Inferring Models for Microbial Dynamics

Abstract: Microbial communities are widespread from the human gut to the deep ocean and influence systems including animal development, host health, and biogeochemical cycles. Characterization of complex communities is challenging, as morphology, physiology, evolution, and sensitivity to the environment all influence microbial interactions– richness not captured in commonly used Lotka-Volterra-style models originally developed for macro-scale ecological systems. High throughput sequencing has enabled high-resolution quantification of populations within natural and synthetic communities, which could aid in the development of novel mathematical models to explain complex interactions such as diauxic shifts, cross-feeding, biofilm formation, and pH modification. Data-driven model development presents several mathematical challenges: 1) usually measurements of relative but not absolute abundances are available, 2) unmeasured dynamic variables such as nutrient levels can strongly impact populations, and 3) evaluation of all possible models and interactions is costly due to the combinatorial complexity of possible interactions. Challenges 1 and 2 manifest mathematically as identifiability issues; multiple models and parameter sets can produce the same trends in the data. To identify ensembles of possible models, we will perform parameter estimation across tens of thousands of possible models capturing the range of interaction mechanisms. Informed by commonalities of structure and behavior in the ensemble and statistical analysis of fluctuations we will develop identifiability-informed model sampling techniques to accelerate future screens and infer absolute abundance dynamics from relative abundance data.