Systems Biology (1) Introduction David Gilbert Bioinformatics Research Centre www.brc.dcs.gla.ac.uk Department of Computing Science, University of Glasgow
Systems Biology lectures outline • ‘Putting it all together’ - Systems Biology • Motivation • Biological background • Modelling – Network Models – Data models • Analysis: – Static – Dynamic • Standardisation (sbml & sbw) • Technologies • Current approaches • Systems robustness (c) David Gilbert, 2007 Systems Biology Introduction 2
Resources • DRG’s handouts • www.brc.dcs.gla.ac.uk/~drg/bioinformatics/resources.html • www.ebi.ac.uk/2can – Bioinformatics educational resource at the EBI • International Society for Computational Biology: www.iscb.org – very good rates for students, and you get on-line access to the Journal of Bioinformatics. • Broder S, Venter J C, Whole genomes: the foundation of new biology and medicine, Curr Opin Biotechnol. 2000 Dec;11(6):581-5. • Kitano H. Looking beyond the details: a rise in system-oriented approaches in genetics and molecular biology. Curr Genet. 2002 Apr;41(1):1-10. • Milo R, Shen-Orr S, Itzkovitz S, Kashtan N, Chklovskii D, Alon U. Network motifs: simple building blocks of complex networks. Science. 2002 Oct 25;298(5594):824-7. • Yuri Lazebnick. Can a biologist fix a radio? - Or, What I learned while studying Apoptosis. Cancer Cell september 2002 vol 2 179-182. • Post Genome Informatics Kanehisa. Publisher OUP. Year 2000. Isbn 0198503261. Category background (c) David Gilbert, 2007 Systems Biology Introduction 3
Introductory lecture outline • ‘Putting it all together’ - Systems Biology • Motivation • Technological drivers • Some biological background • Introduction to some (systems biology) databases (c) David Gilbert, 2007 Systems Biology Introduction 4
Motivation • The amount and variety of biological data now available, together with techniques developed so far have enabled research in Bioinformatics to move beyond the study of individual biological components (genes, proteins etc) – albeit in a genome-wide context – to attempt to study how individual parts cooperate in their operation. • Bioinformatics as a scientific activity has now moved closer to the area of Systems Biology which seeks to integrate biological data as an attempt to understand how biological systems function. • By studying the relationships and interactions between various parts of a biological system it is hoped that an understandable model of the whole system can be developed. (c) David Gilbert, 2007 Systems Biology Introduction 5
Central Dogma • The central dogma of information flow in biology essentially states that the sequence of amino acids making up a protein and hence its structure (folded state) and thus its function, is determined by transcription from DNA via RNA. • “This states that once ‘information’ has passed into protein it cannot get out again. In more detail, the transfer of information from nucleic acid to nucleic acid, or from nucleic acid to protein may be possible, but transfer from protein to protein, or from protein to nucleic acid is impossible. Information means here the precise determination of sequence, either of bases in the nucleic acid or of amino acid residues in the protein.” Francis Crick, On Protein Synthesis, in Symp. Soc. Exp. Biol. XII, 138-167 (1958) • (Nothing said explicitly about transfer from RNA to DNA) (c) David Gilbert, 2007 Systems Biology Introduction 6
Behaviour of the gene … (c) David Gilbert, 2007 Systems Biology Introduction 7
… their interaction (c) David Gilbert, 2007 Systems Biology Introduction 8
Genes to systems DNA "gene" mRNA Protein sequence Folded Protein (c) David Gilbert, 2007 Systems Biology Introduction 9
Terminology: Pathways or Networks? • Pathways implies ‘paths’ - sequences of objects • Networks - more complex connectivity • Both are represented by graphs • Networks: generic; Pathways: specific (?) – ‘Signal transduction networks’ – ‘The ERK signal transduction pathway’ (c) David Gilbert, 2007 Systems Biology Introduction 10
Networks • Gene regulation • Protein-protein interaction • Metabolic • Developmental • Signalling (c) David Gilbert, 2007 Systems Biology Introduction 11
Gene regulation (c) David Gilbert, 2007 Systems Biology Introduction 12
Biochemical networks We can describe the general topology and single biochemical steps. However, we do not understand the network function as a whole. Receptor e.g. 7-TMR cell membrane γ γ α tyrosine Ras AdCyc α β β shc SOS kinase Ras cAMP Akt heterotrimeric grb2 Rac G-protein Rap1 GEF ATP cAMP PI-3 cAMP Raf-1 cAMP K PKA cAMP PAK B-Raf AMP cAMP PKA MEK PDE MEK1,2 ERK1,2 ERK1,2 cytosol MKP transcription factors What happens? nucleus Why does it happen ? How is specificity achieved? (c) David Gilbert, 2007 Systems Biology Introduction 13
ERK signalling pathway Mitogens Growth factors receptor Receptor kinase Ras P P P Raf P P MEK P P ERK cytoplasmic substrates Elk AP1 Gene (c) David Gilbert, 2007 Systems Biology Introduction 14
Signal Transduction (c) David Gilbert, 2007 Systems Biology Introduction 15
Protein-protein interaction in yeast (c) David Gilbert, 2007 Systems Biology Introduction 16
Protein-protein interaction (c) David Gilbert, 2007 Systems Biology Introduction 17
Developmental pathway (c) David Gilbert, 2007 Systems Biology Introduction 18
Human Genome (c) David Gilbert, 2007 Systems Biology Introduction 19
After Human Genome Project (HGP) The Seven (7) ways the HGP has impacted biology (Hood, 2002) • Biology is an informational science • Discovery science enhances global analyses • A generic parts list provides a toolbox of genetic elements for systems analyses • High-throughput platforms permit one to carry out global analyses at the DNA, RNA, and protein levels • Computational, Mathematical, and Statistical tools are essential for handling the explosion of biological information • Model organisms are Rosetta stones for deciphering biological information • Comparative genomics is a key to deciphering biological complexity Each of these seven changes has catalysed the emergence of systems biology (c) David Gilbert, 2007 Systems Biology Introduction 20
More genomes …... Arabidopsis Buchnerasp. Yersinia Aquifex Archaeoglobus Borrelia Mycobacterium thaliana pestis APS aeolicus fulgidus burgorferi tuberculosis Vibrio Caenorhabitis Thermoplasma Campylobacter Chlamydia Drosophila Escherichia cholerae elegans jejuni pneumoniae melanogaster coli acidophilum Neisseria Plasmodium Ureaplasma Helicobacter Mycobacterium Pseudomonas mouse meningitidis falciparum urealyticum pylori leprae aeruginosa Z2491 Bacillus Thermotoga Xylella Rickettsia Saccharomyces Salmonella rat subtilis prowazekii cerevisiae enterica maritima fastidiosa (c) David Gilbert, 2007 Systems Biology Introduction 21
Whole genomes • Our genomic DNA sequence provides a unique glimpse of the provenance and evolution of our species, the migration of peoples, and the causation of disease. • Understanding the genome may help resolve previously unanswerable questions, including perhaps which human characteristics are innate or acquired. • Such an understanding will make it possible to study how genomic DNA sequence varies among populations and among individuals, including the role of such variation in the pathogenesis of important illnesses and responses to pharmaceuticals. • The study of the genome and the associated proteomics of free-living organisms will eventually make it possible to localize and annotate every human gene, as well as the regulatory elements that control the timing, organ-site specificity, extent of gene expression, protein levels, and post- translational modifications. • For any given physiological process, we will have a new paradigm for addressing its evolution, development, function, and mechanism. • Broder S, Venter J C, Whole genomes: the foundation of new biology and medicine, Curr Opin Biotechnol. 2000 Dec;11(6):581-5. (c) David Gilbert, 2007 Systems Biology Introduction 22
Database Growth EMBL - sequences PDB protein structures Data deluge is an PDB - structures URBAN MYTH??? DBs growing exponentially!!! •Biobliographic (MedLine, …) •Amino Acid Seq (SWISS-PROT, …) •3D Molecular Structure (PDB, …) •Nucleotide Seq (GenBank, EMBL, …) •Biochemical Pathways (KEGG, WIT…) •Molecular Classifications (SCOP, CATH,…) •Motif Libraries (PROSITE, Blocks, …) (c) David Gilbert, 2007 Systems Biology Introduction 23
Nucleotide sequences The Complexity of Biological Data (c) David Gilbert, 2007 Nucleotide structures Gene expressions Protein Structures Protein functions Protein-protein interaction (pathways) Systems Biology Introduction l C e l Cell signalling Tissues Organs Physiology Organisms 24
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