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Metabolic Network modelling of Microbial Communities

Talk outline

• Metabolic modeling

• Single strain applications

• From single organism to community modeling

• Community modeling techniques

• Comparison

• Conclusion: automated efficient / tools

2

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

Model creatrion

4

A

B

Cr1

r2 r3

e1

e2

e3

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

Simulation

6

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

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

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

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

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)

Simulating ecosystems: modeling bacteria communities

o Enzyme soup

o Compartmentalization

o Agent Based Modeling integration

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Enzyme soup

14

A

B

Cr1

r2 r3

e1

e2

e3

Model 1

Enzyme soup

15

A Cr1e1 e3D

e4

r4 r5

Model 2

Enzyme soup

16

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

Compartmentalization

17

A

B

Cr1

r2 r3

e1

e2

e3 A Cr1e1 e3D

e4

r4 r5

Compartmentalization

18

A

B

Cr1

r2 r3

ie1

ie2

ie3 A Cr1ie1 ie3D

ie4

r4 r5

e1

e2 e3e4

A

B C D

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

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

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

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

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

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

2525

Integrated gut model in BacArena

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

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