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OverviewCourses & ModulesResearch Teams
Team Information Polarization – “The Polarization of Information on the Web”Team Forest Ecology – “Identifying Shifts in Forest Communities Using Machine Learning Techniques”Team Cyber-Physical Security – “Vetting the Energy and Security of Smart Buildings with Data Science”Team Disaster Emergency Data – “Improving the Distribution of Disaster Emergency Assistance Programs in the USA Based on Major Disasters Data Mapping 2008-2017”Codeswitching Triggers TeamDiabetes Probability TeamCrash Reduction TeamTeam Genetics Analysis of Substance AbuseTeam Machine Learning SecurityTeam Cancer Data - Improving Cancer Therapeutics through Medical Data AnalysisQuantitative Analyses of Cargo Trafficking Compartmental Integrity in Lowe SyndromeDevelopment of an Automatic Quantification Algorithm for Determining Fluorescence Distribution in Yeast CellsQuantitative Analysis of Yeast Cell Morphology Defects Induced by Gene De-RegulationAnalysis of Information Content of Biological ImagingUnderstanding Information-Energy InteractionsInvestigation of Metabolic Phenomena Using Information TheoryGraph Inference based on Random WalksA Fresh Look at Boolean Functions
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  • Quantitative Analysis of Yeast Cell Morphology Defects Induced by Gene De-Regulation

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    Integrating experimental biology, computer science, and statistics has enabled us to expedite research on Huntington’s disease, a fatal neurodegenerative disorder that causes dementia in middle aged individuals. Although Huntington’s disease is a disease that affects humans, the molecular pathways that lead to the the onset of neuron death can be studied within the budding yeast system. We are currently developing an algorithm that will recognize specific cell division defects out of multiple images without the need of human intervention. The goal of the algorithm is to be able to quantify budding yeast cell morphological defects at rates 100 times faster than by quantification via subjective analysis. This algorithm will undoubtedly expedite our quantification efforts towards finding the biological relevance of the Huntingtin protein and its interaction with Huntingtin Interacting Protein-1, as well as how polyQ overexpansion on the Huntingtin protein itself lead to neurodegeneration. This project has been active from September 1, 2015 - current. 

    A presentation of this project is avaliable here.


    Contact Information


    The primary contact for this Research Team is Felix Francisco-Sanchez who can be reached at: ffranci@purdue.edu

    Research Team Members
    In alphabetical order
    Student PI: Felix Francisco-Sanchez
    Graduate Student
    Purdue University
    ffranci@purdue.edu
    Advisor: Mark Daniel Ward
    Student PI: Mckeith Pearson
    Graduate Student
    Purdue University
    mnpearso@purdue.edu
    Advisor: R. Claudio Aguilar
    Advisors
    In alphabetical order
    Mark Daniel Ward
    Professor
    Purdue University
    mdw@purdue.edu
    R. Claudio Aguilar
    Associate Professor and Assistant Head
    Purdue University
    raguilar@purdue.edu
    Keywords
    Cell (biology)
    Gene
    Quantitative
    Yeast

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