Program 
Abstract
Transcription Regulation in the Context of
Comparative Genomics and Evolution.
 
David Landsman, Ph.D.
Computational Biology Branch
NCBI, NLM, NIH
 

Histone gene expression is a highly conserved phenotype in eukaryotes. In all cases studied in eukaryotes, histone gene synthesis is high during S phase of the cell cycle and substantially lower during other phases. There is a low level of synthesis of replacement histones during non-S phases to support histone turnover. We evaluate the evolutionary dynamics of the S phase regulation by a comparative genomics approach.

Histone gene regulatory sequences were analyzed and the binding sites for known histone transcription regulation were identified and compared using a novel technique. We have identified a dissonance between the evolutionary conservation of the core histone gene regulatory phenotypes and the divergence of their regulatory mechanisms. We will discuss these findings and show, potentially, how this can be applied on a case by case basis to other genes.
Biosketch
David Landsman, Ph.D.

David Landsman is the Chief of the Computational Biology Branch for the National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health. He received his Ph.D. in Biochemistry from the University of Cape Town, South Africa, and has since become an international leader in the field now known as bioinformatics: the management, analysis, and advance of extensive biological and genetics information with computer databasing and modeling technologies. In his role at the NIH, Landsman's research interests include the structure and function of interphase chromatin and nuclei; molecular and cellular interactions controlling gene expression in eukaryotes; recognition and modelling of functional domains in proteins; database design for data aboutbiology; and computational tools for molecular biology. Biologists of all types are faced with a rapidly increasing volume of data which is presented in a variety of forms. The challenge is to identify the answers to specific questions about biology and medicine by utilizing these huge masses of data to understand, formulate, and test hypotheses.