Computational Analysis in an extended model of E. Coli
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E. Coli is one of the most intensive studied bacteria. It is easy and cheap to grow. Since the last 60 years of study, six detailed network reconstructions of the metabolism of this bacteria was developed. Despite this long reasearch, current model lack of volatile metabolites which can describe the inner life cycle of a cell in a even more intensive way. Facing the complex nature of such models, my approach is to consult several databases with information we can use, to query them automatically and integrate this date in a update model of E. Coli. To solve this, I used some techniques from A.I. and Data Mining.
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Computational Analysis in an extended model of E. Coli
- 1. S T E V E N S TA D L E R | B L A N K L A B C O M P U TAT I O N A L A N A LY S I S I N A N E X T E N D E D STOICHIOMETRIC MODEL OF ESCHERICHIA COLI 1
- 2. ESCHERICHIA COLI E. COLI Rod-shaped bacterium Commonly found in the lower intestine of warm-blooded organisms Can be grown easily and inexpensively in a laboratory setting Investigated for over 60 years 5 metabolic reconstruction available 2
- 3. C U R R E N T M O S T D E TA I L E D M O D E L O F E . C O L I IJO1366 Published in 2011 from the systems biology research group in California under the supervision of Bernhard Palsson Contains 2251 reactions, 1136 unique metabolites, and 1366 genes. An update of iAF1260, to include biosynthetic pathways 3
- 4. A N U P D A T E O F I J O 1 3 6 6 W I T H V O L A T I L E M E TA B O L I T E S AN APPROACH TO ENHANCE A M O D E L W I T H D ATA B A S E K N O W L E D G E 3788 additional reactions Fetched from KEGG Addition of 194 volatile metabolites 124 were identifiable in KEGG Hit rate of 61% (118/194) 54% with standard reactions of KEGG 4
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- 6. FURTHER FOCUS OF THIS P R E S E N TAT I O N Explanation of the stoichiometric nature of iJO1366 Computational Analysis with FBA Querying of data to enhance iJO1366 Introduction of common pathway databases Integration of data into iJO1366 6
- 7. PA T H W A Y S T O D E S C R I B E C H E M I C A L P R O C E S S E S STOICHIOMETRY Branch of chemistry that deals with the relative quantities of reactants and products in chemical reactions. m x n matrix S with m metabolites and n reactions Starting from knowledge we have, we do: Spot all reactions Detect which metabolite is consumed and wich is produced Loss of informativeness!!! 7
- 8. FURTHER FOCUS OF THIS P R E S E N TAT I O N Explanation of the stoichiometric nature of iJO1366 Computational Analysis with FBA Querying of data to enhance iJO1366 Introduction of common pathway databases Integration of data into iJO1366 8
- 9. F L U X B A L A N C E A N A LY S I S FBA A mathematical approach for analyzing the flow of metabolites through a metabolic network 9
- 10. FURTHER FOCUS OF THIS P R E S E N TAT I O N Explanation of the stoichiometric nature of iJO1366 Computational Analysis with FBA Querying of data to enhance iJO1366 Introduction of common pathway databases Integration of data into iJO1366 10
- 11. T Y P I C A L D ATA M I N I N G I N F L U E N C E D WORKFLOW 11
- 12. D E V E L O P M E N T O F A N A P P L I C A T I O N T O E N H A N C E M O D E L S A U T O M A T I C A L LY H O W T O C O N S U LT A D ATA B A S E Typhoeus a RUBY GEM Runs HTTP requests in parallel while cleanly encapsulating handling logic Grab as many as possible Dynamic storage mechanism Hash Maps Instant accessing time 12
- 13. REST R E P R E S E N TAT I O N A L S TAT E T R A N S F E R Architectural principle of web-based applications defined by Roy Fielding in 2000 Influenced from HTTP The standard INTERNET protocol ! SERVICE DESCRIPTION AS URL GET R E Q U E S T A PA G E /GENES/ALL POST S E N D M U LT I P L E D ATA TO SERVER /GENES/NEW PUT UPLOAD A RESOURCE TO SERVER / G E N E S / THRA DELETE REMOVE RESOURCE / G E N E S / THRA / D E L E T E 13
- 14. FURTHER FOCUS OF THIS P R E S E N TAT I O N Explanation of the stoichiometric nature of iJO1366 Computational Analysis with FBA Querying of data to enhance iJO1366 Introduction of common pathway databases Integration of data into iJO1366 14
- 15. VISUALIZATION OF THE INNER STOICHIOMETRIC LIFE OF A CELL PAT H W AY D ATA B A S E S Visualizable as an acyclic, directed graph No cycles and every edge is directed A Pathway shows the transformation of a metabolic species to a an end product Enzyme catalyze a reaction Reaction transforms educts to products 15
- 16. O N O F T H E B I G G E S T PA T H W A Y D A TA B A S E S KEGG Kyoto Encyclopedia of Genes and Genomes (KEGG) Database resource for understanding high-level functions and utilities of the biological system Initiated in May 1995 it consists of 3 databases PATHWAY GENES LIGAND 16
- 17. AN EXAMPLE OF AN REACTION ENTRY KEGG 17
- 18. A PA R A G O N O F A R E S T F U L A P I KEGG API Application programming interface (API) Specifies how software components should interact with each other. 18
- 19. O N O F T H E B I G G E S T PA T H W A Y D A TA B A S E S KEGG ENTRY 19
- 20. F O C U S O F T H I S P R E S E N TAT I O N Explanation of the stoichiometric nature of iJO1366 Computational Analysis with FBA Querying of data to enhance iJO1366 Introduction of common pathway databases Integration of data into iJO1366 20
- 21. SEVERAL STEPS ARE NECESSARY TO AUTOMATIZE AN ENHANCEMENT PROCESS W O R K F L O W O F A N A P P L I C AT I O N T O ENHANCE MODELS Querying of additional data Storage of additional data Structured as objects for easier access Creation of a search tree with nodes as states Evaluate the worth of a possible enhancement 21
- 22. A C O M M O N PA R A D I G M I N T H E P R O G R A M M I N G C O M M U N I T Y O B J E C T- O R I E N T E D D ATA MANAGEMENT Every concept is a object With all its attributes Easier to iterate through, to get objects with certain attributes 22
- 23. INFLUENCED FROM HEURISTIC BASED A.I. TECHNIQUES S TAT E - B A S E D T H I N K I N G Every enhancement induces a new situation Additional metabolites Additional reactions Start with an metabolites as first state Expand it and his successors until a goal state is reached 23
- 24. EFFICIENT AND POWERFUL I T E R AT I V E D E E P E N I N G State space search strategy Depth-limited search is run repeatedly Increasing the depth limit with each iteration Stops at pre-defined depth d Depending on the task, the max depth has to be chosen 24
- 25. EFFICIENT AND POWERFUL I T E R AT I V E D E E P E N I N G State space search strategy Depth-limited search is run repeatedly Increasing the depth limit with each iteration Stops at pre-defined depth d 25
- 26. EFFICIENT AND POWERFUL I T E R AT I V E D E E P E N I N G State space search strategy Depth-limited search is run repeatedly Increasing the depth limit with each iteration Stops at pre-defined depth d 26
- 27. EFFICIENT AND POWERFUL I T E R AT I V E D E E P E N I N G State space search strategy Depth-limited search is run repeatedly Increasing the depth limit with each iteration Stops at pre-defined depth d 27
- 28. EFFICIENT AND POWERFUL I T E R AT I V E D E E P E N I N G State space search strategy Depth-limited search is run repeatedly Increasing the depth limit with each iteration Stops at pre-defined depth d 28
- 29. EFFICIENT AND POWERFUL I T E R AT I V E D E E P E N I N G State space search strategy Depth-limited search is run repeatedly Increasing the depth limit with each iteration Stops at pre-defined depth d 29
- 30. EFFICIENT AND POWERFUL I T E R AT I V E D E E P E N I N G State space search strategy Depth-limited search is run repeatedly Increasing the depth limit with each iteration Stops at pre-defined depth d 30
- 31. EFFICIENT AND POWERFUL I T E R AT I V E D E E P E N I N G State space search strategy Depth-limited search is run repeatedly Increasing the depth limit with each iteration Stops at pre-defined depth d 31
- 32. O N E O F T H E H A R D E S T P R O B L E M S I N S TA T E B A S E D A P P R O A C H E S S TAT E E X P L O S I O N Number of states grow rapidly Exponential d # S TAT E S # S TAT E S (REACTIONS REVERSIBLE) Reactions focused on E. Coli 2 241 4 3651 54241 6 205360 1,2 X 10^7 8 32 65 12 X 10^6 2,8 X 10^9
- 33. S E V E R A L A P P R O A C H E S T O H A N D L E A B I G S TA T E S PA C E M A N A G I N G T H E S E A R C H S PA C E Pruning of the search space Remove unworthy nodes Pre-ordering of the search state Expand worthy states first Depending on the implemented heuristic 33
- 34. A S TA T I S T I C A B O U T T H E A V E R A G E N U M B E R O F N E W E D I C T S A F T E R A N S TA T E E X PA N S I O N M A N A G I N G T H E S E A R C H S PA C E AV E R A G E N U M B E R O F N E W E D U C T S Depth 2 1 Depth 3 Depth 4 0,75 0,5 0,25 0 C00207 C00014 C00180 C16521 34 C00409 C02223 C00146 C01879
- 35. IS IT WORTH TO GO DEEPER? M A N A G I N G T H E S E A R C H S PA C E R AT I O N B E T W E E N S O L U T I O N S A N D S TAT E S Depth 2 1 Depth 3 Depth 4 0,75 0,5 0,25 0 C00207 C00014 C00180 C16521 35 C00409 C02223 C00146 C01879
- 36. F A C I N G I N C O N S I S T E N C Y W E H A V E T O I N T E G R A T E T H E D A TA I N T E G R AT I N G N E W D ATA Fetched data has to be normalized Use regular expressions to grab necessary data Transform data to current standard Be aware of inconsistency Missing information has to be estimate Is it worth to estimate? Use data you already have 36
- 37. P O I N T S W H I C H S T I L L H A V E T O B E S O LV E D P R O B L E M S T O S O LV E Application is specialized to iJO1366 Find normalization methods Queries depend on restful web services Develop wrapper methods Application is constrained on E. Coli reactions only Find solutions to handle state explosions Evaluate states concerning biological aspects 37
- 38. SUGGESTIONS FOR THE FURTHER WORK OUTLOOK More and more web services become restful Easier to develop web crawler to fetch new data A standardization of data should be developed KEGG is a proper example of standardization and easy APIs Future network reconstructions are already very complex Collaboration of programmers with biologists to develop applications for an easier work with metabolic models 38
- 40. REFERENCES Pictures adapted from Jan Schellenberger, Richard Que, Ronan M T Fleming, Ines Thiele, Jeffrey D Orth, Adam M Feist, Daniel C Zielinski, Aarash Bordbar, Nathan E Lewis, Sorena Rah- manian, Joseph Kang, Daniel R Hyduke, and Bernhard O Palsson. Quantitative prediction of cellular metabolism with constraint-based models: the cobra toolbox v2.0. Nat. Protocols, 6(9):1290 1307, 09 2011. Jong Min Lee, Erwin P. Gianchandani, and Jason A. Papin. Flux ! balance analysis in the era of metabolomics. Brie鍖ngs in Bioinformatics, 7(2):140150, 2006. doi: 10.1093/bib/bbl007. 40
- 41. Questions? S T E V E N S TA D L E R 41