Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “metabolomic”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

ABF DFO with Technology Holding, Inc.

This Agile BioFoundry Directed Funding Opportunity project with Technology Holding and partners focuses on the development of both a strain of Pseudomonas putida KT2440 and a corresponding bioprocess to convert cellulosic sugars to beta-ketoadipic acid, which can be used in performance nylons and polyesters. Our approach follows the Design-Build-Test-Learn cycle wherein we have transferred learnings from muconic acid production in P. putida to develop a glucose and xylose-utilizing beta-ketoadipic acid production strain. This strain achieves 65 g/L of beta-ketoadipic acid at 0.7 g/L/hr and a C-mol yield of 0.40. We are currently on-boarding arabinose utilization as well. To identify non-intuitive strain modifications as well, we are deploying a beta-ketoadipic acid biosensor and building randomly barcoded transposon insertion sequencing (RB-TnSeq) libraries and gene over-expression libraries in beta-ketoadipic acid production strains. Moreover, we are using global metabolomics and other systems biology tools to identify off-target pathways. Lastly, we are scaling up beta-ketoadipic acid production to kg-scale production for Technology Holding to evaluate in performance polymers with their partners.

beta-ketoadipic acid↗
DOE OSTI2023

Method for simultaneous characterization and expansion of reference libraries for small molecule identification

A variational autoencoder (VAE) has been developed to learn a continuous numerical, or latent, representation of molecular structure to expand reference libraries for small molecule identification. The VAE has been extended to include a chemical property decoder, trained as a multitask network, to shape the latent representation such that it assembles according to desired chemical properties. The approach is unique in its application to metabolomics and small molecule identification, focused on properties that are obtained from experimental measurements (m/z, CCS) paired with its training paradigm, which involves a cascade of transfer learning iterations. First, molecular representation is learned from a large dataset of structures with m/z labels. Next, in silico property values are used to continue training. Finally, the network is further refined by being trained with the experimental data. The trained network is used to predict chemical properties directly from structure and generate candidate structures with desired chemical properties. The network is extensible to other training data and molecular representations, and for use with other analytical platforms, for both chemical property and feature prediction as well as molecular structure generation.

Colby, Sean M.↗
DOE OSTI2025

Neural Network Analysis of Nuclear Magnetic Resonance and Infrared Spectra

Nuclear magnetic resonance (NMR) spectroscopy and infrared (IR) spectroscopy are powerful chemical characterization techniques with broad general usage. However, the manual evaluation of the resulting spectra is time-consuming and requires significant expertise, preventing insights from being used in real-time applications. With recent advances in computation and artificial intelligence (AI), new tools are available for automating spectral interpretation. In this work, machine learning (ML) algorithms using 1-dimensional convolutional neural networks (CNNs) were applied to identify common functional groups from spectral information. Raw spectra were collected virtually from the Human Metabolome Database (HMDB) and National Institute of Standards and Technology (NIST) Chemistry WebBook and processed into a suitable standard. Algorithm design was tailored to best fit the nature of the problem, with built-in flexibility to accommodate relevant parameters beyond the raw spectral input, specifically solvent identity and magnetic frequency for NMR. The predictive capability of the algorithm in identifying functional groups is displayed in several examples. This methodology has been compiled into a code repository and could easily be modified to adapt alternative data sources, including other spectrum types. To mitigate overfitting, a common problem in mathematical modeling where overfamiliarity with training data produces trends that are not representative of the general data, a novel metric was developed, referred to as Accufit. Accufit includes a parameter that penalizes substantial differences in the training accuracy and the accuracy of an independent validation set. Examples are presented showing the effectiveness of Accufit in maintaining the model’s predictive capability while controlling the overfitting when used as a custom metric for hyperparameter tuning.

Sturgill, James↗
DOE OSTI2026

Leveraging 13C-Labeling to Assign Molecular Formulas to Unknown Yeast Metabolites

Mass spectrometry analyses have identified tens of thousands of unknown small molecule-associated peaks in different biological specimens. Notably, even the simplest and best studied organisms like Escherichia coli and Saccharomyces cerevisiae yield thousands of unknown peaks. A key question is how many of these reflect actual novel endogenous metabolites. To explore this, Mahieu and Patti used complete 13 C -labeling in E. coli to credential peaks as biological. This reduced the number of unknowns by more than 90%. Here, we carry out similar uniform 13 C-labeling in the Baker’s yeast S. cerevisiae and two less-studied bioenergy-relevant yeasts Rhodotorula toruloides (lipid producer) and Issatchenkia orientalis (organic acid producer). Identification of unknown metabolite peaks and their molecular formulas is facilitated through software tailored for 13 C labeling data and resulting knowledge of carbon atom count. A classification model evaluates the plausibility of each candidate formula, with peaks lacking plausible candidate formulas unlikely to reflect metabolite molecular ions. This approach prioritizes about one hundred candidate abundant unknown metabolites with logical molecular formulas. Most of these are species-specific rather than conserved across yeasts, and more are found in the nonmodel yeasts than S. cerevisiae. Thus, 13 C-labeling data on unknown metabolites highlights the potential for discovering new metabolites and pathways in nonmodel yeasts.

Carbon↗
DOE OSTI2025

A cytosolic glyoxylate shunt complements the canonical photorespiratory pathway in Arabidopsis

Photorespiration functions in part to support photosynthetic performance, especially under stress such as high light, yet the underlying mechanisms are poorly understood. To identify modulators of photorespiration under high light, we have isolated genetic suppressors of the photorespiratory mutant hpr1 (hydroxypyruvate reductase 1) from Arabidopsis. A suppressor that partially rescues hpr1 is mapped to GLYR1, which encodes the cytosolic glyoxylate reductase 1 that converts glyoxylate to glycolate. Independent glyr1 mutants also partially rescue hpr1 and another photorespiratory mutant, catalase 2. Our genetic, transcriptomic and metabolic profiling analyses together reveal a connection between cytosolic glyoxylate and a non-canonical photorespiratory route mediated by HPR2, which we name the photorespiratory glyoxylate shunt. This shunt complements the canonical photorespiratory pathway and is especially critical when high photorespiratory fluxes are required and when the major photorespiratory pathway is deficient. Our findings support the metabolic flexibility of photorespiration and may help to improve crop performance under stress.

Light stress↗
DOE OSTI2021

Accelerating strain phenotyping with desorption electrospray ionization-imaging mass spectrometry and untargeted analysis of intact microbial colonies

Significance Synthetic biology has entered an era in which reading and writing DNA sequences are no longer rate-limiting steps in microbial strain engineering. Indeed, analytical methods measuring the resulting metabolic outcomes of specific gene edits have lagged behind the ability to generate new recombinant strains. Herein, we report a mass spectrometry strategy to accelerate these analytical workflows by directly analyzing metabolites and molecules produced from engineered microorganisms in a multiplexed process. Using untargeted acquisitions and unsupervised analytics, we assess the molecular features that change across discrete strains including primary target species, secondary products, and species outside the engineered fatty acid biosynthesis pathway.

09 BIOMASS FUELS↗
DOE OSTI2026

Data for An End-to-End Pipeline for Succinic Acid Production at an Industrially Relevant Scale Using Issatchenkia orientalis

Microbial production of succinic acid (SA) at an industrially relevant scale has been hindered by high downstream processing costs arising from neutral pH fermentation for over three decades. Here, we metabolically engineer the acid-tolerant yeast Issatchenkia orientalis for SA production, attaining the highest titers in sugar-based media at low pH (pH 3) in fed-batch fermentations, i.e. 109.5 g/L in minimal medium and 104.6 g/L in sugarcane juice medium. We further perform batch fermentation using sugarcane juice medium in a pilot-scale fermenter (300×) and achieve 63.1 g/L of SA, which can be directly crystallized with a yield of 64.0%. Finally, we simulate an end-to-end low-pH SA production pipeline, and techno-economic analysis and life cycle assessment indicate our process is financially viable and can reduce greenhouse gas emissions by 34–90% relative to fossil-based production processes. We expect I. orientalis can serve as a general industrial platform for production of organic acids.

Metabolomics↗
DOE OSTI2026

Data for KETCHUP: Parameterizing of Large-Scale Kinetic Models Using Multiple Datasets with Different Reference States

Repository for Kinetic Estimation Tool Capturing Heterogeneous Datasets Using Pyomo (KETCHUP), a flexible parameter estimation tool that leverages a primal-dual interior-point algorithm to solve a nonlinear programming (NLP) problem that identifies a set of parameters capable of recapitulating the steady-state fluxes and concentrations in wild-type and perturbed metabolic networks. KETCHUP can use K-FIT [2] input files. Example K-FIT input files are located in the K-FIT repository at https://github.com/maranasgroup/K-FIT.

Metabolomics↗
DOE OSTI2026

Data for "Quantifying the Propagation of Parametric Uncertainty on Flux Balance Analysis"

In the repository are example scripts that perform uncertainty injection and propagation to flux balance analysis with outputs for a small sample size (for demonstration purpose only). For proper analysis, user should download the scripts and run for a large sample size (e.g., 10,000 samples). If you use the scripts, please cite the following Metabolic Engineering article: “Quantifying the propagation of parametric uncertainty on flux balance analysis” (https://doi.org/10.1016/j.ymben.2021.10.012) There are two subdirectories: /uncFBA/uncBiom: injection of normally distributed noise to biomass precursor coeffcients and ATP maintenance (growth-associated ATP maintenance (GAM) and non-growth associated ATP maintenance (NGAM)) /uncFBA/uncRHS: departure from steady-state by adding noise drawn from normal distribution to the RHS terms of mass balance constraints

Metabolomics↗
DOE OSTI2026

Data for "Metabolic Engineering Strategies to Produce Medium-Chain Oleochemicals via Acyl-ACP:CoA Transacylase Activity"

Microbial lipid metabolism is an attractive route for producing oleochemicals. The predominant strategy centers on heterologous thioesterases to synthesize desired chain-length fatty acids. To convert acids to oleochemicals (e.g., fatty alcohols, ketones), the narrowed fatty acid pool needs to be reactivated as coenzyme A thioesters at cost of one ATP per reactivation – an expense that could be saved if the acyl-chain was directly transferred from ACP- to CoA-thioester. Here, we demonstrate such an alternative acyl-transferase strategy by heterologous expression of PhaG, an enzyme first identified in Pseudomonads, that transfers 3-hydroxy acyl-chains between acyl-carrier protein and coenzyme A thioester forms for creating polyhydroxyalkanoate monomers. We use it to create a pool of acyl-CoA’s that can be redirected to oleochemical products. Through bioprospecting, mutagenesis, and metabolic engineering, we develop three strains of Escherichia coli capable of producing over 1 g/L of medium-chain free fatty acids, fatty alcohols, and methyl ketones.

Bioproducts↗
DOE OSTI2026

Data for Rewiring Yeast Metabolism for Producing 2,3-Butanediol and Two Downstream Applications: Techno-Economic Analysis and Life Cycle Assessment of Methyl Ethyl Ketone (MEK) and Agricultural Biostimulant Production

Rising concerns for sustainability and global climate change have driven the development of sustainable production pathways for biofuels and chemicals from lignocellulosic biomass via integrated biological and chemical processes. We constructed an engineered Saccharomyces cerevisiae capable of producing 2,3-butanediol (2,3-BDO) from glucose without accumulating ethanol and glycerol, which hinder downstream processing of 2,3-BDO, through extensive metabolic reprogramming. Specifically, we introduced heterologous 2,3-BDO biosynthetic enzymes and deleted the major isozymes of ethanol and glycerol biosynthetic enzymes. In addition, we introduced an NAD+ regenerating Pyruvate-Malate (PM) cycle and enhanced the NAD+ regenerating capability of the PM cycle to resolve the redox imbalance from the deletion of ethanol and glycerol production pathways. The resulting engineered yeast produced 109.9 g/L of 2,3-BDO with a productivity of 1.0 g/L/h and a yield of 0.36 g/g glucose in a fed-batch fermentation. We also conducted techno-economic analysis (TEA) and life cycle assessment (LCA) of the production of methyl ethyl ketone (MEK) through catalytic dehydration of 2,3-BDO. A TEA based on the experimental results indicated that the minimum product selling price (MPSP) was estimated to be $1.90/kg. Regarding cradle-to-grave LCA, 100-year global warming potential (GWP100) and fossil energy consumption (FEC) were found to be 0.37 kg CO2 eq/kg and 3.1 MJ/kg, respectively. These results demonstrated the feasibility of cost-competitive and sustainable bio-based MEK production via yeast fermentation. In addition, we explored the possibility of using the fermentation broth containing 2,3-BDO as a biostimulant inducing drought tolerance in plants. As a result, the yeast 2,3-BDO fermentation broth can induce drought tolerance in Arabidopsis thaliana without a complicated purification process.

Economics↗
DOE OSTI2025

High resolution characterization of soil dissolved organic matter with FTICR-MS (Fourier-transform ion cyclotron resonance mass spectrometry) from soil samples in control and warming plots in Blodgett Forest, CA (2014 and 2018)

The pathways of carbon transport and loss through and from soils—soil organic matter (SOM) depolymerization to dissolved organic carbon and mineralization to carbon dioxide (CO2)—are fundamentally driven by microbial activity, which is strongly regulated by environmental conditions. As part of Lawrence Berkeley National Laboratory (LBNL) Terrestrial Ecosystem Science (TES) Belowground Biogeochemistry Science Focus Area (SFA), we have established a novel whole-soil long-term warming experiment at the University of California (UC) Blodgett Forest Research Station (Sierra Nevada) in 2014, where we study the role of biogeochemical, microbial and geochemical process interactions in SOM decomposition and stabilization.This package contains Fourier transform ion cyclotron resonance mass spectrometry (21 Tesla FTICR-MS) data measured in negative and positive ionization mode from water and methanol soil extracts. Soil samples were collected in 2014/06/03 and 2018/06/04 from 3 replicated paired plots that had been subjected to experimental warming since June 2014 to simulate a predicted climate change scenario for northern California. The following files are included: (1) fticr_neg_h2oMeoh_data_raw.csv: raw data from combined water (H2O) and methanol (MeOH) extracts in negative ion mode, (2) fticr_neg_h2oMeoh_data_processed.csv: processed data from combined water (H2O) and methanol (MeOH) extracts in negative ion mode, (3) fticr_neg_metadata.csv: metadata for samples/measurements in negative ion mode, (4) fticr_pos_h2oMeoh_data_raw.csv: raw data from combined water (H2O) and methanol (MeOH) extracts in positive ion mode, (5) fticr_pos_h2oMeoh_data_processed.csv: processed data from combined water (H2O) and methanol (MeOH) extracts in positive ion mode, (6) fticr_pos_metadata.csv: metadata for samples/measurements in positive ion mode.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Interactions with microbial consortia have variable effects in organic carbon and production of exometabolites among genotypes of Populus trichocarpa

Abstract Poplar is a short‐rotation woody crop frequently studied for its significance as a sustainable bioenergy source. The successful establishment of a poplar plantation partially depends on its rhizosphere—a dynamic zone governed by complex interactions between plant roots and a plethora of commensal, mutualistic, symbiotic, or pathogenic microbes that shape plant fitness. In an exploratory endeavor, we investigated the effects of a consortium consisting of ectomycorrhizal fungi and a beneficial Pseudomonas sp. strain GM41 on plant growth (including height, stem girth, leaf, and root growth) and as well as growth rate over time, across four Populus trichocarpa genotypes. Additionally, we compared the level of total organic carbon and plant exometabolite profiles across different poplar genotypes in the presence of the microbial consortium. These data revealed no significant difference in plant growth parameters between the treatments and the control across four different poplar genotypes at 7 weeks post‐inoculation. However, total organic carbon and exometabolite profiles were significantly different between the genotypes and the treatments. These findings suggest that this microbial consortium has the potential to trigger early signaling responses in poplar, influencing its metabolism in ways crucial for later developmental processes and stress tolerance.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Single-Filament Imaging Mass Spectrometry Lipidomics in Arthrospira platensis

Elucidating intra-organismal biochemical and lipid organization in photosynthetic biological cell factories of filamentous cyanobacteria, i.e., Arthrospira platensis (Spirulina), is important to track physiological response mechanisms during growth. Little is known about the filaments' biochemical organization and cellular structure and no label-free imaging techniques exist that provide molecular mapping. We applied ultra-high resolution mass spectrometry (7T FT-ICR-MS) matrix-assisted laser desorption ionization (MALDI) imaging to immobilized Spirulina filaments to investigate the localization of lipids across distinct physiological regions. We optimized matrix selection and deposition methods with the goal of facilitating high spatial, and intra-filament, resolution using untargeted multivariate statistical spectral deconvolution across MS pixels. Our results demonstrate an improved two step application with an optimized procedure for intra-organismal lipid profiling to improve analyte sensitivity and achieve higher spatial resolution, whereby we evaluate three conventional matrices 2,5-dihydroxybenzoic acid (DHB), a 9:1 ratio of DHB:superDHB (sDHB), 1,5- diaminonaphthalene (DAN) and a 50:50 mix of DHB:sDHB and compare delineation and pixel-based elucidation of intra-filament lipidomics. We identified a total of 1,626 features that could be putatively assigned a lipid-like formula based on database query and 46 unique features, with associated lipid assignments that were significantly distinct in their intra-filament location. MALDI- imaging MS with untargeted statistical spectral deconvolution was used to visualize intra-filament lipidomics organization in Spirulina filaments. Improvements in matrix deposition, including sequential sublimation and pneumatic spraying, increased signal abundance at high spatial resolution and allowed for identification of distinct lipid composition regions. Here in this work outlines a methodology that may be used for micro-ecological untargeted molecular phenotyping.

09 BIOMASS FUELS↗
DOE OSTI2020

Chapter 14: Dynamic Flux Analysis: An Experimental Approach of Fluxomics

Metabolic flux analysis represents an essential perspective to understand cellular physiology and offers quantitative information to guide pathway engineering. A valuable approach for experimental elucidation of metabolic flux is dynamic flux analysis, which estimates the relative or absolute flow rates through a series of metabolic intermediates in a given pathway. It is based on kinetic isotope labeling experiments, liquid chromatography-mass spectrometry (LC-MS), and computational analysis that relate kinetic isotope trajectories of metabolites to pathway activity. Herein, we illustrate the mathematic principles underlying the dynamic flux analysis and mainly focus on describing the experimental procedures for data generation. This protocol is exemplified using cyanobacterial metabolism as an example, for which reliable labeling data for central carbon metabolites can be acquired quantitatively. This protocol is applicable to other microbial systems as well and can be readily adapted to address different metabolic processes.

BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Multi-omics profiling of the cold tolerant Monoraphidium minutum 26B-AM in response to abiotic stress

Microalgae that are of interest for biofuel production must be able to tolerate environmental changes that occur in outdoor cultivation systems. While algal cultures may experience daily temperature fluctuations and seasonal environmental changes, the underlying mechanisms that control and regulate physiological responses and adaptation to environmental pressures are largely unknown. Systems-level characterization enabled by functional genomics can help identify biochemical pathways that promote stability and productivity of algae in various environmental conditions. Monoraphidium minutum 26B-AM, a freshwater green microalga, was identified as a top performer in biomass production in winter season screens. We sequenced the genome of M. minutum 26B-AM and applied our multi-omics pipeline to profile this high potential strain under high salt and cold temperature perturbations. Through comparative analysis, including other green algae in the class Chlorophyceae, we identified gene families unique to the genus Monoraphidium, including a desaturase that has been linked to cold tolerance in plants. We observed that osmolytes, such as trehalose, proline and betaine, accumulate under salt stress, coinciding with upregulation of genes involved in biosynthesis of these metabolites. From the genome annotation, we reconstructed a metabolic model to provide a detailed map of the metabolic pathways and can be used to simulate growth and reaction fluxes. This multi-omics analysis provides a foundation to explore algal strain potential for biofuel applications, guides strain engineering, and expands our understanding of metabolic and regulatory mechanisms of algae in applied systems.

59 BASIC BIOLOGICAL SCIENCES↗