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Community Modeling Workshop

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Learn how to use tools in systems biology to model metabolic interactions within microbial communities. The workshop will cover background in constrained based modeling and show applications of modelling single and multiple species.

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Community Modeling Workshop

  1. 1. Community Modeling Workshop Federico Baldini & Eugen Bauer
  2. 2. What are we going to do today? 1. Motivation – Eugen 2. Introduction – Federico 3. Theory of BacArena – Eugen 4. BacArena practical – Eugen 5. Social event – Susanne
  3. 3. Why I study Science
  4. 4. Jeong et al, Nature, 2011 Why I study Systems Biology Emergence: Phenomenon in which larger components arise through local interactions of smaller components such that larger components have additional properties Systems biology: Study of the interactions between the components of biological systems, and how these interactions give rise to the function of that system
  5. 5. Systems Biology Philosophies Top Down • Data driven • Network inference • Statistical modeling Bottom Up • Hypothesis driven • Model formulation • Model assembly Genes Metabolites Proteins …. Organelles Metabolism .… Organisms .… Ecosystem
  6. 6. Systems Biology Philosophies Top Down • Data driven • Network inference • Statistical modeling Bottom Up • Hypothesis driven • Model formulation • Model assembly Genes Metabolites Proteins …. Organelles Metabolism .… Organisms .… Ecosystem
  7. 7. Genome GenomeGenes Enzymes Glucose Glucose-6P Fructose-6P Gluconate-6P ATP ADP ATP ADP NADP NADPH Constrained Based Modeling
  8. 8. Glucose-6P + ADP – Glucose – ATP = 0 Fructose-6P – Glucose-6P = 0 Gluconate-6P + NADPH – Glucose-6P – NADP = 0 … Genome GenomeGenes Enzymes Reactions Reconstruction Glucose Glucose-6P Fructose-6P Gluconate-6P ATP ADP ATP ADP NADP NADPH Rxn1 Rxn2 Rxn3 … Glc -1 0 0 . G6P 1 -1 -1 . F6P 0 1 0 . Gl6P 0 0 1 . ADP 1 0 0 . ATP -1 0 0 . NADP 0 0 -1 . NADPH 0 0 1 . … . . . . 𝑆 = Model Orth et al, Nature Biotech, 2010 Constrained Based Modeling
  9. 9. Now it’s Federicos turn
  10. 10. Mathematical formulation A B Cr1 r2 r3 e1 e2 e3
  11. 11. Mathematical formulation dA/dt dB/dt dC/dt e1 e2 e3 r1 r2 r3 1 0 0 -1 -1 0 0 -1 0 0 1 -1 0 0 -1 1 0 1 = * S v dA/dt = e1 – r1 – r2 dB/dt = r2 – e2 – r3 dC/dt = r1 + r3 – e3 dA/dt dB/dt dC/dt 1 0 0 -1 -1 0 0 -1 0 0 1 -1 0 0 -1 1 0 1 = * S v dA/dt = e1 – r1 – r2 dB/dt = r2 – e2 – r3 dC/dt = r1 + r3 – e3 A B Cr1 r2 r3 e1 e2 e3
  12. 12. Simulation • Steady state assumption: no change of concentrations -> no compound accumulation 0 0 0 e1 e2 e3 r1 r2 r3 1 0 0 -1 -1 0 0 -1 0 0 1 -1 0 0 -1 1 0 1 = * S v 0 = e1 – r1 – r2 0 = r2 – e2 – r3 0 = r1 + r3 – e3 dA/dt = 0 dB/dt = 0 dC/dt = 0 A B Cr1 r2 r3 e1 e2 e3
  13. 13. A B Cr1 r2 r3 e1 e2 e3 1 0 0 -1 -1 0 0 -1 0 0 1 -1 0 0 -1 1 0 1 = * S v • Steady state assumption • Constrained flux assumption 0 0 0 0 = e1 – r1 – r2 0 = r2 – e2 – r3 0 = r1 + r3 – e3 e1 e2 e3 r1 r2 r3 Simulation
  14. 14. Simulation: Flux Balance Analysis e1 e2 e3 r1 r2 r3 1 0 0 -1 -1 0 0 -1 0 0 1 -1 0 0 -1 1 0 1 = * S v • Steady state assumption • Constrained flux assumption • Objective function (biomass) optimization 0 = e1 – r1 – r2 0 = r2 – e2 – r3 0 = r1 + r3 – e3 0 0 0
  15. 15. In few words.... • Growth measurement and type of metabolism in a specific environment • Strain characterisation: required media for growth • Essential enzymes for growth • Biotechnological applications: strain engineering
  16. 16. Examples of applications
  17. 17. Examples of applications
  18. 18. Biofilm Gut microbiota http://ausubellab.mgh.harvard.edu/picturehtml/pic20.html Zoetendal, Raes et al. (2012) Pseudomonas aeruginosa biofilm Biofilm microcolony formed by P. aeruginosa strain PA14 carrying GFP. Biofilms were cultivated in flow chambers under continuous culture conditions. Analysis of biofilm spatial structures were done using confocal scanning laser microscopy after 9 hours of incubation. From single organism to community modeling
  19. 19. Enzyme soup A B Cr1 r2 r3 e1 e2 e3 Model 1
  20. 20. A Cr1e1 e3 D e4 r4 r5 Model 2 Enzyme soup
  21. 21. A B Cr1 r2 r3 e1 e2 e3 D e4 r4 r5 panModel • Limited “a priori” knowledge • No attempt to segregate reactions by strains / species • Exploration of metabolic potential of an entire community more then interactions between community members Enzyme soup
  22. 22. Compartmentalization A B Cr1 r2 r3 e1 e2 e3 A Cr1e1 e3 D e4 r4 r5
  23. 23. A B Cr1 r2 r3 ie1 ie2 ie3 A Cr1ie1 ie3 D ie4 r4 r5 e1 e2 e3e4 A B C D Compartmentalization
  24. 24. Cumulative biomass as objective function o Combination of the biomass functions for each species: same abundance for each species o Weighted combination of the biomass functions for each species on the base of their presence in experimental active communities o Data integration B𝑐 = 𝑋𝐵1 + YB2 … . +ZBn Cumulative biomass
  25. 25. Simulating ecosystems: modeling bacteria communities o Enzyme soup Exploring community potential No Individuals representation o Compartmentalization Abundances fixed and not changing No concentrations No time and space resolved simulation Variable control problem predict uptake and secretion of metabolites with known species abundances predict community growth with known uptake and secretion rates o Agent Based modeling integration
  26. 26. Now it’s Eugens turn What is BacArena?
  27. 27. BacArena = Bac + Arena
  28. 28. BacArena – How it works Models of different or same species Integration of constrained and agent based modeling
  29. 29. BacArena – How it works Models of different or same species Movement & Replication of species
  30. 30. BacArena – How it works Models of different or same species Movement & Replication of species Metabolite concentration in the Arena
  31. 31. BacArena – How it works Models of different or same species Movement & replication of species Metabolite concentration in the Arena Uptake & Secretion of metabolites
  32. 32. BacArena – How it works Models of different or same species Movement & replication of species Metabolite concentration in the Arena Uptake & Secretion of metabolites Interactions come from exchange
  33. 33. BacArena – How it works Models of different or same species Movement & replication of species Metabolite concentration in the Arena Uptake & Secretion of metabolites Interactions come from exchange Metabolic Phenotypes in Individuals
  34. 34. BacArena – How it works Models of different or same species Movement & replication of species Metabolite concentration in the Arena Uptake & Secretion of metabolites Interactions come from exchange Metabolic Phenotypes in Individuals Discrete time steps simulating spatial metabolic dynamics
  35. 35. BacArena – How it works Models of different or same species Movement & replication of species Metabolite concentration in the Arena Uptake & Secretion of metabolites Interactions come from exchange Metabolic Phenotypes in Individuals Discrete time steps simulating spatial metabolic dynamics How do I know the model parameters?
  36. 36. Parameterize the Model with Experimental Data Bauer et al, in revision  Values are taken from experimental literature, but you can also plug in your own data
  37. 37. Programming Details • R package deposited in CRAN • Matrix based implementation • Modular, extendible code • Object oriented programming • Arena  environment • Bac  species & models • Substance  metabolites • Eval  evaluate simulation • Separate simulation & analysis
  38. 38. Programming Details • R package deposited in CRAN • Matrix based implementation • Modular, extendible code • Object oriented programming • Arena  environment • Bac  species & models • Substance  metabolites • Eval  evaluate simulation • Separate simulation & analysis
  39. 39. Programming Details • R package deposited in CRAN • Matrix based implementation • Modular, extendible code • Object oriented programming • Arena  environment • Bac  species & models • Substance  metabolites • Eval  evaluate simulation • Separate simulation & analysis
  40. 40. Programming Details • R package deposited in CRAN • Matrix based implementation • Modular, extendible code • Object oriented programming • Arena  environment • Bac  species & models • Substance  metabolites • Eval  evaluate simulation • Separate simulation & analysis
  41. 41. Programming Details • R package deposited in CRAN • Matrix based implementation • Modular, extendible code • Object oriented programming • Arena  environment • Bac  species & models • Substance  metabolites • Eval  evaluate simulation • Separate simulation & analysis
  42. 42. Now let’s start the Demonstration Everything will be uploaded here: http://rsg-luxembourg.iscbsc.org/
  43. 43. Availability of BacArena • Paper is currently under revision • Official version is on CRAN: • https://CRAN.R-project.org/package=BacArena • Development version is hosted on GitHub: • https://github.com/euba/BacArena
  44. 44. Compare with Experiments Photomicrograph of P. aeruginosa biofilm cross sections stained for APase activity Xu et al, Appl Environ Microbiol, 1998
  45. 45. Conclusions Metabolism of individual cells in population • Top down data integration • Meta-genomic data • Meta-transcriptomic data • Model assumptions • Metabolite diffusion • Heterogeneous metabolism From local interactions arises complexity
  46. 46. Acknowledgments Molecular Systems Physiology Group: Ines Thiele (PI) Stefania Magnusdottir Marouen Guebilla Dmitry Ravcheev Laurent Heirendt Alberto Noronha Federico Baldini Almut Heinken Maike Aurich Christian-Albrechts-Universität Kiel: Christoph Kaleta Johannes Zimmermann Thanks to the HPC facilities of the University of Luxembourg
  47. 47. The RSG Luxembourg Board … the RSG spirit
  48. 48. More RSG Courses – Stay Tuned! 20.03. B'RAIN Company Presentation When? Monday 20.03.2017 from 17:00 to 19:00 Where? Maison du Savoir Room 4.410 05.04. Latex Workshop When? Monday 05.04.2017 from 17:00 to 19:00 Where? Maison du Savoir Room 4.410 12.04. Git Workshop When? Wednesday 12.04.2017 from 17:00 to 19:00 Where? TBA
  49. 49. Further Acknowledgments Join us as a RSG Luxembourg member! Thank you for attention
  50. 50. THE END

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