Individual Based Modeling of Microbial Individual Based Modeling of Microbial Communities: Solving the Microbial Subgrid Communities: Solving the Microbial Subgrid

Scale ProblemScale Problem

Dave Siegel, Satoshi Mitarai, Roger Dave Siegel, Satoshi Mitarai, Roger Nisbet, Bruce Kendall & Jeff MoehlisNisbet, Bruce Kendall & Jeff Moehlis

University California, Santa BarbaraUniversity California, Santa Barbara

• Definition of predictabilityDefinition of predictabilityThe ability to correctly forecast the future state The ability to correctly forecast the future state

of a systemof a system

• Microbial communities are hard to predict Microbial communities are hard to predict [well][well]

We don’t know really who is out thereWe don’t know really who is out thereOf who know, we don’t know really what they are Of who know, we don’t know really what they are

doing doing Don’t know interactions among participants & Don’t know interactions among participants &

their environmenttheir environment

Predictability of Microbial Predictability of Microbial CommunitiesCommunities

• Classical approach – population dynamicsClassical approach – population dynamics Widely used for modeling changes in space & Widely used for modeling changes in space &

time time Biogeochemical cycling, ecosystem dynamics, Biogeochemical cycling, ecosystem dynamics,

natural resource management, etc.natural resource management, etc.

• Population dynamics assumes …Population dynamics assumes … All organisms & substrates are well mixedAll organisms & substrates are well mixedAll organisms of same species are identical & all All organisms of same species are identical & all

are at the same physiological stateare at the same physiological stateAll organisms experience the same environmentAll organisms experience the same environment

Predictability of Microbial Predictability of Microbial CommunitiesCommunities

An Artistic Representation of a An Artistic Representation of a Microbial Community Microbial Community

Art by Farooq Azam

•Spatially organized Spatially organized Hot spots of activityHot spots of activityMostly unoccupiedMostly unoccupied

•How important is spatial clustering on How important is spatial clustering on population dynamics?population dynamics?

•Subgrid scale problemSubgrid scale problemUnresolved processes regulate population Unresolved processes regulate population level dynamicslevel dynamics

• Make Make discrete analog of a discrete analog of a population system population system allowing allowing interactions with interactions with environment environment

• Solve in 3DSolve in 3D for small volume for small volume• Resolve space at Resolve space at high resolutionhigh resolution including including

flow dynamicsflow dynamics• Model the organisms’ life cyclesModel the organisms’ life cycles• CompareCompare IBM results with population-level dynamics IBM results with population-level dynamics

AfSu

Individual Based Modeling Individual Based Modeling of Microbial Communitiesof Microbial Communities

Cells assimilatelocal nutrients

Cells divide when their quotas are twice a minimum

Death occursrandomly

Dead cell nutrientsare recycled locally

Nutrients diffuseby Fick’s law

Cells can moveby sinking or

swimmingDaughter cells are located

randomly

Microbial Life Cycle ExampleMicrobial Life Cycle Example

Example Slice Through Example Slice Through DomainDomain

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Su

Example Slice Through Example Slice Through DomainDomain

Rates of Cell Interactions Rates of Cell Interactions & Competition & Competition

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Su

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IBM

High interaction: discrete & population model results matchLow interaction: they differ as cells are isolated from each otherIndividual scale interactions change result of competition

PopulationDynamicsSlow Fast

•Develop an extensible IBM framework for Develop an extensible IBM framework for studying microbial community dynamicsstudying microbial community dynamicsModel both osmotrophs & grazersModel both osmotrophs & grazers

Swimming, ingestion, reproduction, etc. Swimming, ingestion, reproduction, etc. Include the flow field characteristicsInclude the flow field characteristics

Shear, turbulence, dispersion, diffusion, etc.Shear, turbulence, dispersion, diffusion, etc.Remember to model corresponding population Remember to model corresponding population

systemsystem•Start with simple systems & work to harder onesStart with simple systems & work to harder ones

AMC’s to MesocosmsAMC’s to Mesocosms

Individual Based Modeling Individual Based Modeling of Microbial Communitiesof Microbial Communities

• Use the IBM results to guide the Use the IBM results to guide the parameterization of the microbial subgrid parameterization of the microbial subgrid scale problemscale problem Compare population dynamic & IBM results Compare population dynamic & IBM results Develop moment formulations to account for spatial Develop moment formulations to account for spatial

correlations in organismscorrelations in organisms

• Remember that our abilities to predict Remember that our abilities to predict microbial community dynamics may be microbial community dynamics may be limitedlimitedCan we predict probabilistic outcomes?Can we predict probabilistic outcomes?

Linking IBM’s to Population Linking IBM’s to Population Dynamics ModelsDynamics Models

ConclusionsConclusions• IBM is the best available tool to test IBM is the best available tool to test

the importance of individual the importance of individual organisms to microbial community organisms to microbial community dynamicsdynamics

• Will change ecology and maybe our Will change ecology and maybe our understanding of life on our planetunderstanding of life on our planet

• Need to model organisms discretely & allow Need to model organisms discretely & allow them to interact with their environmentthem to interact with their environmentModel both organisms & their fluid environsModel both organisms & their fluid environs

• Individual based models (IBM’s)Individual based models (IBM’s)Solves the dynamics of many individual agents within Solves the dynamics of many individual agents within

an evolving environmentan evolving environmentDeveloped for forestry & fisheries applicationsDeveloped for forestry & fisheries applications

• Difficulties in applying IBM to microbial Difficulties in applying IBM to microbial dynamicsdynamicsComputationally challenging Computationally challenging Hard to relate to population dynamic systemsHard to relate to population dynamic systems

Individual Based Modeling Individual Based Modeling of Microbial Communitiesof Microbial Communities