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

1. Individual Based Modeling of Microbial Communities: Solving the Microbial Subgrid Scale Problem Dave Siegel, Satoshi Mitarai, Roger Nisbet, Bruce Kendall & Jeff Moehlis University California, Santa Barbara

2. Predictability of Microbial Communities • Definition of predictability The ability to correctly forecast the future state of a system • Microbial communities are hard to predict [well] We don’t know really who is out there Of who know, we don’t know really what they are doing Don’t know interactions among participants & their environment

3. Predictability of Microbial Communities • Classical approach – population dynamics Widely used for modeling changes in space & time Biogeochemical cycling, ecosystem dynamics, natural resource management, etc. • Population dynamics assumes … All organisms & substrates are well mixed All organisms of same species are identical & all are at the same physiological state All organisms experience the same environment

4. An Artistic Representation of a Microbial Community • Spatially organized Hot spots of activity Mostly unoccupied • How important is spatial clustering on population dynamics? • Subgrid scale problem Unresolved processes regulate population level dynamics Art by Farooq Azam

5. Individual Based Modeling of Microbial Communities • Make discrete analog of a population system allowing interactions with environment • Solve in 3D for small volume • Resolve space at high resolution including flow dynamics • Model the organisms’ life cycles • Compare IBM results with population-level dynamics Af Su

6. Microbial Life Cycle Example Cells divide when their quotas are twice a minimum Cells assimilate local nutrients Cells can move by sinking or swimming Daughter cells are located randomly Death occurs randomly Nutrients diffuse by Fick’s law Dead cell nutrients are recycled locally

7. Example Slice Through Domain Su Af

8. Example Slice Through Domain Su

9. SiO4 Su Af Rates of Cell Interactions & Competition IBM Population Dynamics Slow Fast High interaction: discrete & population model results match Low interaction: they differ as cells are isolated from each other Individual scale interactions change result of competition

10. Individual Based Modeling of Microbial Communities • Develop an extensible IBM framework for studying microbial community dynamics Model both osmotrophs & grazers Swimming, ingestion, reproduction, etc. Include the flow field characteristics Shear, turbulence, dispersion, diffusion, etc. Remember to model corresponding population system • Start with simple systems & work to harder ones AMC’s to Mesocosms

11. Linking IBM’s to Population Dynamics Models • Use the IBM results to guide the parameterization of the microbial subgrid scale problem Compare population dynamic & IBM results Develop moment formulations to account for spatial correlations in organisms • Remember that our abilities to predict microbial community dynamics may be limited Can we predict probabilistic outcomes?

12. Conclusions • IBM is the best available tool to test the importance of individual organisms to microbial community dynamics • Will change ecology and maybe our understanding of life on our planet

14. Individual Based Modeling of Microbial Communities • Need to model organisms discretely & allow them to interact with their environment Model both organisms & their fluid environs • Individual based models (IBM’s) Solves the dynamics of many individual agents within an evolving environment Developed for forestry & fisheries applications • Difficulties in applying IBM to microbial dynamics Computationally challenging Hard to relate to population dynamic systems