Pizza club - February 2017 - Federico
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Transcript of Pizza club - February 2017 - Federico
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1
Metabolic Network modelling of Microbial Communities
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Talk outline
• Metabolic modeling
• Single strain applications
• From single organism to community modeling
• Community modeling techniques
• Comparison
• Conclusion: automated efficient / tools
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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
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Model creatrion
4
A
B
Cr1
r2 r3
e1
e2
e3
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Mathematical formulation
5
A
B
Cr1
r2 r3
e1
e2
e3dA/dt
dB/dt
dC/dt
e1e2e3r1r2r3
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
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Simulation
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A
B
Cr1
r2 r3
e1
e2
e30
0
0
e1e2e3r1r2r3
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
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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
e1e2e3r1r2r3
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Simulation: Flux Balance Analysis
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e1e2e3r1r2r3
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
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In few words
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• Growth measurement and type of metabolism in a specific environment• Strain characterisation: required media for growth• Essential enzymes for growth • Biotechnological applications: strain engineering
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Examples of application
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Examples of application
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From single organism to community modeling
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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)
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Simulating ecosystems: modeling bacteria communities
o Enzyme soup
o Compartmentalization
o Agent Based Modeling integration
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Enzyme soup
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A
B
Cr1
r2 r3
e1
e2
e3
Model 1
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Enzyme soup
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A Cr1e1 e3D
e4
r4 r5
Model 2
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Enzyme soup
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A
B
Cr1
r2 r3
e1
e2
e3D
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
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Compartmentalization
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A
B
Cr1
r2 r3
e1
e2
e3 A Cr1e1 e3D
e4
r4 r5
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Compartmentalization
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A
B
Cr1
r2 r3
ie1
ie2
ie3 A Cr1ie1 ie3D
ie4
r4 r5
e1
e2 e3e4
A
B C D
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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
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B𝑐𝑐 = 𝑋𝑋𝑋𝑋1 + YB2 … . +ZBn
Cumulative biomass
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Agent Based Modeling
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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
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Agent Based Modeling integration (BacArena)
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Diffusion
Environment (Grid/Matrix)
Model of a microbial consortium in BacArena
• Creation of 2D environment (arena)
• Metabolites can freely diffuse in the arena
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Agent Based Modeling integration (BacArena)
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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
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Agent Based Modeling integration (BacArena)
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Diffusion
Environment (Grid/Matrix)
• Different proprieties associated to different organisms
•Organisms can proliferate, move in the grid creating different metabolites concentrations
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Agent Based Modeling integration (BacArena)
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Diffusion
Environment (Grid/Matrix)
Diffusion
Environment (Grid/Matrix)
Diffusion
Movement and
Replication
Environment (Grid/Matrix)
Simulation
T0 TfTime
Model of a microbial consortium in BacArena
Time space resolved:
• Individuals growth and colonies formation• Metabolites dynamics• Organisms’ metabolic phenotyping • Organisms interactions
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2525
Integrated gut model in BacArena
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Comparison
Features “Enzyme soup” Compartment. ABM integration
Info required Low Medium MediumVariables control High High Medium
Versatility Very low Low Extremely highSpeed High Low Low
Data integration No Yes Yes Dynamic community No No YesDynamic simulation No No Yes Organisms interact. No Yes Yes
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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.
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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 MagnusdottirMarouen Ben Guebilla
Dmitry RavcheevLaurent HeirendtAlberto NoronhaFederico BaldiniAlmut HeinkenMaike AurichEugen Bauer
THANK YOU FOR LISTENING !!