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# Pizza club - February 2017 - Federico

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Biggs, M. B., Medlock, G. L., Kolling, G. L. and Papin, J. A. (2015), Metabolic network modeling of microbial communities. WIREs Syst Biol Med, 7: 317–334. doi:10.1002/wsbm.1308

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### Pizza club - February 2017 - Federico

1. 1. 1 Metabolic Network modelling of Microbial Communities
2. 2. Talk outline • Metabolic modeling • Single strain applications • From single organism to community modeling • Community modeling techniques • Comparison • Conclusion: automated efficient / tools 2
3. 3. Metabolic modeling 3 Genome annotation Network reconstruction Model creation refinement Simulation Orth, J. D., et al. (2010) Phenotype prediction http://science.howstuffworks.com Mathematical formulation http://www.uleth.ca
4. 4. Model creatrion 4 A B Cr1 r2 r3 e1 e2 e3
5. 5. Mathematical formulation 5 A B Cr1 r2 r3 e1 e2 e3 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
6. 6. Simulation 6 A B Cr1 r2 r3 e1 e2 e3 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 • Steady state assumption: no change of concentrations -> no compound accumulation dA/dt = 0 dB/dt = 0 dC/dt = 0
7. 7. Simulation 7 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
8. 8. Simulation: Flux Balance Analysis 8 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
9. 9. In few words 9 • Growth measurement and type of metabolism in a specific environment • Strain characterisation: required media for growth • Essential enzymes for growth • Biotechnological applications: strain engineering
10. 10. Examples of application 10
11. 11. Examples of application 11
12. 12. From single organism to community modeling 12 Bioaugementation Gut microbiota http://4genviro.com/markets-served/water-soil-remediation/ Biological augmentation- the addition of archaea or bacterial cultures required to speed up the rate of degradation of a contaminant Zoetendal, Raes et al. (2012)
13. 13. Simulating ecosystems: modeling bacteria communities o Enzyme soup o Compartmentalization o Agent Based Modeling integration 13
14. 14. Enzyme soup 14 A B Cr1 r2 r3 e1 e2 e3 Model 1
15. 15. Enzyme soup 15 A Cr1e1 e3 D e4 r4 r5 Model 2
16. 16. Enzyme soup 16 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
17. 17. Compartmentalization 17 A B Cr1 r2 r3 e1 e2 e3 A Cr1e1 e3 D e4 r4 r5
18. 18. Compartmentalization 18 A B Cr1 r2 r3 ie1 ie2 ie3 A Cr1ie1 ie3 D ie4 r4 r5 e1 e2 e3e4 A B C D
19. 19. Cumulative biomass as objective function o Approach first used to simulate eukaryotic cell 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 o Abundances fixed and not changing o Each species is growing optimally o Variable control problem: • Alpha: predict uptake and secretion of metabolites with known species abundances • Beta: predict species abundances with known uptake and secretion rates 19 B𝑐𝑐 = 𝑋𝑋𝑋𝑋1 + YB2 … . +ZBn Cumulative biomass
20. 20. Agent Based Modeling 20 o An agent is an entity which plays a role in determining the status system o It acts according to specific rules o The status of the system is a result of the interactions of all the agents scidacreview.org Microbial community Human behavior
21. 21. Agent Based Modeling integration (BacArena) 21 Diffusion Environment (Grid/Matrix) Model of a microbial consortium in BacArena • Creation of 2D environment (arena) • Metabolites can freely diffuse in the arena
22. 22. Agent Based Modeling integration (BacArena) 22 Diffusion Environment (Grid/Matrix) Diffusion Environment (Grid/Matrix) Simulation T0 Model of a microbial consortium in BacArena • Metabolic models of organisms can be inserted • Dynamic scenario: a certain time period is simulated
23. 23. Agent Based Modeling integration (BacArena) 23 Diffusion Environment (Grid/Matrix) • Different proprieties associated to different organisms •Organisms can proliferate, move in the grid creating different metabolites concentrations
24. 24. Agent Based Modeling integration (BacArena) 24 Diffusion Environment (Grid/Matrix) Diffusion Environment (Grid/Matrix) Diffusion Movement and Replication Environment (Grid/Matrix) Simulation T0 Tf Time Model of a microbial consortium in BacArena Time space resolved: • Individuals growth and colonies formation • Metabolites dynamics • Organisms’ metabolic phenotyping • Organisms interactions
25. 25. 25 25 Integrated gut model in BacArena
26. 26. Comparison Features “Enzyme soup” Compartment. ABM integration Info required Low Medium Medium Variables control High High Medium Versatility Very low Low Extremely high Speed High Low Low Data integration No Yes Yes Dynamic community No No Yes Dynamic simulation No No Yes Organisms interact. No Yes Yes 26
27. 27. Conclusion: need for automated and efficient tools o Importance of understanding communities interactions o All three approaches are interesting and useful to answer to different questions o Data integration (metagenomics) is important Need for automated, user friendly and fast tools capable of integrating data onto different modelling frameworks and implement standardize result analysis. 27
28. 28. Literature: Orth, J. D., et al. (2010). "What is flux balance analysis?" Nat Biotech 28(3): 245-248. Holland, J. H. (1992). "Complex adaptive systems." Daedalus: 17-30. Zimmermann, E. B. a. J. "BacArena: Modeling Framework for Cellular Communities in their Environments." Thiele, I. and B. Ø. Palsson (2010). "A protocol for generating a high-quality genome-scale metabolic reconstruction." Nature protocols 5(1): 93-121. 28 Molecular Systems Physiology Group: Ines Thiele (PI) Stefania Magnusdottir Marouen Ben Guebilla Dmitry Ravcheev Laurent Heirendt Alberto Noronha Federico Baldini Almut Heinken Maike Aurich Eugen Bauer THANK YOU FOR LISTENING !!