Community Modeling Workshop Federico Baldini & Eugen Bauer
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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