(534e) Phenotype Prediction Through Genome-Scale Modeling and Raman Spectroscopy

A cellular phenotype is determined by its metabolic network and the response of that network to environmental conditions.  This means that both genetic and environmental perturbations can be responsible for altering the chemical composition of a cell.  This has profound implications with microorganisms as we seek to understand the response of cultures to fermentive products (e.g., tolerance to biofuels) and inhibitory substrates.  This is also of considerable interest to understand genetic manipulations designed to engineering higher cellulose content in plants.  Of course, genome-scale models represent the vital link between cellular genotype and phenotype.  However, genome-scale models contain a static biomass constituting equation.  This equation must change in response to genetic and/or environmental perturbations.  Previously, it has been shown that a genetic algorithm can draw this link based on carefully crafted objective functions.  But, how can predicted phenotype results be tested easily?  Cellular composition determination involving mass spectroscopy is time consuming, does not consider spatial variation, and is destructive to dynamically responding cultures.  Here, the power of Raman spectroscopy and surface-enhanced Raman spectroscopy (SERS) are demonstrated and interfaced with genome-scale modeling for accurate phenotype predictions.  Examples of advanced biofuel tolerance by several bacterial strains and the accumulation of higher cellulose content by Arabidopsis are presented in detail.