The last decade has witnessed the tremendous power of systems biology and multi-omics tools to characterize and understand complex biological systems, computational tools to integrate and leverage multi-omics datasets to improve systems-level predictions of biological processes, and synthetic biology tools for multiplexed editing of genes, pathways, and whole genomes. An ideal integrated application of these systems-scale methods is the engineering and optimization of microbial catalysts for predictable biofuel production from lignocellulosic biomass. Despite significant achievements in this arena, cellulosic or sugar-based processes have remained the primary focus of systems and synthetic biology approaches to develop biorefineries. To achieve sustainable and economic biofuel production from lignocellulosic biomass, we must develop technologies that utilize the complete biomass, including lignin, which is currently considered as waste. To this end, our team proposes a hybrid conversion technology, where recalcitrant lignin is thermo-catalytically converted into a mixture of phenol derivatives, which are converted into bioproducts by a phenolics-utilizing bacterium Rhodococcus opacus PD630.
Our research has focused on the interrogation of the metabolic networks and genetic regulation that control the utilization of and tolerance to lignin-derived, aqueous-soluble phenolics in the triacylglycerol (TAG, a biodiesel precursor)-accumulating bacterium Rhodococcus opacus PD630. Based upon research on Rhodococcus opacus mutants that our team has generated by adaptive evolution using various aromatics as sole carbon sources (including phenol, vanillate, benzoate, guaiacol, 4-hydroxybenzoate, as well as their mixtures), we have developed a hypothesis that a major mechanism by which Rhodococcus opacus tolerates and utilizes lignin-derived compound is through up-regulation of various pathways/genes. Our approach uniquely integrates multi-omics methods, metabolic flux analyses, and novel computation tools to enhance a genome-scale model (GSM) to specify a transcriptomic "solution space" for a desired metabolic output. The research proposed in this Science Theme Proposal will enable the construction of a novel Proteomes and Transcripts to Fluxes (PT2F) model that predicts expected metabolic flux confidence intervals using a mix of machine learning and mechanistic models from transcriptomic and proteomic data. Thus, this Science Theme research proposal has three interrelated aims: Aim 1: Lignomics to predict and analyze influxes into cell metabolism for the PT2F model; Aim 2: 13C-Metabolic flux analysis (13C-MFA) to link transcription profiles to cell functional outputs and properly constrain the PT2F model; Aim 3: Proteomics to provide data for machine learning approaches involved in the PT2F model.