Molecular Biology, Cell Biology and Biochemistry Program

 
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URI: http://hdl.handle.net/2144/2426

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  • PC3 prostate tumor-initiating cells with molecular profile FAM65Bhigh/MFI2low/LEF1low increase tumor angiogenesis 

    Zhang, Kexiong; Waxman, David J. (BioMed Central, 2010-12-29)
    BACKGROUND Cancer stem-like cells are proposed to sustain solid tumors by virtue of their capacity for self-renewal and differentiation to cells that comprise the bulk of the tumor, and have been identified for a variety ...

  • A Unique Family of Mrr-Like Modification-Dependent Restriction Endonucleases 

    Zheng, Yu; Cohen-Karni, Devora; Xu, Derrick; Chin, Hang Gyeong; Wilson, Geoffrey; Pradhan, Sriharsa; Roberts, Richard J. (Oxford University Press, 2010-5-5)
    Mrr superfamily of homologous genes in microbial genomes restricts modified DNA in vivo. However, their biochemical properties in vitro have remained obscure. Here, we report the experimental characterization of MspJI, a ...

  • Gyrase Inhibitors Induce an Oxidative Damage Cellular Death Pathway in Escherichia Coli 

    Dwyer, Daniel J.; Kohanski, Michael A.; Hayete, Boris; Collins, James J. (2007-03-13)
    Modulation of bacterial chromosomal supercoiling is a function of DNA gyrase-catalyzed strand breakage and rejoining. This reaction is exploited by both antibiotic and proteic gyrase inhibitors, which trap the gyrase ...

  • Machine Learning for Regulatory Analysis and Transcription Factor Target Prediction in Yeast 

    Holloway, Dustin T.; Kon, Mark; DeLisi, Charles (Kluwer Academic Publishers, 2006-10-31)
    High throughput technologies, including array-based chromatin immunoprecipitation, have rapidly increased our knowledge of transcriptional maps-the identity and location of regulatory binding sites within genomes. Still, ...

  • VisANT: Data-Integrating Visual Framework for Biological Networks and Modules 

    Hu, Zhenjun; Mellor, Joe; Wu, Jie; Yamada, Takuji; Holloway, Dustin; DeLisi, Charles (Oxford University Press, 2005-06-27)
    VisANT is a web-based software framework for visualizing and analyzing many types of networks of biological interactions and associations. Networks are a useful computational tool for representing many types of biological ...

  • Classifying Transcription Factor Targets and Discovering Relevant Biological Features 

    Holloway, Dustin T.; Kon, Mark; DeLisi, Charles (BioMed Central, 2008-5-30)
    BACKGROUND. An important goal in post-genomic research is discovering the network of interactions between transcription factors (TFs) and the genes they regulate. We have previously reported the development of a ...

  • Portraits of Breast Cancer Progression 

    Dalgin, Gul S.; Alexe, Gabriela; Scanfeld, Daniel; Tamayo, Pablo; Mesirov, Jill P.; Ganesan, Shridar; DeLisi, Charles; Bhanot, Gyan (BioMed Central, 2007-8-6)
    BACKGROUND. Clustering analysis of microarray data is often criticized for giving ambiguous results because of sensitivity to data perturbation or clustering techniques used. In this paper, we describe a new method based ...

  • In silico regulatory analysis for exploring human disease progression 

    Holloway, Dustin T.; Kon, Mark; DeLisi, Charles (BioMed Central, 2008-6-18)
    BACKGROUND. An important goal in bioinformatics is to unravel the network of transcription factors (TFs) and their targets. This is important in the human genome, where many TFs are involved in disease progression. Here, ...

  • Identification and Characterization of Renal Cell Carcinoma Gene Markers 

    Dalgin, Gul S.; Holloway, Dustin T.; Liou, Louis S.; DeLisi, Charles (Libertas Academica, 2007-2-9)
    Microarray gene expression profiling has been used to distinguish histological subtypes of renal cell carcinoma (RCC), and consequently to identify specific tumor markers. The analytical procedures currently in use find ...

  • Data perturbation independent diagnosis and validation of breast cancer subtypes using clustering and patterns 

    Alexe, Gabriela; Dalgin, Gul S.; Ramaswamy, R.; DeLisi, Charles; Bhanot, Gyan (Libertas Academica, 2007-2-19)
    Molecular stratification of disease based on expression levels of sets of genes can help guide therapeutic decisions if such classifications can be shown to be stable against variations in sample source and data perturbation. ...