Prince:Research

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(Protein post-translational modifications)
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==The Dynamic Proteome==
==The Dynamic Proteome==
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Our aim is to create predictive models of cellular behaviour, with a focus on modelling signal transduction dynamics.
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Our aim is to create predictive models of cellular behavior, with a focus on modeling signal transduction dynamics.
===Protein Protein Interactions===
===Protein Protein Interactions===
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Proteins interact with one another in order to transmit signals from the outside environment (signal transduction), form complex molecular machines, and localize to specific areas of the cell.  Current methods for capturing protein-protein interactions (PPIs) lack context and/or specificity.  We are developing methods that will enable us to capture ''in vivo'' PPIs involving new biochemical, mass spectrometric, and computational techniques.
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Proteins interact with one another in order to transmit signals from the outside environment (signal transduction), form complex molecular machines, and localize to specific areas of the cell.  Current methods for capturing protein-protein interactions (PPIs) lack context and/or specificity.  We are developing methods that will enable us to capture ''in vivo'' PPIs involving new biochemical, mass spectrometric, and computational techniques.  See [[Prince:Notebook/PPIX|PPIX project page]].
===Protein post-translational modifications===
===Protein post-translational modifications===
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Proteins undergo state changes through post-translational modifications.  These chemical modifications determine the activity of enzymes, the localization of proteins, and a protein's interactions.  We are working to perfect high-throughput methodologies to measure global protein phosphorylation.
Proteins undergo state changes through post-translational modifications.  These chemical modifications determine the activity of enzymes, the localization of proteins, and a protein's interactions.  We are working to perfect high-throughput methodologies to measure global protein phosphorylation.
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In collaboration with Emily Bates' lab, we are analyzing differential phosphorylation in brain tissue between migraine susceptible and wild-type mice.
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In collaboration with Emily Bates' lab, we are currently analyzing differential phosphorylation in brain tissue between migraine susceptible and wild-type mice. See [[Prince:Notebook/Mouse_Brain_Phosphoproteomics|Mouse Brain Phosphoproteomics project page]].
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===Measuring cellular state===
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===Systems Biology===
===Systems Biology===
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The integration of measures of protein state to create meaningful models of cellular behavior is an ongoing challenge in Systems Biology.
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Given certain time series data and over/underexpression of particular nodes, Dynamical Structure Analysis (DSA) has proven capable of reconstructing network topology.  We are working with Sean Warnick's lab to apply DSA to signal transduction networks.  Questions we are addressing involve scaling DSA to larger numbers of measurements, dealing with noise and signal loss in measurements, and supplementing network reconstruction with direct measures of protein interactions.  Relevant publications from the Warnick lab: [http://idealabs.byu.edu/publications/conference/ReconBioNetCDC08.pdf CDC08.pdf],[http://idealabs.byu.edu/publications/conference/FOSBE-ModelRed.pdf FOSBE09], [http://idealabs.byu.edu/publications/conference/FOSBE-Comparison.pdf FOSBE09-2],[http://idealabs.byu.edu/publications/conference/CDC2009-ModelRed.pdf CDC09]
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===Open, Dynamic Tools for Proteomics===
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Mass spectrometry based proteomic data sets are challenging to analyze due to their enormous size, complexity, and changing specifications (as they attempt to keep up with a rapidly evolving field).  The development of '''fast''', '''easily modifiable''' software enables researchers to glean much additional information from existing data sets and rapidly test new analytical approaches.
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We are the main support behind the [http://mspire.rubyforge.org/ mspire] libraries, enabling programmatic access to a variety of mass spectrometry data.
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We are also currently working on:
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* A dynamic, massively parallel data analysis pipeline (aptly named '''pipeline''' at the moment).
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* Blazingly fast visualization of mass spectrometry data sets.

Revision as of 17:41, 27 May 2010

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Contents

The Dynamic Proteome

Our aim is to create predictive models of cellular behavior, with a focus on modeling signal transduction dynamics.

Protein Protein Interactions

Proteins interact with one another in order to transmit signals from the outside environment (signal transduction), form complex molecular machines, and localize to specific areas of the cell. Current methods for capturing protein-protein interactions (PPIs) lack context and/or specificity. We are developing methods that will enable us to capture in vivo PPIs involving new biochemical, mass spectrometric, and computational techniques. See PPIX project page.

Protein post-translational modifications

Proteins undergo state changes through post-translational modifications. These chemical modifications determine the activity of enzymes, the localization of proteins, and a protein's interactions. We are working to perfect high-throughput methodologies to measure global protein phosphorylation.

In collaboration with Emily Bates' lab, we are currently analyzing differential phosphorylation in brain tissue between migraine susceptible and wild-type mice. See Mouse Brain Phosphoproteomics project page.

Systems Biology

The integration of measures of protein state to create meaningful models of cellular behavior is an ongoing challenge in Systems Biology.

Given certain time series data and over/underexpression of particular nodes, Dynamical Structure Analysis (DSA) has proven capable of reconstructing network topology. We are working with Sean Warnick's lab to apply DSA to signal transduction networks. Questions we are addressing involve scaling DSA to larger numbers of measurements, dealing with noise and signal loss in measurements, and supplementing network reconstruction with direct measures of protein interactions. Relevant publications from the Warnick lab: CDC08.pdf,FOSBE09, FOSBE09-2,CDC09

Open, Dynamic Tools for Proteomics

Mass spectrometry based proteomic data sets are challenging to analyze due to their enormous size, complexity, and changing specifications (as they attempt to keep up with a rapidly evolving field). The development of fast, easily modifiable software enables researchers to glean much additional information from existing data sets and rapidly test new analytical approaches.

We are the main support behind the mspire libraries, enabling programmatic access to a variety of mass spectrometry data.

We are also currently working on:

  • A dynamic, massively parallel data analysis pipeline (aptly named pipeline at the moment).
  • Blazingly fast visualization of mass spectrometry data sets.
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