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Evolutionary Analysis: RNA, Genetic Variation, and Computational Models

Explore the evolution of RNA and genetic variation through statistical alignment, association mapping, and combinatorics. Develop computational models to study the evolution of cell cycle dynamics, gene genealogies, and hidden structures within genomes. Collaborate with experts in bioinformatics and systems biology to advance our understanding of evolutionary processes.

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Evolutionary Analysis: RNA, Genetic Variation, and Computational Models

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  1. People Postdocs Rune Lyngsø - computer scientist, RNA and algorithms Istvan Miklos - mathematician, Statistical Alignment Garrett Hellenthal - Geneticist, Association Mapping Shuhei Mano - Mathematical Geneticist Bhalchandra Thatte - Combinatorics Inference of Pedigrees Graduates Rahul Satija - Footprinting and Statistical Alignment Joanna Davies - Integrative Genomics of Asthma Aziz Mithani - Comparison of Metabolic Pathways Marton Munz - Comparative Molecular Dynamics Student Projects Plamen Turmenjev - evolution of cell cycle dynamics Cong Xie - inference of somatic cell phylogeny Stephen Okeefe - MCMC statistical alignment Tian Zuo - MinARGs and Association Mapping Markus GerstelEvolving English Grammar

  2. Unknown ancestral sequences Evolution/Homology: Unknown genealogy Evolutionary model Variation within Species Domain Expansion: Systems and Modeling Themes: Evolution, Variation and Systems

  3. Focus Sequence Evolution & Hidden Structure Hein, Schierup and Wiuf Gene Genealogies, Variation and Evolution Genomes Recombination

  4. Some Projects Theoretical Projects/Methodology Projects (Taylor, Darden,..) The TPS Algorithm & the Evolutionary Path of Protein Structures Evolutionary Docking of Proteins Difficult Concepts in Systems Biology Alternative Splicing: Functionality, Evolution and Selection Collaboration Projects (Kay Davies, Cookson, Peter Simmonds, Iber, Decode) Computational Promoter Analysis of non-Coding RNAs Population Pedigree Inference from Genomic Data Comparative Virus Annotation Parameter and Sensitivity Analysis for Large System of ODEs Systems Biology (Bela Novak, Engineering Systems Biology of the Cell Cycle Evolving Dynamical Systems

  5. Comments +’s: Oxford is clearly great place Extremely good collaborations and connections can be made -’s: We provide ideas, expertise, discussion, criticism, but are not strong on micro-management A very good project & a very good plan for it should be provided The content of a project could be Theoretical/Mathematical/Algorithmic Software engineering Empirical Collaboration Some idea of yours

  6. Outreach • Courses: • Bioinformatics and Computational Biology (undergrad) 16*60 min • DTC LSI & DTC SysBiol (graduate) 12*90 min • Part Time MSc Bioinformatics (masters) • Lecture Series: • 2004 The Human Genome • 2005 Beyond the Human Genome • 2005 Bioinformatics, Systems Biology and the OMICS • 2007 All about Drosophila • Future: Major Diseases and the Model Organisms, The Great Genetic Diseases, The Great Infectious Diseases • Bioinformatics Days: • 2003 Comparative Genomics • 2003 Pathogen Analysis • 2004 Mathematical Modelling and Systems Biology • 2004 Expression Data Analysis • 2005 Regulatory Elements in Eukaryotic Genomes 2005 Bioinformatics and Network Biology 2006 Bioinformatics and Protein Science 2006 Computational Problems in Biology 2007 Genetics of Disease Future: Linguistic Models in Biology and Language, Prostate and Breast Cancer

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