(582dg) Plantseed: Utilizing Functional Annotation of Plant Protein Families to Generate Metabolic Models Capable of Phenotypic Predictions

Seaver, S., Argonne National Laboratory
Henry, C. S., Argonne National Laboratory

Over the past two decades, there has been progress towards the development of in silico metabolic reconstructions of microbial organisms that enable researchers to explore the complex relationship between genotype and phenotype. The progress has advanced along three paths: the accelerated annotation of enzymes and reactions, the refinement of algorithms and techniques for both propagation of annotation and metabolic analysis, and the growth in the number of sequenced genomes.  These paths have come together through the use of subsystems technology in the form of ModelSEED, a framework for the automated reconstruction of metabolic models that has been applied to construction over 13,000 genome-scale models.

To capitalize on the growth of next-generation sequencing and the interest in exploring metabolic pathways in plants, we present PlantSEED, a unique collaboration between teams at Argonne and the University of Florida to create a specialized niche within the ModelSEED ecosystem geared towards the development of plant metabolic reconstructions.  The teams are working towards a tight integration between the current annotation of biochemistry with highly homologous and iso-functional plant-specific protein families (FIGfams) to build an ensemble of subsystems that can be utilized to automatically generate metabolic models that can be used to generate predictions of metabolic phenotypes in plant tissue.