logging in or signing up Industriakademin 2005 10 27 Hillary Download Post to : URL : Related Presentations : Share Add to Flag Embed Email Send to Blogs and Networks Add to Channel Uploaded from authorPOINTLite Insert YouTube videos in PowerPont slides with aS Desktop Copy embed code: (To copy code, click on the text box) Embed: URL: Thumbnail: WordPress Embed Customize Embed The presentation is successfully added In Your Favorites. Views: 23 Category: Education License: All Rights Reserved Like it (0) Dislike it (0) Added: February 06, 2008 This Presentation is Public Favorites: 0 Presentation Description No description available. Comments Posting comment... Premium member Presentation Transcript Utmaningar för bioinformatiken inom industri och akademi: Utmaningar för bioinformatiken inom industri och akademi Per Kraulis Biovitrum AB Industriakademin, 27 okt 2005Landskapet 1: Landskapet 1 Homo sapiens genomet klart (nåja) Flera mammalie-genom, i användbart skick 1000 bakterie-genom (snart) Genidentifiering, annotering Proteinkodande gener identifierade Sköts av stora centra: inget för mindre grupper RNA-gener och reglering: mycket att göraLandskapet 2: Landskapet 2 Expressionsanalys Mognande teknik och analysmetoder Nya applikationer/analyser? Proteomik Stora dataset, nya typer av data Ex: HPR www.proteinatlas.org Mycket att göraLandskapet 3: Landskapet 3 Nätverksanalys Signalering Metabola nätverk Databaser, litteratur (text mining) Systembiologi Modeller av mekanismer, simulering Förklaring av förlopp Men: Prediktion! Varför så skralt?Förändring av fokus: Förändring av fokus Struktur genom gener proteinsekvenser proteindomäner Processer förlopp nätverk signallering metabolism BioinformaticsIntegrated Pharmacology, Biovitrum: Bioinformatics Integrated Pharmacology, Biovitrum Sequence analysis Homologs Orthologs Splice variants Expression patterns Pathway analysis System effects Study design Readouts Biomarkers Drug discovery pipeline Target Lead Animal models In vivo studies CD In vitro ClinicalTes 1: Sekvensorienterad bioinformatik är rutin: Tes 1: Sekvensorienterad bioinformatik är rutin Annotering finns i publika DB Verktyg finns tillgängliga Få uppenbara möjligheter till “lyft” Undantag RNA Fylogenetiska jämförelserTes 2: Vissa behov ej uppfyllda: Tes 2: Vissa behov ej uppfyllda Saknas: Annoteringssystem för små grupper med intresse för specifika gener/system Existerande produkter är “imperialistiska” Idéer: Modell: Dossier eller ‘best current view’ Editera: web eller specialverktyg Läs: web eller PDFTes 3: Nya ‘drug targets’ från biologi, funktion (inte sekvens): Tes 3: Nya ‘drug targets’ från biologi, funktion (inte sekvens) ‘Drug target hunting’ är passerat kapitel Tillbaka till cellbiologi, farmakologi, mm Hur kan bioinfo hjälpa experimentalisten? Ordna, systematisera litteraturen Designa experiment Välja ‘read-outs’ Handskas med data (DB motsv)Tes 4: (Bio)informatik krävs för systembiologi: Tes 4: (Bio)informatik krävs för systembiologi Mekanistiska modeller standard (nåja) SBML, Reactome, KEGG, etc Men förloppen som ska simuleras? Datamodeller/databaser saknas! Initialvärden, randvillkor Kontext Dynamiska förändringarTes 5: Bioinformatiken måste ta sig an biologiska förlopp: Tes 5: Bioinformatiken måste ta sig an biologiska förlopp Förlopp (processer) är biologins hjärta Den temporala aspekten är central Ex: Vad händer när en cell stimuleras? Ex: Cell-cykeln: vilka komponenter, processer? Få databaser/datamodeller! Jmf: Geographical Information Systems (GIS), temporala aspekter forskas kring sedan 15 årProto-Systems biology?: Proto-Systems biology? If sufficient regularity can be found between molecular entities and logical and informational outcomes to allow appropriate databases to be built, then genomic and post-genomic data could be interrogated more effectively. … If successful, this approach would not require detailed kinetic analyses of all processes within cells, but rather rely on more cursory calculations to study phenomena of interest. Paul Nurse, ”Understanding cells”, Nature (424) 2003, 883.Computable information: Computable information Free text Ontology Relational DB Keyword/value data Petri Net, logic Statecharts, UML Annotated text Unwritten Diff equ model SimulationMultiple levels and types in biology: Multiple levels and types in biology Objects Molecules Complexes Compartments Cells Events Reactions Transport Signals Processes Millán & Ridley (2005)TheoryExplanationPrediction: (Computable) information Theory Explanation PredictionThe predictions…: The predictions… Roles of uncharacterized components Behavior after perturbation Suggest points of intervention; drug targetsPart 1: Molecular data: Part 1: Molecular data Molecular components Genomics, transcriptomics, proteomics, etc Molecular events Interactions Modifications Localisation Explicit data model required for DB!Part 2: Macroscopic processes: Part 2: Macroscopic processes Describe macroscopic processes Simulations must be compared with something Goal-oriented description? What is required to achieve a specific state? Life processes as projects Goals, milestones Resource usage; scheduling Subprojects, tasksGeneCV concepts: GeneCV concepts Entities Genes Proteins Molecules Complexes States Complexes, member of Modifications Location Transitions Creation Destruction Interactions Regulation Transport Ras p21 Chem: unmodified Location: cytosol Ras p21 Chem: farnesylated Location: membraneStatecharts: Statecharts David Harel, 1987 Describe reactive computer systems Event-driven Responding to external and internal stimuli State-transition diagrams extended with: Hierarchy Orthogonality Communication Now part of UMLStatecharts: states and events: Statecharts: states and events On Off Error ResetStatecharts: state hierarchy: Statecharts: state hierarchyStatecharts: state orthogonality: Statecharts: state orthogonality Active On Off Error Reset Mode DebugStatecharts: conditions: Statecharts: conditions Debug_command [User_is_admin]Modeling T-cell transformationsKam, Cohen, Harel 2001: Modeling T-cell transformations Kam, Cohen, Harel 2001Example: Lysine post-transl mod's: Example: Lysine post-transl mod'swww.reactome.org: www.reactome.org CSHL, EBI, GO collaboration Entities Generic/concrete No explicit state; no hierarchy of states Events Hierarchy Molecular as well as macroscopic (processes)www.signaling-gateway.org: www.signaling-gateway.org Alliance for Cell Signaling, AfCS Molecules Proteins States No hierarchy Molecular only; complexes are states Location is not state Transitions Conditions?GeneCV: GeneCV The life of a biomolecule Molecular data only! Creation Maturation Transport Interactions Destruction Mendenhall & Hodge 1998 You do not have the permission to view this presentation. In order to view it, please contact the author of the presentation.
Industriakademin 2005 10 27 Hillary Download Post to : URL : Related Presentations : Share Add to Flag Embed Email Send to Blogs and Networks Add to Channel Uploaded from authorPOINTLite Insert YouTube videos in PowerPont slides with aS Desktop Copy embed code: (To copy code, click on the text box) Embed: URL: Thumbnail: WordPress Embed Customize Embed The presentation is successfully added In Your Favorites. Views: 23 Category: Education License: All Rights Reserved Like it (0) Dislike it (0) Added: February 06, 2008 This Presentation is Public Favorites: 0 Presentation Description No description available. Comments Posting comment... Premium member Presentation Transcript Utmaningar för bioinformatiken inom industri och akademi: Utmaningar för bioinformatiken inom industri och akademi Per Kraulis Biovitrum AB Industriakademin, 27 okt 2005Landskapet 1: Landskapet 1 Homo sapiens genomet klart (nåja) Flera mammalie-genom, i användbart skick 1000 bakterie-genom (snart) Genidentifiering, annotering Proteinkodande gener identifierade Sköts av stora centra: inget för mindre grupper RNA-gener och reglering: mycket att göraLandskapet 2: Landskapet 2 Expressionsanalys Mognande teknik och analysmetoder Nya applikationer/analyser? Proteomik Stora dataset, nya typer av data Ex: HPR www.proteinatlas.org Mycket att göraLandskapet 3: Landskapet 3 Nätverksanalys Signalering Metabola nätverk Databaser, litteratur (text mining) Systembiologi Modeller av mekanismer, simulering Förklaring av förlopp Men: Prediktion! Varför så skralt?Förändring av fokus: Förändring av fokus Struktur genom gener proteinsekvenser proteindomäner Processer förlopp nätverk signallering metabolism BioinformaticsIntegrated Pharmacology, Biovitrum: Bioinformatics Integrated Pharmacology, Biovitrum Sequence analysis Homologs Orthologs Splice variants Expression patterns Pathway analysis System effects Study design Readouts Biomarkers Drug discovery pipeline Target Lead Animal models In vivo studies CD In vitro ClinicalTes 1: Sekvensorienterad bioinformatik är rutin: Tes 1: Sekvensorienterad bioinformatik är rutin Annotering finns i publika DB Verktyg finns tillgängliga Få uppenbara möjligheter till “lyft” Undantag RNA Fylogenetiska jämförelserTes 2: Vissa behov ej uppfyllda: Tes 2: Vissa behov ej uppfyllda Saknas: Annoteringssystem för små grupper med intresse för specifika gener/system Existerande produkter är “imperialistiska” Idéer: Modell: Dossier eller ‘best current view’ Editera: web eller specialverktyg Läs: web eller PDFTes 3: Nya ‘drug targets’ från biologi, funktion (inte sekvens): Tes 3: Nya ‘drug targets’ från biologi, funktion (inte sekvens) ‘Drug target hunting’ är passerat kapitel Tillbaka till cellbiologi, farmakologi, mm Hur kan bioinfo hjälpa experimentalisten? Ordna, systematisera litteraturen Designa experiment Välja ‘read-outs’ Handskas med data (DB motsv)Tes 4: (Bio)informatik krävs för systembiologi: Tes 4: (Bio)informatik krävs för systembiologi Mekanistiska modeller standard (nåja) SBML, Reactome, KEGG, etc Men förloppen som ska simuleras? Datamodeller/databaser saknas! Initialvärden, randvillkor Kontext Dynamiska förändringarTes 5: Bioinformatiken måste ta sig an biologiska förlopp: Tes 5: Bioinformatiken måste ta sig an biologiska förlopp Förlopp (processer) är biologins hjärta Den temporala aspekten är central Ex: Vad händer när en cell stimuleras? Ex: Cell-cykeln: vilka komponenter, processer? Få databaser/datamodeller! Jmf: Geographical Information Systems (GIS), temporala aspekter forskas kring sedan 15 årProto-Systems biology?: Proto-Systems biology? If sufficient regularity can be found between molecular entities and logical and informational outcomes to allow appropriate databases to be built, then genomic and post-genomic data could be interrogated more effectively. … If successful, this approach would not require detailed kinetic analyses of all processes within cells, but rather rely on more cursory calculations to study phenomena of interest. Paul Nurse, ”Understanding cells”, Nature (424) 2003, 883.Computable information: Computable information Free text Ontology Relational DB Keyword/value data Petri Net, logic Statecharts, UML Annotated text Unwritten Diff equ model SimulationMultiple levels and types in biology: Multiple levels and types in biology Objects Molecules Complexes Compartments Cells Events Reactions Transport Signals Processes Millán & Ridley (2005)TheoryExplanationPrediction: (Computable) information Theory Explanation PredictionThe predictions…: The predictions… Roles of uncharacterized components Behavior after perturbation Suggest points of intervention; drug targetsPart 1: Molecular data: Part 1: Molecular data Molecular components Genomics, transcriptomics, proteomics, etc Molecular events Interactions Modifications Localisation Explicit data model required for DB!Part 2: Macroscopic processes: Part 2: Macroscopic processes Describe macroscopic processes Simulations must be compared with something Goal-oriented description? What is required to achieve a specific state? Life processes as projects Goals, milestones Resource usage; scheduling Subprojects, tasksGeneCV concepts: GeneCV concepts Entities Genes Proteins Molecules Complexes States Complexes, member of Modifications Location Transitions Creation Destruction Interactions Regulation Transport Ras p21 Chem: unmodified Location: cytosol Ras p21 Chem: farnesylated Location: membraneStatecharts: Statecharts David Harel, 1987 Describe reactive computer systems Event-driven Responding to external and internal stimuli State-transition diagrams extended with: Hierarchy Orthogonality Communication Now part of UMLStatecharts: states and events: Statecharts: states and events On Off Error ResetStatecharts: state hierarchy: Statecharts: state hierarchyStatecharts: state orthogonality: Statecharts: state orthogonality Active On Off Error Reset Mode DebugStatecharts: conditions: Statecharts: conditions Debug_command [User_is_admin]Modeling T-cell transformationsKam, Cohen, Harel 2001: Modeling T-cell transformations Kam, Cohen, Harel 2001Example: Lysine post-transl mod's: Example: Lysine post-transl mod'swww.reactome.org: www.reactome.org CSHL, EBI, GO collaboration Entities Generic/concrete No explicit state; no hierarchy of states Events Hierarchy Molecular as well as macroscopic (processes)www.signaling-gateway.org: www.signaling-gateway.org Alliance for Cell Signaling, AfCS Molecules Proteins States No hierarchy Molecular only; complexes are states Location is not state Transitions Conditions?GeneCV: GeneCV The life of a biomolecule Molecular data only! Creation Maturation Transport Interactions Destruction Mendenhall & Hodge 1998