial distribution of proteins on cell membranes


Please use this identifier to cite or link to this item: http://hdl.handle.net/1928/10918

Markov random field modeling of the spatial distribution of proteins on cell membranes

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Title: Markov random field modeling of the spatial distribution of proteins on cell membranes
Author: Zhang, Jun
Advisor(s): Williams, Lance
Committee Member(s): Lane, Terran
Luan, Shuang
Steinberg, Stanly
Wilson, Bridget
Department: University of New Mexico. Dept. of Computer Science
Subject: Cell membrane
Spatial distribution
Markov random fields
Parameter estimation
LC Subject(s): Cell receptors--Mathematical models.
Cellular signal transduction--Mathematical models.
Markov random fields.
Parameter estimation.
Degree Level: Doctoral
Abstract: Cell membranes display a range of receptors that bind ligands and activate signaling pathways. Signaling is characterized by dramatic changes in membrane molecular topography, including the co-clustering of receptors with signaling molecules and the segregation of other signaling molecules away from receptors. Electron microscopy of immunogold-labeled membranes is a critical technique to generate topographical information at the 5-10 nm resolution needed to understand how signaling complexes assemble and function. However, due to experimental limitations, only two molecular species can usually be labeled at a time. A formidable challenge is to integrate experimental data across multiple experiments where there are from 10 to 100 different proteins and lipids of interest and only the positions of two species can be observed simultaneously. As a solution, Markov random field (MRF) modeling is proposed to reconstruct the distribution of multiple cell membrane constituents from pair-wise data sets. MRFs are a powerful mathematical formalism for modeling correlations between states associated with neighboring sites in spatial lattices. The presence or absence of a protein of a specific type at a point on the cell membrane is a state. Since only two protein types can be observed, i.e., those bound to particles, and the rest cannot be observed, the problem is one of deducing the conditional distribution of a MRF with unobservable (hidden) states. Here, a multiscale MRF model has been developed and mathematical programming techniques have been used to infer the conditional distribution of a MRF for proteins of three types from observations showing the spatial relationships between only two types. Application to synthesized data shows that the spatial distributions of three proteins can be reliably estimated. Application to experimental data provides the first maps of the spatial relationship between groups of three different signaling molecules. Initially, a 4-neighborhood system was used in the MRF modeling. In order to improve reconstruction quality, a larger 8-neighborhood system was subsequently used in a multiscale Gibbs random field (GRF) formulation by exploiting the Markov-Gibbs equivalence. Application of the multiscale GRF model to synthesized and experimental data shows that the quality of reconstruction is improved. This work is an important step towards a more complete understanding of membrane spatial organization and dynamics during signaling.
Graduation Date: May 2010
URI: http://hdl.handle.net/1928/10918

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