Community Modeling Workshop

Community Modeling WorkshopFederico Baldini & Eugen Bauer

What are we going to do today?Motivation EugenIntroduction FedericoTheory of BacArena EugenBacArena practical EugenSocial event Susanne

Why I study Science

Jeong et al, Nature, 2011

Why I study Systems BiologyEmergence: 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

Systems Biology Philosophies

Top DownData drivenNetwork inferenceStatistical modeling

Bottom UpHypothesis drivenModel formulationModel assemblyGenesMetabolitesProteins.OrganellesMetabolism.Organisms.Ecosystem

Systems Biology Philosophies

Top DownData drivenNetwork inferenceStatistical modeling

Bottom UpHypothesis drivenModel formulationModel assemblyGenesMetabolitesProteins.OrganellesMetabolism.Organisms.Ecosystem

GenomeGenomeGenesEnzymes

GlucoseGlucose-6P

Fructose-6P

Gluconate-6P

ATPADP

ATPADP

NADPNADPH Constrained Based Modeling

Glucose-6P + ADP Glucose ATP = 0Fructose-6P Glucose-6P = 0Gluconate-6P + NADPH Glucose-6P NADP = 0GenomeGenomeGenesEnzymes

ReactionsReconstruction

GlucoseGlucose-6P

Fructose-6P

Gluconate-6P

ATPADP

ATPADP

NADPNADPHRxn1Rxn2Rxn3Glc-100.G6P1-1-1.F6P010.Gl6P001.ADP100.ATP-100.NADP00-1.NADPH001.....

ModelOrth et al, Nature Biotech, 2010Constrained Based Modeling

Now its Federicos turn

Mathematical formulation

ABCr1r2r3e1e2e3

Mathematical formulationdA/dt

dB/dt

dC/dte1e2e3r1r2r31 0 0 -1 -1 0

0 -1 0 0 1 -1

0 0 -1 1 0 1=*

SvdA/dt = e1 r1 r2

dB/dt = r2 e2 r3

dC/dt = r1 + r3 e3

dA/dt

dB/dt

dC/dt1 0 0 -1 -1 0

0 -1 0 0 1 -1

0 0 -1 1 0 1=*

SvdA/dt = e1 r1 r2

dB/dt = r2 e2 r3

dC/dt = r1 + r3 e3

ABCr1r2r3e1e2e3

SimulationSteady state assumption:

no change of concentrations -> no compound accumulation 0

0

0e1e2e3r1r2r31 0 0 -1 -1 0

0 -1 0 0 1 -1

0 0 -1 1 0 1=*

Sv0 = e1 r1 r2

0 = r2 e2 r3

0 = r1 + r3 e3

dA/dt = 0

dB/dt = 0

dC/dt = 0

ABCr1r2r3e1e2e3

ABCr1r2r3e1e2e31 0 0 -1 -1 0

0 -1 0 0 1 -1

0 0 -1 1 0 1=*

Sv

Steady state assumption Constrained flux assumption

0

0

00 = e1 r1 r2

0 = r2 e2 r3

0 = r1 + r3 e3e1e2e3r1r2r3Simulation

Simulation: Flux Balance Analysise1e2e3r1r2r31 0 0 -1 -1 0

0 -1 0 0 1 -1

0 0 -1 1 0 1=*

Sv

Steady state assumption Constrained flux assumption Objective function (biomass) optimization0 = e1 r1 r2

0 = r2 e2 r3

0 = r1 + r3 e30

0

0

In few words....

Growth measurement and type of metabolism in a specific environmentStrain characterisation: required media for growthEssential enzymes for growth Biotechnological applications: strain engineering

Examples of applications

Examples of applications

BiofilmGut microbiota http://ausubellab.mgh.harvard.edu/picturehtml/pic20.html

Zoetendal, Raes et al. (2012)

Pseudomonas aeruginosa biofilmBiofilm 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

Enzyme soup

ABCr1r2r3e1e2e3Model 1

ACr1e1e3D

e4r4r5Model 2Enzyme soup

ABCr1r2r3e1e2e3D

e4r4r5panModel 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

Compartmentalization

ABCr1r2r3e1e2e3

ACr1e1e3D

e4r4r5

ABCr1r2r3ie1ie2ie3

ACr1ie1ie3D

ie4r4r5

e1e2e3e4

A

BCD

Compartmentalization

Cumulative biomass as objective function Combination of the biomass functions for each species: same abundance for each species

Weighted combination of the biomass functions for each species on the base of their presence in experimental active communities

Data integration

Cumulative biomass

Simulating ecosystems: modeling bacteria communities Enzyme soupExploring community potentialNo Individuals representation

CompartmentalizationAbundances fixed and not changingNo 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

Agent Based modeling integration

Now its Eugens turnWhat is BacArena?

BacArena = Bac + Arena

BacArena How it works

Models of different or same speciesIntegration of constrained and agent based modeling

BacArena How it works

Models of different or same speciesMovement & Replication of species

BacArena How it works

Models of different or same speciesMovement & Replication of speciesMetabolite concentration in the Arena

BacArena How it works

Models of different or same speciesMovement & replication of speciesMetabolite concentration in the ArenaUptake & Secretion of metabolites

BacArena How it works

Models of different or same speciesMovement & replication of speciesMetabolite concentration in the ArenaUptake & Secretion of metabolitesInteractions come from exchange

BacArena How it works

Models of different or same speciesMovement & replication of speciesMetabolite concentration in the ArenaUptake & Secretion of metabolitesInteractions come from exchangeMetabolic Phenotypes in Individuals

BacArena How it worksModels of different or same speciesMovement & replication of speciesMetabolite concentration in the ArenaUptake & Secretion of metabolitesInteractions come from exchange

Metabolic Phenotypes in IndividualsDiscrete time steps simulating spatial metabolic dynamics

BacArena How it worksModels of different or same speciesMovement & replication of speciesMetabolite concentration in the ArenaUptake & Secretion of metabolitesInteractions come from exchange

Metabolic Phenotypes in IndividualsDiscrete time steps simulating spatial metabolic dynamicsHow do I know the model parameters?

Parameterize the Model with Experimental DataBauer et al, in revision Values are taken from experimental literature, but you can also plug in your own data

Programming DetailsR package deposited in CRANMatrix based implementationModular, extendible codeObject oriented programmingArena environmentBac species & modelsSubstance metabolitesEval evaluate simulationSeparate simulation & analysis

Programming DetailsR package deposited in CRANMatrix based implementationModular, extendible codeObject oriented programmingArena environmentBac species & modelsSubstance metabolitesEval evaluate simulationSeparate simulation & analysis

Programming DetailsR package deposited in CRANMatrix based implementationModular, extendible codeObject oriented programmingArena environmentBac species & modelsSubstance metabolitesEval evaluate simulationSeparate simulation & analysis

Programming DetailsR package deposited in CRANMatrix based implementationModular, extendible codeObject oriented programmingArena environmentBac species & modelsSubstance metabolitesEval evaluate simulationSeparate simulation & analysis

Programming DetailsR package deposited in CRANMatrix based implementationModular, extendible codeObject oriented programmingArena environmentBac species & modelsSubstance metabolitesEval evaluate simulationSeparate simulation & analysis

Now lets start the DemonstrationEverything will be uploaded here: http://rsg-luxembourg.iscbsc.org/

Availability of BacArenaPaper is currently under revisionOfficial version is on CRAN:https://CRAN.R-project.org/package=BacArenaDevelopment version is hosted on GitHub:https://github.com/euba/BacArena

Compare with Experiments

Photomicrograph of P. aeruginosa biofilm cross sections stained for APase activityXu et al, Appl Environ Microbiol, 1998

ConclusionsMetabolism of individual cells in populationTop down data integrationMeta-genomic dataMeta-transcriptomic dataModel assumptionsMetabolite diffusionHeterogeneous metabolismFrom local interactions arises complexity

AcknowledgmentsMolecular Systems Physiology Group:Ines Thiele (PI)Stefania MagnusdottirMarouen GuebillaDmitry RavcheevLaurent HeirendtAlberto NoronhaFederico BaldiniAlmut HeinkenMaike Aurich

Christian-Albrechts-Universitt Kiel:Christoph KaletaJohannes ZimmermannThanks to the HPC facilities of the University of Luxembourg

The RSG Luxembourg Board

the RSG spirit

More RSG Courses Stay Tuned!

20.03. B'RAIN Company PresentationWhen? Monday 20.03.2017 from 17:00 to 19:00Where? Maison du Savoir Room 4.41005.04. Latex WorkshopWhen? Monday 05.04.2017 from 17:00 to 19:00Where? Maison du Savoir Room 4.410

12.04. Git WorkshopWhen? Wednesday 12.04.2017 from 17:00 to 19:00Where? TBA

Further Acknowledgments

Join us as a RSG Luxembourg member!Thank you for attention

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