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Modeling Methanococcus maripaludis for the production of Methanol

Modeling Methanococcus maripaludis for the production of Methanol. Nathan Suek August 5, 2016. About ISB. Systems biology is based on the understanding that the whole is greater than the sum of its parts.

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Modeling Methanococcus maripaludis for the production of Methanol

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  1. Modeling Methanococcus maripaludis for the production of Methanol Nathan Suek August 5, 2016

  2. About ISB • Systems biology is based on the understanding that the whole is greater than the sum of its parts. • Interdisciplinary teams of biologists, computer scientists, engineers, physicists and many others • Founded by Lee Hood, whose lab pioneered the automated DNA sequencer which made possible the Human Genome Project

  3. Background • M. maripaludis is a methanogen, a bacteria that produces methane from other carbon sources • Our goal is to engineer its metabolism to take in methane (greenhouse gas) and produce methanol (biofuel) • Using a metabolic model of M. maripaludis, several strategies are presented for metabolic engineering of its native pathway to produce methanol

  4. Introduction: Producing MeOH

  5. Add MeOH Rxn Extracellular CO2

  6. Add MeOH Rxn Extracellular CO2

  7. Add MeOH Rxn Extracellular CO2

  8. Reverse Pathway: Make MeOH

  9. Reverse Pathway: Make MeOH

  10. Solving our Metabolic System: Infeasible

  11. Solving our Metabolic System: Infeasible

  12. Solving our Metabolic System: Infeasible

  13. Reverse Pathway: Make MeOH

  14. Methods: SimOptStrain

  15. SimOptStrain • Program developed by Jennifer Reed’s Group (UW Madison) • Program could simultaneously consider reaction additions & deletions that would optimize objective • Can use program to pick a reaction addition to make our model feasible • SimOptStrain was complicated – rewrote program to only consider additions

  16. How SimOptStrain Works Database Rxn 1 2 3 … … 99 100 Solution: fluxes for all rxns – including added rxn – in model such that objective is optimized Result Add reaction(s) from database K that will make model feasible

  17. Results

  18. SEED Database Run Seed Database: 13272 reactions Single Rxn Addition Solutions: 87 Solutions "rxn01514" "rxn00241" "rxn03541" "rxn09907" "rxn09913“ "rxn05157" "rxn09925" "rxn01511" "rxn00363" "rxn00913" "rxn00436" "rxn01492" "rxn00408" "rxn09908" "rxn09918“ "rxn09931" "rxn09516" "rxn00216" "rxn08067" "rxn00518" "rxn09502" "rxn09694" "rxn13842" "rxn10578" "rxn05514" "rxn05622" "rxn10443" "rxn10838" "rxn10170" "rxn05165" "rxn10448" "rxn09926“ "rxn09938" "rxn08981" "rxn05166" "rxn10571" "rxn10558" "rxn01221" "rxn10584" "rxn00556" "rxn00545" "rxn00557" "rxn00554" "rxn00841" "rxn09177" "rxn08066“ "rxn00118" "rxn00097" "rxn05312" "rxn00435" "rxn05210" "rxn03020" "rxn11987" "rxn05807" "rxn00150" "rxn07034" "rxn07049" "rxn06874" "rxn10447" "rxn10168" "rxn08204" "rxn08205" "rxn09014" "rxn05059" "rxn08202" "rxn08201“ "rxn08203" "rxn00367" "rxn08200" "rxn08199" "rxn01222" "rxn01537" "rxn00831" "rxn00366" "rxn10583" "rxn00061" "rxn01103" "rxn01677" "rxn00407“ "rxn00088" "rxn00062" "rxn00153" "rxn00287" "rxn07971" "rxn00513" "rxn09901“ "rxn00120"

  19. Thanks • Special thanks to • Nathan Price for the opportunity to be at ISB this summer • Vangelis & Matt for the helpful mentorship throughout project • Austin for collaborating on the data analysis • Thank you!

  20. Appendices

  21. FVA Heatmap Figure provided by Austin Prince

  22. Strictly Positive ATPS Reactions H2O + GAP + 2 Oxidized Ferredoxin <=> 3 H + 3-Phosphoglycerate + 2 Reduced Ferredoxin

  23. Test Cases Test 2 Test 3 Control Test 1

  24. Test Cases Test 2 Test 3 Control Test 1

  25. Additional Tests Test solutions found using GapFill Strategy Test 1 Test 2

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