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Falk Schreiber - From big data to smart knowledge ‐ integrating multimodal biological data and modelling metabolism
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Falk Schreiber - From big data to smart knowledge ‐ integrating multimodal biological data and modelling metabolism

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Modern data acquisition methods in the life sciences allow the procurement of different types of data in increasing quantity, facilitating a comprehensive view of biological systems. As data is …

Modern data acquisition methods in the life sciences allow the procurement of different types of data in increasing quantity, facilitating a comprehensive view of biological systems. As data is usually gathered and interpreted by separate domain scientists, it is hard to grasp multi‐domain properties and structures. Consequently there is a need for the integration, analysis, modelling, simulation, and visualisation of life science data from different sources and of different types.
This talk focuses on these two aspects: firstly, methods for the integration and visualization of multimodal biological data are presented. This is achieved based on two graphs representing the meta‐relations between biological data, and the measurement combinations, respectively. Both graphs are linked and serve as different views of the integrated data with navigation and exploration possibilities. Data can be combined and visualised multifariously, resulting in views of the integrated biological data. Secondly, methods to reconstruct, simulate, and analyse detailed metabolic models are presented. We will focus on stoichiometric models, and see how different types of data are used to gather new insights into metabolic processes shown on an example of metabolism in plants.

First presented at the 2014 Winter School in Mathematical and Computational Biology http://bioinformatics.org.au/ws14/program/

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  • 1. Martin Luther University Halle-Wittenberg Falk Schreiber From Big Data to Smart Knowledge Integrating Multimodal Biological Data and Modelling Metabolism 14/07/2014 1 Leibniz Institute IPK Gatersleben
  • 2. Observations 1.  A tidal wave of scientific data
  • 3. Observations 1.  A tidal wave of scientific data Year Time Costs (Mio. US$) 2003 13 years 2700 2007 a few months 1 2009 a few weeks 0,05 2014 a few days 0,001 ~2017 cheaper to reproduce data than storing it
  • 4. Observations 1.  A tidal wave of scientific data 2.  From building blocks to complex systems genes transcripts proteins metabolites reductionistapproach
  • 5. Observations 1.  A tidal wave of scientific data 2.  From building blocks to complex systemsreductionistapproach integrativeapproach genes transcripts proteins metabolites
  • 6. Observations 1.  A tidal wave of scientific data 2.  From building blocks to complex systems 3.  Multi-domain data
  • 7. Observations 1.  A tidal wave of scientific data 2.  From building blocks to complex systems 3.  Multi-domain data
  • 8. From Data to Knowledge – Outline of the Talk Understanding metabolism via modelling
  • 9. From Data to Knowledge – Outline of the Talk Understanding metabolism via modelling Integrating and exploring multimodal biological data
  • 10. !  Network of thousands of biochemical reactions ! Enzyme-catalysed ! Transporter-mediated !  Supports all biological activity !  Metabolic model = List of reactions + associated information Metabolism Source: http://www.genome.jp/kegg/Source: Michael 1993
  • 11. + kinetic rate laws + kinetic parameters Topological analysis network structure Petri net (P/T) analysis + thermodynamics + stoichiometry Flux balance analysis (FBA) + mass balance + capacity constraints + stochastic rate laws + metabolite concentrations Kinetic modelling Petri net (SPN) analysis Metabolic Models Sizeofmodel Levelofdetail
  • 12. + kinetic rate laws + kinetic parameters Topological analysis network structure Petri net (P/T) analysis + thermodynamics + stoichiometry Flux balance analysis (FBA) + mass balance + capacity constraints + stochastic rate laws + metabolite concentrations Kinetic modelling Petri net (SPN) analysis Metabolic Models Sizeofmodel Levelofdetail
  • 13. Flux Balance Analysis !  Constraint-based stoichiometric modelling approach to predict and analyse the metabolic steady state conversion rates (fluxes) !  Advantages ! No kinetic parameters required ! Quantitative predictions ! Applicable to large systems !  Applications ! Prediction of optimal metabolic yields and flux distributions ! Prediction of phenotype/viability of knockout-mutants ! Prediction of pathway redundancies ! And more
  • 14. Principles of Flux Balance Analysis
  • 15. Simulation Oxygene level
  • 16. Objective Function How to identify plausible physiological states? Question Objective What are the biochemical production capabilities? Maximise metabolite product What is the maximal growth rate and biomass yield? Maximise growth rate How efficiently can metabolism channel metabolites through the network? Minimise the Euclidean norm What is the tradeoff between biomass production and metabolite overproduction? Maximise biomass production for a given metabolite production How energetically efficient can metabolism operate? Minimise ATP production or minimise nutrient uptake
  • 17. History of FBA
  • 18. Software Tools and Pipelines for FBA !  CellNetAnalyzer (CNA) http://www.mpi-magdeburg.mpg.de/projects/cna/cna.html !  COBRA Toolbox http://gcrg.ucsd.edu/downloads/COBRAToolbox !  FBA-SimVis http://fbasimvis.ipk-gatersleben.de !  Thiele et al. A protocol for generating a high-quality genome- scale metabolic reconstruction. Nature Protocols, 5(1): 93–121, 2010. !  Grafahrend-Belau et al. Plant metabolic pathways: databases and pipeline for stoichiometric analysis. In Agrawal and Rakwal (Eds.), Seed development: omics technologies toward improvement of seed quality and crop yield, Springer, 345-366, 2012.
  • 19. FBA Model of seed Metabolism in Hordeum vulgare Grafahrend-Belau et al. Plant Physiology, 2009
  • 20. FBA Model of seed Metabolism in Hordeum vulgare Grafahrend-Belau et al. Plant Physiology, 2009 Size 257 reactions, 234 metabolites Pathways Glyc, TCA, PPP, oxP, Ferm, Rubisco, AA, Starch, CW, and others
  • 21. Example of Model Application !  Imaging uncovers metabolic compartmentation !  Alanine synthesis mainly in central endosperm, alanine gradient reflects the local oxygen state !  Modelling purpose: elucidate the role of alanine metabolism Source of images: L. Borisjuk and H. Rolletschek, IPK Melkus et al. Plant Biotechnology Journal, 2011 Rolletschek et al. Plant Cell, 2011
  • 22. Simulation of Region-specific Metabolism A B Central endosperm (hypoxic) Peripheral endosperm (aerobic) Melkus et al. Plant Biotechnology Journal, 2011 Rolletschek et al. Plant Cell, 2011
  • 23. Simulation of Region-specific Metabolism A B Central endosperm (hypoxic) Peripheral endosperm (aerobic) Melkus et al. Plant Biotechnology Journal, 2011 Rolletschek et al. Plant Cell, 2011
  • 24. Obtaining Parameters !  Influx ! Quantification from video data !  Relation of substances in the same area ! Multimodal alignment Scharfe et al. BMC Bioinformatics, 2010 Fester et al. GCB, 2009 !  Biomass accumulation ! Quantification from image series Hartmann et al. BMC Bioinformatics, 2011
  • 25. Scaling up - Multi* and High Throughput Modelling
  • 26. Coupling of Organ-specific FBA Models
  • 27. Coupling of FBA and FSA Models Müller et al. IEEE PMA, 2012 Grafahrend-Belau et al. Plant Physiology, 2013
  • 28. High Throughput Modelling !  Path2Models: A pipeline to compute draft models !  >140.000 kinetic, logical and constraint-based models Le Novère et al. BMC Systems Biology, 2013
  • 29. High Throughput Modelling !  Path2Models: A pipeline to compute draft models !  >140.000 kinetic, logical and constraint-based models Le Novère et al. BMC Systems Biology, 2013
  • 30. From Data to Knowledge – Outline of the Talk Understanding metabolism via modelling Integrating and exploring multimodal biological data
  • 31. Multi-domain Biological Data
  • 32. Data Domains
  • 33. Available Tools
  • 34. Data Integration – A Major Problem (Example: Networks)
  • 35. !  Bridge the abyss! Data Integration – A Major Problem (Example: Networks)
  • 36. !  Many information resources can be utilized as IDMappers: ! Web services, web sites (e.g. PICR, CRONOS, …) ! Relational databases (e.g. STRING, PDD, …) ! Flat files (e.g. Kegg, UniProt, …) Overview: Mehlhorn et al. TransID – the flexible identifier mapping service 112-121 (Internat. Symp. Integrative Bioinformatics), 2013. !  Unified using the BridgeDB framework IDMappers
  • 37. !  Comprises a set of identifiers (nodes) and a set of identifier mappings (edges) !  Used to explore identifier interconnections !  Basis of the integration of biological networks !  Example The Data Linkage Graph (Tair) (UniProt) (EC number)
  • 38. !  Composed of biological networks and the inferred identifier mappings as mapping edges !  Mapping edges represent identifier connections in the data linkage graph !  Example The Integrated Graph Data linkage graph Integrated graph
  • 39. !  Metabolic pathways: Glycolysis, Pyruvate metabolism from KEGG !  Gene regulatory network: Arabidopsis thaliana from Regulogs Example
  • 40. Example
  • 41. Available Tools
  • 42. Data, Mappings and Mapping Function !  Set of measurements !  Mappings with the object path functions which derives the relevant metadata and any set of graph element attributes !  Basis: ID Mappers 𝑚 𝑚 𝑚 𝑚 Rohn et al. Bioinformatics, 2011
  • 43. Example of Integrated Data http://www.vanted.org !  The ABC(DE)-model of Arabidopsis thaliana floral organ specification !  Determination of floral organ identity depends on the combinatorial expression of floral homeotic genes from different classes !  Integration of color-coded images, representing floral homeotic gene expression patterns, into the context of a regulatory network Junker et al. Frontiers in Plant Science, 2012.
  • 44. Standards for Modelling and Simulation in SysBio
  • 45. Standards for Modelling and Simulation in SysBio
  • 46. Can You Understand This?
  • 47. Can You Understand This? Stimulates? but ... what exactly? Associates into? Trans- locates? Reciprocal stimulation? Is degraded? Stimulates gene Trans- cription?
  • 48. Ambiguity in Conventional Representation
  • 49. Standardised Symbols are Important Most English speaking country Quebec Iran China Israel Singapore Norway Poland USA and Canada
  • 50. What is SBGN? !  A way to unambiguously describe biochemical and cellular events in graphs !  Limited amount of symbols (~30) à Smooth learning curve !  Can graphically represent quantitative models, biochemical pathways, at different levels of granularity !  Developed since 2006 by a interdisciplinary community, part of COMBINE !  Three languages ! Process Descriptions à one state = one glyph ! Entity Relationships à one entity = one glyph ! Activity Flow à conceptual level
  • 51. Graph Trinity: Three Languages in One http://sbgn.org Process Description maps Entity Relationships maps Activity Flow maps !  Unambiguous !  Mechanistic !  Sequential !  Combinatorial explosion !  Unambiguous !  Mechanistic !  Non-Sequential !  Ambiguous !  Conceptual !  Sequential Le Novère et al. Nature Biotechnology, 2009
  • 52. Graph Trinity: Three Languages in One Process Description Entity Relationships Activity Flow
  • 53. Systems Biology Graphical Notation (SBGN)
  • 54. Working with SBGN http://www.sbgn-ed.org !  Verification Czauderna et al. Bioinformatics, 2010 !  Synthesis / bricks Junker et al. Trends in Biotechnology, 2012 !  Translation Czauderna et al. BMC Bioinformatics, 2013 !  Layout Schreiber et al. BMC Bioinformatics, 2009 Dwyer et al. IEEE Transactions Visualization & Computer Graphics, 2008 !  Data integration Junker et al. Nature Protocols, 2012
  • 55. Modelling, Visual Analytics, Standards, Network Analysis Optimise Predict visualise, explore, integrate, analyse, model present, understand simulate, predict
  • 56. Thank You “We now have unprecedented ability to collect data about nature but there is now a crisis developing in biology, in that completely unstructured information does not enhance understanding. We need a framework to put all of this knowledge and data into - that is going to be the problem in biology. […] Driving toward that framework is really the big challenge.” Sydney Brenner

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