Informing trait-based models with omics data: An example application modeling dynamic energy budgets in the rhizosphere

Gianna Marschmann, Lawrence Berkeley National Laboratory, Ulas Karaoz and Eoin L. Brodie, Earth and Environmental Sciences, Lawrence Berkeley National Laboratory, Berkeley, CA, Jinyun Tang, Earth Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, Kateryna Zhalnina, Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA

A challenge in scaling from microbial (genomic) diversity to ecosystem function stems from the need to quantify ecologically-relevant trait variation, and test whether useful simplifications exist in dimensions tractable for ecosystem models. I outline a modeling workflow that capitalizes upon synergistic cross-over between existing modeling capabilities (Genome-scale Metabolic models, Reaction network-based model of Soil Organic Matter and microbes, and new frontiers at the intersection of machine learning and Enzymatic Flux Cost Minimization. The interrogation of coupled microbial models can give proof-of-principle that fundamental ecology, as encoded in microbial metabolism, can be translated into improved understanding of plant-microbe interactions in soil.