Chemical and Biological Engineering ETDs

Meghan Pryor

Publication Date

9-12-2014

Abstract

ErbB transmembrane receptors are a family of 4 receptor tyrosine kinases that interact with one another through homo and heterodimer interactions. When these dimers form, the kinase domains on the receptor tails interact with one another, transphosphorylating one another, initiating a signal cascade. The signaling pathways these receptors participate in are responsible for many different cell functions including apoptosis, growth, and proliferation. The overexpression of these receptors has been linked to various forms of cancer, emphasizing the importance of understanding how these receptors interact with one another to trigger these cascades. Single Particle Tracking experiments have provided more precise and detailed measures of dimer lifetimes and diffusion. A major observation from the experiments is the anomalous diffusion of the receptors. One suggested contributor to this anomalous diffusion is confinement zones on the membrane. In this work, we develop, validate, and implement a spatial stochastic model to study these receptors and uncover how their kinetics and dynamics as well as the membrane landscape come together to impact erbB activation. We start by focusing on erbB1. Single particle tracking experiments show that receptor pairs interact repeatedly over a period of time. One possible explanation for these repeated interactions is to facilitate phosphorylation. An asymmetric phosphorylation model is proposed, where one receptor in the dimer pair is responsible for activating the other receptor, the receiver, which then in turn phosphorylates the original activator. The model shows that the confinement zones on the membrane play a critical role in causing repeated receptor interactions and reveals that receptors dynamically switch between different activation states over time. Our work continues by delving deeper into the membrane landscape. Single particle tracking data is analyzed to investigate the characteristics of the observed anomalous diffusion. The analysis gives an estimate for the size range of the confinement zones and shows that they are a series of domains, not corrals. Taking the single particle tracking analysis one step further, we develop a Domain Reconstruction Algorithm that reconstructs confinement zone shapes and sizes from single particle tracking trajectories. In the final study, we move on to erbB2 and erbB3 interactions. ErbB3, which is traditionally believed to be kinase dead, has recently been shown to have weak kinase activity. Through kinase assay experiments, we show in the presence of erbB2 and heregulin, erbB3 has measurable kinase activity. Using the reconstructed domains from erbB2 and erbB3 data to create a simulation space, and experimental data from the kinase assay and single particle tracking, we extend the erbB1 spatial stochastic model for this study. We show that erbB2 and erbB3 have significantly different interactions with the cellular membrane confinement zones, erbB3 is dependent on erbB2 activation, and erbB3 homodimer stability inhibits erbB3 activation. Extension of the model to investigate mutation behaviors in erbB3 receptors reveals insights into how a gain of function mutation in the erbB3 kinase domain impacts erbB2 and erbB3 interactions. Finally, discovery of a gain of function mutation in the kinase domain of erbB3 is connected to an uptick in erbB3 kinase activity. As a path forward from this work, we suggest using the spatial stochastic model to investigate more possible mutations in erbB3 receptors to give better insight into which mutations would be promising to explore.

Keywords

Computational Biology, Mathematical Modeling, ErbB Receptor Family, Cell Signaling, Cellular Membrane

Sponsors

Spatial Temporal Modeling Center at University of New Mexico, National Science Foundation Integrative Graduate Education and Research Traineeship, Center for Nonlinear Studies at Los Alamos National Laboratory

Document Type

Dissertation

Language

English

Degree Name

Chemical Engineering

Level of Degree

Doctoral

Department Name

Chemical and Biological Engineering

First Committee Member (Chair)

Wilson, Bridget

Second Committee Member

Lidke, Diane

Third Committee Member

Shreve, Andrew

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