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2MNW/3I/3AI/3PHAR bachelor course Introduction to Bioinformatics Lecture 1 : Introduction

C. E. N. T. E. R. F. O. R. I. N. T. E. G. R. A. T. I. V. E. B. I. O. I. N. F. O. R. M. A. T. I. C. S. V. U. 2MNW/3I/3AI/3PHAR bachelor course Introduction to Bioinformatics Lecture 1 : Introduction. Centre for Integrative Bioinformatics VU (IBIVU)

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2MNW/3I/3AI/3PHAR bachelor course Introduction to Bioinformatics Lecture 1 : Introduction

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  1. C E N T E R F O R I N T E G R A T I V E B I O I N F O R M A T I C S V U 2MNW/3I/3AI/3PHAR bachelor courseIntroduction to BioinformaticsLecture 1: Introduction Centre for Integrative Bioinformatics VU (IBIVU) Faculty of Exact Sciences / Faculty of Earth and Life Sciences http://ibi.vu.nl, heringa@few.vu.nl, 87649 (Heringa), Room R4.41

  2. Other teachers in the course • Anton Feenstra, UD (1/09/05) • Bart van Houte – PhD (1/09/04) • Walter Pirovano – PhD (1/09/05) • Thomas Binsl - PhD (01/06/06) • Sandra Smit - PhD (01/03/07)

  3. Gathering knowledge Rembrandt, 1632 • Anatomy, architecture • Dynamics, mechanics • Informatics (Cybernetics – Wiener, 1948) (Cybernetics has been defined as the science of control in machines and animals, and hence it applies to technological, animal and environmental systems) • Genomics, bioinformatics Newton, 1726

  4. Bioinformatics Chemistry Biology Molecular biology Mathematics Statistics Bioinformatics Computer Science Informatics Medicine Physics

  5. Bioinformatics “Studying informational processes in biological systems” (Hogeweg, early 1970s) • No computers necessary • Back of envelope OK “Information technology applied to the management and analysis of biological data” (Attwood and Parry-Smith) Applying algorithms with mathematical formalisms in biology (genomics) Not good: biology and biological knowledge is crucial for making meaningful analysis methods!

  6. How does information come in? • Modeling a lion leaving its territory due to food shortage • Prey density? What does this mean for the lion? • What else?

  7. Bioinformatics in the olden days • Close to Molecular Biology: • (Statistical) analysis of protein and nucleotide structure • Protein folding problem • Protein-protein and protein-nucleotide interaction • Many essential methods were created early on (1970s - .. ) • Protein sequence analysis (pairwise and multiple alignment) • Protein structure prediction (secondary, tertiary structure) • Protein docking (interaction) prediction

  8. Bioinformatics in the olden days (Cont.) • Evolution was studied and methods created • Phylogeny: evolutionary ancestry • Phylogenetic reconstruction (clustering – e.g., Neighbour Joining (NJ) method)

  9. The citric-acid cycle b) Individual species mightnot have a complete CAC. This diagram shows the genes for the CAC for each unicellularspecies for which a genome sequence has been published, together with the phylogeny ofthe species. The distance-based phylogeny was constructed using the fraction of genesshared betweengenomes as a similarity criterion29. The major kingdoms of life are indicatedin red (Archaea), blue (Bacteria) and yellow (Eukarya). Question marks representreactions for which there is biochemical evidence in the species itself or in a relatedspecies but for which no genes could be found. Genes that lie in a single operon areshown in the same color.Genes were assumed to be located in a single operon whenthey were transcribed in the same direction and the stretches of non-coding DNA separatingthem were less than 50 nucleotides in length. M. A. Huynen, T. Dandekar and P. Bork ``Variation and evolution of thecitric acid cycle: a genomic approach'' Trends Microbiol, 7, 281-29 (1999)

  10. But then the big bang….

  11. The Human Genome -- 26 June 2000

  12. The Human Genome -- 26 June 2000 “Without a doubt, this is the most important, most wondrous map ever produced by humankind.” U.S. President Bill Clinton on 26 June 2000 during a press conference at the White House.

  13. The Human Genome -- 26 June 2000 Francis Collins (USA) / Sir John Sulston (UK) Human Genome Project Dr. Craig Venter Celera Genomics -- Shotgun method

  14. Human DNA • There are at least 3bn (3  109) nucleotides in the nucleus of almost all of the trillions (~5  1012 ) of cells of a human body (an exception is, for example, red blood cells which have no nucleus and therefore no DNA) – a total of ~1022 nucleotides! • Many DNA regions code for proteins, and are called genes (1 gene codes for 1 protein as a base rule, but the reality is a lot more complicated) • Human DNA contains ~26,000 expressed genes • Deoxyribonucleic acid (DNA) comprises 4 different types of nucleotides: adenine (A), thiamine (T), cytosine (C) and guanine (G). These nucleotides are sometimes also called bases

  15. Human DNA (Cont.) • All people are different, but the DNA of different people only varies for 0.1% or less. Evidence in current genomics studies (Single Nucleotide Polymorphisms or SNPs) imply that on average only 1 nucleotide out of about 1400 is different between individuals. Over the whole genome, this means that 2 to 3 million letters would differ between individuals. • The structure of DNA is the so-called double helix, discovered by Watson and Crick in 1953, where the two helices are cross-linked by A-T and C-G base-pairs (nucleotide pairs – so-called Watson-Crick base pairing).

  16. Modern bioinformatics is closely associated with genomics • The aim is to solve the genomics information problem • Ultimately, this should lead to biological understanding how all the parts fit (DNA, RNA, proteins, metabolites) and how they interact (gene regulation, gene expression, protein interaction, metabolic pathways, protein signalling, etc.) • Genomics will result in the “parts list” of the genome

  17. Functional GenomicsFrom gene to function Genome Expressome Proteome TERTIARY STRUCTURE (fold) Metabolome TERTIARY STRUCTURE (fold)

  18. Systems Biology is the study of the interactions between the components of a biological system, and how these interactions give rise to the function and behaviour of that system (for example, the enzymes and metabolites in a metabolic pathway). The aim is to quantitatively understand the system and to be able to predict the system’s time processes • the interactions are nonlinear • the interactions give rise to emergent properties, i.e. properties that cannot be explained by the components in the system

  19. Systems Biology understanding is often achieved through modeling and simulation of the system’s components and interactions. Many times, the ‘four Ms’ cycle is adopted: Measuring Mining Modeling Manipulating

  20. A system response Apoptosis: programmed cell death Necrosis: accidental cell death

  21. Translational Medicine • “From bench to bed side” • Genomics data to patient data • Integration

  22. Neuroinformatics • Understanding the human nervous system is one of the greatest challenges of 21st century science. • Its abilities dwarf any man-made system - perception, decision-making, cognition and reasoning. • Neuroinformatics spans many scientific disciplines - from molecular biology to anthropology.

  23. Neuroinformatics • Main research question: How does the brain and nervous system work? • Main research activity: gathering neuroscience data, knowledge and developing computational models and analytical tools for the integration and analysis of experimental data, leading to improvements in existing theories about the nervous system and brain. • Results for the clinic:Neuroinformatics provides tools, databases, models, networks technologies and models for clinical and research purposes in the neuroscience community and related fields.

  24. Introduction to Bioinformatics course content • Pattern recognition • Supervised/unsupervised learning • Types of data, data normalisation, lacking data • Search image • Similarity/distance measures • Clustering • Principal component analysis

  25. Introduction to Bioinformatics course content • Protein • Folding • Structure and function • Protein structure prediction • Secondary structure • Tertiary structure • Function • Post-translational modification • Prot.-Prot. Interaction -- Docking algorithm • Molecular dynamics/Monte Carlo

  26. Introduction to Bioinformatics course content • Sequence analysis • Pairwise alignment • Dynamic programming (NW, SW, shortcuts) • Multiple alignment • Combining information • Database/homology searching (Fasta, Blast, Statistical issues-E/P values)

  27. Introduction to Bioinformatics course content • Gene structure and gene finding algorithms • Genomics • Sequencing projects • Expression data, Nucleus to ribosome, translation, etc. • Proteomics, Metabolomics, Physiomics • Databases • DNA, EST • Protein sequence (SwissProt) • Protein structure (PDB) • Microarray data • Proteomics • Mass spectrometry/NMR/X-ray

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