Course in Bioinformatics

Bio Infomatics Course : Bioinformatics involves the application of computational methods in order to address problems in molecular biology. This course will provide a graduate introduction to algorithms and their applications in bioinformatics. Topics in molecular biology that will motivate the algorithmic content of the course include: sequence alignment, phylogenetic tree reconstruction, prediction of RNA and protein structure, gene finding and sequence annotation, gene expression, and biomolecular computing. This is an interdisciplinary course, and the goal is to involve students who have either a strong computer science background or a strong background in molecular biology (such as students in the genetics graduate program), but not necessarily both. It is understood that students from these groups will have different skills and experience, and course lectures and assignments will take this into account. However, all students should already have a solid background in computer programming and should be comfortable with mathematical reasoning, such as can be obtained in a college level course in Mathematics or Statistics. Background in discrete mathematics or in probability theory is especially relevant to the course content. Introduction and Basics * basics of molecular biology o foundations of molecular genetics: DNA, RNA, proteins; transcription, translation, regulation; genome organisation, sexual and asexual reproduction o cellular organisation, metabolism, regulatory pathways o tree of life, phylogeny o >modern biochemical techniques: cloning, DNA and protein sequencing, genome mapping, PCR, mutagenesis; in-vivo / in-vitro / in-silico o biological models and formalisms: exceptions are the rule * problems in bioinformatics: overview o sequencing genomes: physical mapping, genome structure o interpreting genomic sequence data: sequence alignment, gene finding, structure prediction (rna and proteins), pattern discovery o understanding the cell / organisms: regulatory pathways and networks, simulations o relations between organisms and evolutionary questions: phylogenetic trees, computational models of evolution, simulations o biomolecular computing: dna computing, inverse folding, self-assembling structures o bioinformatics: tools vs. synergistic research Phylogenetic Trees * Intro and common algorithms o formulation of the tree reconstruction problem o parsimony: a sequence-based algorithm o distance-based algorithms * Probabilistic models and associated algorithms * probabilistic models of evolution; * the maximum likelihood algorithm * experimental studies of phylogenetic tree algorithms # Biological perspectives [Wo98]; current research directions * the universal tree of life * properties of molecular sequence data used to construct trees * fit between models of evolution and real evolution * synopsis of other topics in phylogeny and current research questions