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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Metabolic engineering of Caldicellulosiruptor bescii for 2,3-butanediol production from unpretreated lignocellulosic biomass and metabolic strategies for improving yields and titers

ABSTRACT The platform chemical 2,3-butanediol (2,3-BDO) is used to derive products, such as 1,3-butadiene and methyl ethyl ketone, for the chemical and fuel production industries. Efficient microbial 2,3-BDO production at industrial scales has not been achieved yet for various reasons, including product inhibition to host organisms, mixed stereospecificity in product formation, and dependence on expensive substrates (i.e., glucose). In this study, we explore engineering of a 2,3-BDO pathway inCaldicellulosiruptor bescii, an extremely thermophilic (optimal growth temperature = 78°C) and anaerobic bacterium that can break down crystalline cellulose and hemicellulose into fermentable C 5 and C 6 sugars. In addition, C. besciigrows on unpretreated plant biomass, such as switchgrass. Biosynthesis of 2,3-BDO involves three steps: two molecules of pyruvate are condensed into acetolactate; acetolactate is decarboxylated to acetoin, and finally, acetoin is reduced to 2,3-BDO.C. besciinatively produces acetoin; therefore, in order to complete the 2,3-BDO biosynthetic pathway,C. besciiwas engineered to produce a secondary alcohol dehydrogenase (sADH) to catalyze the final step. Two previously characterized, thermostable sADH enzymes with high affinity for acetoin, one from a bacterium and one from an archaeon, were tested independently. When either sADH was present inC. bescii,the recombinant strains were able to produce up to 2.5-mM 2,3-BDO from crystalline cellulose and xylan and 0.2-mM 2,3-BDO directly from unpretreated switchgrass. This serves as the basis for higher yields and productivities, and to this end, limiting factors and potential genetic targets for further optimization were assessed using the genome-scale metabolic model ofC. bescii. IMPORTANCE Lignocellulosic plant biomass as the substrate for microbial synthesis of 2,3-butanediol is one of the major keys toward cost-effective bio-based production of this chemical at an industrial scale. However, deconstruction of biomass to release the sugars for microbial growth currently requires expensive thermochemical and enzymatic pretreatments. In this study, the thermo-cellulolytic bacteriumCaldicellulosiruptor besciiwas successfully engineered to produce 2,3-butanediol from cellulose, xylan, and directly from unpretreated switchgrass. Genome-scale metabolic modeling ofC. besciiwas applied to adjust carbon and redox fluxes to maximize productivity of 2,3-butanediol, thereby revealing bottlenecks that require genetic modifications.

Biotechnology & Applied Microbiology↗
DOE OSTI2024

Comparing Sensor Fusion and Multimodal Chemometric Models for Monitoring U(VI) in Complex Environments Representative of Irradiated Nuclear Fuel

Optical sensors and chemometric models were leveraged for the quantification of uranium(VI) (0–100 μg mL –1 ), europium (0–150 μg mL –1 ), samarium (0–250 μg mL –1 ), praseodymium (0–350 μg mL –1 ), neodymium (0–1000 μg mL –1 ), and HNO 3 (2–4 M) with varying corrosion product (iron, nickel, and chromium) levels using laser fluorescence, Raman scattering, and ultraviolet–visible–near-infrared absorption spectra. In this paper, an efficient approach to developing and evaluating tens of thousands of partial least-squares regression (PLSR) models, built from fused optical spectra or multimodal acquisitions, is discussed. Each PLSR model was optimized with unique preprocessing combinations, and features were selected using genetic algorithm filters. The 7-factor D-optimal design training set contained just 55 samples to minimize the number of samples. The performance of PLSR models was evaluated by using an automated latent variable selection script. PLS1 regression models tailored to each species outperformed a global PLS2 model. PLS1 models built using fused spectra data and a multimodal (i.e., analyzed separately) approach yielded similar information, resulting in percent root-mean-square error of prediction values of 0.9–5.7% for the seven factors. Further, the optical techniques and data processing strategies established in this study allow for the direct analysis of numerous species without measuring luminescence lifetimes or relying on a standard addition approach, making it optimal for near-real-time, in situ measurements. Nuclear reactor modeling helped bound training set conditions and identified elemental ratios of lanthanide fission products to characterize the burnup of irradiated nuclear fuel. Leveraging fluorescence, spectrophotometry, experimental design, and chemometrics can enable the remote quantification and characterization of complex systems with numerous species, monitor system performance, help identify the source of materials, and enable rapid high-throughput experiments in a variety of industrial processes and fundamental studies.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Noonan syndrome patient-specific induced cardiomyocyte model carrying SOS1 gene variant c.1654A>G

Noonan syndrome (NS) is a dominant autosomal genetic disorder, associated with mutations in several genes that exhibit multisystem abnormal development including cardiac defects. NS associated with the Son of Sevenless homolog 1 (SOS1) gene mutation attributes to the development of cardiomyopathy and congenital heart defects. Since the treatment option for NS is very limited, an in vitro disease model with SOS1 gene mutation would be beneficial for exploring therapeutic possibilities for NS. We reprogrammed cardiac fibroblasts obtained from a NS patient and normal control skin fibroblasts (C-SF) into induced pluripotent stem cells (iPSCs). We identified NS-iPSCs carry a heterozygous single nucleotide variation in the SOS1 gene at the c.1654A > G. Furthermore, the control and NS-iPSCs were differentiated into induced cardiomyocytes (iCMCs), and the electron microscopic analysis showed that the sarcomeres of the NS-iCMCs were highly disorganized. FACS analysis showed that 47.5% of the NS-iCMCs co-expressed GATA4 and cardiac troponin T proteins, and the mRNA expression levels of many cardiac related genes, studied by qRT-PCR array, were significantly reduced when compared to the control C-iCMCs. We report for the first time that NS-iPSCs carry a single nucleotide variation in the SOS1 gene at the c.1654A>G were showing significantly reduced cardiac genes and proteins expression as well as structurally and functionally compromised when compared to C-iCMCs. These iPSCs and iCMCs can be used as a modeling platform to unravel the pathologic mechanisms and also the development of novel drug for the cardiomyopathy in patients with NS.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Natural variation in the consequences of gene overexpression and its implications for evolutionary trajectories

Copy number variation through gene or chromosome amplification provides a route for rapid phenotypic variation and supports the long-term evolution of gene functions. Although the evolutionary importance of copy-number variation is known, little is understood about how genetic background influences its tolerance. Here, we measured fitness costs of over 4000 overexpressed genes in 15 Saccharomyces cerevisiae strains representing different lineages, to explore natural variation in tolerating gene overexpression (OE). Strain-specific effects dominated the fitness costs of gene OE. We report global differences in the consequences of gene OE, independent of the amplified gene, as well as gene-specific effects that were dependent on the genetic background. Natural variation in the response to gene OE could be explained by several models, including strain-specific physiological differences, resource limitations, and regulatory sensitivities. This work provides new insight on how genetic background influences tolerance to gene amplification and the evolutionary trajectories accessible to different backgrounds.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Morphological descriptors of nanoparticles: The link between atomistic structures and x-ray absorption spectra

Understanding and quantifying the morphology of nanoparticles are essential for linking their atomic structure to diverse applications and verifying theoretical models. While experimental information on the structure of nanoparticles in the size range below ∼5 nm can be extracted from x-ray absorption spectroscopy using a small number of descriptors—most commonly coordination numbers—developing an understanding of morphology descriptors from experimental data remains a challenge. Here, in this study, we introduce NanoGene, a genetic algorithm-based method for generating structurally diverse nanoparticle models guided by user-defined descriptors. We establish correlations among structural, size-related, and morphological descriptors and demonstrate how experimentally accessible parameters, such as coordination numbers, can be leveraged to infer otherwise inaccessible ones, such as the generalized coordination number or particle oblateness. Principal component and clustering analyses reveal the relative importance of descriptors, with the number of atoms emerging as a key discriminant of the nanoparticle structure. By providing both the methodology and an extensive dataset of nanoparticle geometries, this work offers a practical foundation for descriptor-based analysis and interpretation of experimental observations, bridging the gap between local atomic coordinates and global morphological characterization.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2022

Growth instabilities shape morphology and genetic diversity of microbial colonies

Abstract Cellular populations assume an incredible variety of shapes ranging from circular molds to irregular tumors. While we understand many of the mechanisms responsible for these spatial patterns, little is known about how the shape of a population influences its ecology and evolution. Here, we investigate this relationship in the context of microbial colonies grown on hard agar plates. This a well-studied system that exhibits a transition from smooth circular disks to more irregular and rugged shapes as either the nutrient concentration or cellular motility is decreased. Starting from a mechanistic model of colony growth, we identify two dimensionless quantities that determine how morphology and genetic diversity of the population depend on the model parameters. Our simulations further reveal that population dynamics cannot be accurately described by the commonly-used surface growth models. Instead, one has to explicitly account for the emergent growth instabilities and demographic fluctuations. Overall, our work links together environmental conditions, colony morphology, and evolution. This link is essential for a rational design of concrete, biophysical perturbations to steer evolution in the desired direction.

Golden, Alexander↗
DOE OSTI2022

Language models for the prediction of SARS-CoV-2 inhibitors

The COVID-19 pandemic highlights the need for computational tools to automate and accelerate drug design for novel protein targets. We leverage deep learning language models to generate and score drug candidates based on predicted protein binding affinity. We pre-trained a deep learning language model (BERT) on ∼9.6 billion molecules and achieved peak performance of 603 petaflops in mixed precision. Our work reduces pre-training time from days to hours, compared to previous efforts with this architecture, while also increasing the dataset size by nearly an order of magnitude. For scoring, we fine-tuned the language model using an assembled set of thousands of protein targets with binding affinity data and searched for inhibitors of specific protein targets, SARS-CoV-2 Mpro and PLpro. We utilized a genetic algorithm approach for finding optimal candidates using the generation and scoring capabilities of the language model. Our generalizable models accelerate the identification of inhibitors for emerging therapeutic targets.

Blanchard, Andrew E.↗
DOE OSTI2025

Targeted Chemical Looping Materials Discovery by an Inverse Design

Chemical looping with oxygen uncoupling (CLOU) materials is actively sought for combustion of carbonaceous materials to achieve complete conversion and capture of carbon dioxide. These materials may play a vital role in reducing atmospheric carbon via negative carbon output. However, there is no one‐size‐fits‐all approach as different operating conditions and feedstocks may require different CLOU materials. As a result, the exploration and discovery of high‐performance CLOU materials can be a slow process. To address this challenge, a high‐throughput inverse machine learning workflow that identifies optimum materials from perovskite oxides for a given set of targets is developed—temperature and Gibbs free energy of oxygen formation. The model is trained on high‐throughput density functional theory calculations of CLOU materials and inverts the materials design process using a genetic algorithm to produce realistic substituted SrFeO 3‐δ compositions as output. Using the inverse model, it is able to identify several interesting new families of CLOU materials: Sr 1‐ x A x Fe 1‐ y B y O 3‐δ (e.g., A = Ca or K; B = Mg, Bi, Mn, Ni, Co, Cu, or Zn). These materials have shown promising properties, and some of them even outperform the benchmark material in terms of oxygen release kinetics under relevant CLOU operating conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Giant kelp genetic monitoring before and after disturbance reveals stable genetic diversity in Southern California

Given the impacts of climate change and other anthropogenic stressors on marine systems, there is a need to accurately predict how species respond to changing environments and disturbance regimes. The use of genetic tools to monitor temporal trends in populations gives ecologists the ability to estimate changes in genetic diversity and effective population size that may be undetectable by traditional census methods. Although multiple studies have used temporal genetic analysis, they usually involve commercially important species, and rarely sample before and after disturbance. In this study, we run a temporal analysis of giant kelp, Macrocystis pyrifera , genetic diversity over the scope of 10 years (2008-2018) using the same microsatellite marker panel to assess the genetic consequences of disturbance in several populations of giant kelp ( Macrocystis pyrifera ) in the Southern California Bight. The study is a rare pre- and post-disturbance microsatellite analysis that included declines to giant kelp caused by the 2015/16 El Nino Southern Oscillation event. We used canopy biomass estimated by remote sensing (Landsat) to quantify the extent of disturbance to kelp beds, and sea surface temperature data to understand how kelp was pushed towards its temperature limits during this period. Despite prolonged periods with decreased canopy at several sites, no changes in genetic structure and allelic richness were observed. We argue that giant kelp in the region is best described as a “patchy population” system where true extinctions are rare. We discuss how deep refugia of subsurface sporophytes and cryptic microscopic life stages could have kept genetic diversity through disturbance. Given the increasing effects of climate change and uncertainty in modeling impacts of species with cryptic life history stages, we suggest further investigation to reveal the role such stages play in species resilience. Genetic monitoring studies of sites selected by remote census demographic and climate surveys should be continued in the future given the predicted impacts of climate change.

Klingbeil, III, William H.↗
DOE OSTI2026

Conjugation-based genome engineering enables rapid prototyping and bioproduction in non-model bacteria

Abstract Non-model bacteria offer unique metabolic capabilities for sustainable bioproduction, yet their limited genetic accessibility hinders systematic strain development. Here we present conjugation-based serine recombinase-assisted genome engineering (cSAGE), a broad-host-range platform that enables predictable, iterative genomic integration in transformation-resistant bacteria. cSAGE combines conjugative DNA delivery, standardized low-copy vectors, orthogonal recombinases, and modular genetic parts to support rapid pathway assembly and cross-host benchmarking. Using purple nonsulfur bacteria as a testbed, we integrate promoter engineering, multi-payload genome modification, and genome-scale metabolic modeling to empirically evaluate host-dependent pathway performance. Applying this workflow, we identify strain-specific differences in photosynthetic conversion of lignin-derived p -coumarate to the thermoplastic precursor p -vinylphenol. By enabling genome engineering and functional comparison across diverse bacteria using a single plasmid system, cSAGE provides a general framework for non-model strain prototyping and biotransformation discovery.

Guzman, Michael S. [Department of Chemical Enginee↗
DOE OSTI2022

PlasmidHostFinder: Prediction of Plasmid Hosts Using Random Forest

Plasmids play a major role facilitating the spread of antimicrobial resistance between bacteria. Understanding the host range and dissemination trajectories of plasmids is critical for surveillance and prevention of antimicrobial resistance. Identification of plasmid host ranges could be improved using automated pattern detection methods compared to homology-based methods due to the diversity and genetic plasticity of plasmids. In this study, we developed a method for predicting the host range of plasmids using machine learning—specifically, random forests. We trained the models with 8,519 plasmids from 359 different bacterial species per taxonomic level; the models achieved Matthews correlation coefficients of 0.662 and 0.867 at the species and order levels, respectively. Our results suggest that despite the diverse nature and genetic plasticity of plasmids, our random forest model can accurately distinguish between plasmid hosts. This tool is available online through the Center for Genomic Epidemiology (https://cge.cbs.dtu.dk/services/PlasmidHostFinder/).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Characterization of a novel HIV-1 circulating recombinant form, CRF91_cpx, comprising CRF02_AG, G, J, and U, mostly among men who have sex with men

Prospective molecular studies of HIV-1 pol region (2253–5250 in HXB2 genome) sequences from sequenced samples of 269 HIV-1-infected patients in Cyprus (2017–2021) revealed a transmission cluster of 14 unknown HIV-1 recombinants that were not classified as previously established CRFs. The earliest recombinant was collected in September 2017, and the transmission cluster continued to grow until November 2020. Near full-length HIV-1 genome sequences of the 11 of the 14 recombinants were successfully obtained (790–8795 in HXB2 genome) and aligned against a reference dataset of HIV-1 subtypes and CRFs. We employed MEGAX for maximum-likelihood tree construction (GTR model, 1000 bootstrap replicates), Cluster-Picker for phylogenetic clustering analysis (genetic distance ≤0.045, bootstrap support value ≥70%), and REGA-3.0 for subtype determination. Bootscan and similarity plot analyses (sliding window of 400 nucleotides overlapped by 40 nucleotides) were conducted using SimPlot-v3.5.1, and subregion confirmatory neighbour-joining tree analyses were conducted using MEGAX (Kimura two-parameter model, 1000 bootstrap replicates, ≥70% bootstrap-support value). Exclusive clustering of the HIV-1 recombinants revealed their uniqueness. The recombination analyses illustrated the same unique mosaic pattern with six putative intersubtype recombination breakpoints, seven fragments of subtypes CRF02_AG, G, J and an unclassified fragment. We conclusively characterized the mosaic structure of the novel HIV-1 CRF, named CRF91_cpx, by the Los Alamos HIV Sequence Database. Additionally, we identified a URF of CRF91_cpx with two additional recombination sites, generated by a recombination event between subtype B and CRF91_cpx. Since the identification of CRF91_cpx, two additional patient samples have been entered into the CRF91_cpx transmission cluster, demonstrating active growth.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Small Cells with Big Photosynthetic Productivities: Biotechnological Potential of the Picochlorum Genus

The Picochlorum genus is a distinctive eukaryotic green-algal clade that is the focus of several current biotechnological studies. It is capable of extremely rapid growth rates and has exceptional tolerances to high salinity, intense light, and elevated temperatures. Importantly, it has robust stability and high-biomass productivities in outdoor field trials in seawater. These features have propelled Picochlorum into the spotlight as a promising model for both fundamental and biotechnological research. Recently, several genetic tools, including genome editing, were developed for these algae, enabling insights into Picochlorum photophysiology and algal transformations for expanded capabilities. Here, we survey the Picochlorum genus, its genetic toolbox, recently characterized transformants, and discuss the commercial potential of Picochlorum as a salt-water photoautotrophic biocatalyst.

09 BIOMASS FUELS↗
DOE OSTI2023

Non-canonical d-xylose and l-arabinose metabolism via d-arabitol in the oleaginous yeast Rhodosporidium toruloides

Abstract R. toruloides is an oleaginous yeast, with diverse metabolic capacities and high tolerance for inhibitory compounds abundant in plant biomass hydrolysates. While R. toruloides grows on several pentose sugars and alcohols, further engineering of the native pathway is required for efficient conversion of biomass-derived sugars to higher value bioproducts. A previous high-throughput study inferred that R. toruloides possesses a non-canonical l -arabinose and d -xylose metabolism proceeding through d -arabitol and d -ribulose. In this study, we present a combination of genetic and metabolite data that refine and extend that model. Chiral separations definitively illustrate that d -arabitol is the enantiomer that accumulates under pentose metabolism. Deletion of putative d -arabitol-2-dehydrogenase (RTO4_9990) results in > 75% conversion of d -xylose to d -arabitol, and is growth-complemented on pentoses by heterologous xylulose kinase expression. Deletion of putative d -ribulose kinase (RTO4_14368) arrests all growth on any pentose tested. Analysis of several pentose dehydrogenase mutants elucidates a complex pathway with multiple enzymes mediating multiple different reactions in differing combinations, from which we also inferred a putative l -ribulose utilization pathway. Our results suggest that we have identified enzymes responsible for the majority of pathway flux, with additional unknown enzymes providing accessory activity at multiple steps. Further biochemical characterization of the enzymes described here will enable a more complete and quantitative understanding of R. toruloides pentose metabolism. These findings add to a growing understanding of the diversity and complexity of microbial pentose metabolism.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

DEEPER: An Intergrated Platform for Deeper Roots

Crops with deeper roots would have multiple benefits, including better drought tolerance, reduced requirement for nitrogen fertilizer, and better sequestration of atmospheric CO 2 . DEEPER is an integrated platform of phenomic, genomic, and in silico technologies to generate maize lines with deeper roots. DEEPER is: LEADER (Leaf Elemental Accumulation from Deep Roots) is a breakthrough technology to nondestructively measure rooting depth by using the plant itself as a sensor. LEADER uses handheld X-ray Fluorescence spectrometry to quantify foliar accumulation of elements that are differentially distributed in the soil profile. LEADER is nondestructive and is orders of magnitude cheaper, faster, and more precise than any competing assay of rooting depth in the field. LEADER is able to distinguish deep-rooted from shallow-rooted maize lines in the field without the need for costly and noisy soil coring. RootRobot/DIRT3D, to automatically phenotype root architecture in any field, combining RootRobot, a mechatronics platform to excavate, clean, section, and image mature root crowns, with DIRT3D, software to quantify architectural traits in 3D. Anatomics, a high-throughput platform to phenotype root anatomy, combining LAT 2.0, a technology for 3D imaging of root anatomy and composition, with RootScan3D, software to automatically extract 3D anatomical and cell wall composition metrics from LAT 2.0 output. Using this platform we discovered two novel root traits, parenchyma cell wall thickness and multiseriate cortical sclerenchyma, that improve rooting depth and drought tolerance in maize and wheat. OpenSimRoot/Deep, software to simulate root interaction with hard subsoils. Using this platform we discovered novel concepts regarding how to increase crop rooting depth by modulating how individual root axes respond to hard soil. DeepGenes, a toolkit of genes, parent lines, and genomic selection strategies to enable breeding hybrids with deeper roots. We discovered 3 novel root genes that increase rooting depth in maize and wheat. DEEPER discovered novel root phenotypes for deeper rooting, and delivered validated ideotypes for deeper-rooted maize; novel technologies to rapidly assess root depth, root architecture and anatomy in field-grown plants; novel software tools for root modeling and 3D image analysis of root architecture and anatomy; and validated genes and genomic selection models to deploy traits for deeper rooting in maize breeding. Each DEEPER technology is transformative in its own right, and exceeds existing technologies. They are mutually synergistic, deployable for field-grown plants, and are ready for application. The phenotyping and modeling technologies are readily applicable to many crops, and genetic leads in maize may have utility in other grasses. Taken as a whole they represent a transformative platform to develop deeper-rooted crops, with greater drought tolerance, reduced fertilizer requirement, and greater carbon sequestration.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Genomic-based biosurveillance for avian influenza: whole genome sequencing from wild mallards sampled during autumn migration in 2022–2023 reveals a high co-infection rate on migration stopover site in Georgia

The Caucasus region, including Georgia, is an important intersection for migratory waterbirds, offering potential for avian influenza virus (AIV) transmission between populations from different geographic areas. In 2022 and 2023, wild ducks were sampled during autumn migration events in Georgia to study the genetic relationships and molecular characteristics of influenza strains. Sequencing and phylogenetic analysis were used to compare the sampled strains to reference sequences from Africa, Asia, and Europe, allowing assessment of genetic relationships and virus transmission between migratory birds. Protein language modeling identified potential co-infections. Of 225 duck samples, 128 tested positive for the influenza M gene. 55 influenza-positive samples underwent whole-genome sequencing, revealing significant diversity. Analysis of the hemagglutinin (HA) segment showed notable differences among subtypes. Most samples were H6N1 and H6N6, but co-infections with combinations like H6H3, N8N1, N6H9, N2N6, and H9H6/N1N2 were also identified. These findings demonstrate the high variability of influenza viruses in migratory waterbirds in Georgia, including a notable rate of co-infections. Some samples exhibited uncommon genetic characteristics compared to other strains from the same year, suggesting Georgia’s role as a mixing vessel for influenza viruses. This facilitates reassortment during co-infections and contributes to the genetic diversity observed across flyways.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

An improved dataset for predicting mammal infecting viruses from genetic sequence information

There have been several attempts to develop machine learning (ML) models to identify human infecting viruses from their genomic sequences, with varying degrees of success. Direct comparison between models is problematic, because these models are typically trained and evaluated on different datasets with alternative data splitting schemes, features, and model performance metrics. In this paper we present a standardized dataset of mammal infecting and non-infecting viral pathogens, refined from the previous work of Mollentze et al. to include the latest literature evidence, roughly doubling the number of curated host-virus records available to the community, and new host target labels, primate and mammal. The new host labels were included for several reasons, including previous reports that classification performance is better at broader taxonomic ranks and the idea that there may be more data for primate infection that might serve as a suitable proxy for zoonotic potential and avoidance of false positives for human infection due to absence of evidence. On this dataset, we report the performance of eight machine learning models for predicting mammal-infecting viruses from their genomic sequences. We find that randomly assigning cases in our improved dataset to training/testing sets, when compared to the original assignments into training/testing in Mollentze et al., increases the overall average ROC AUC of prediction of human infection from 0.663 ± 0.070 to 0.784 ± 0.013, consistent with the reduction in phylogenetic distance between train and test sets (relative entropy change from 3.00 to 0.08). The broadest host category of mammal infection can be predicted most reliably at 0.850 ± 0.020. We share our improved dataset and code to enable standardized comparisons of machine learning methods to predict human host infections. Overall, we have presented preliminary evidence that classification of virus host infection is more tractable at higher taxonomic ranks, that unsurprisingly reducing the phylogenetic distance between training and test sets can improve predictive performance, that peptide kmer features appear to be harmful to out of sample model performance, and we are left with the question of whether models for virus host prediction can reasonably be expected to perform well in out of sample scenarios given the likelihood that viruses do not share a common ancestor. Consistent with this concern, when the data is resampled such that there is no overlap between viral families in training and test sets (relative entropy > 24), models perform no better than random chance at prediction of human infection regardless of whether kmers are included (ROC AUC 0.50 ± 0.08) or not (ROC AUC 0.50 ± 0.04).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

A matheuristic for design and dispatch of a utility-connected distributed energy system

Modeling distributed power generation systems often requires complicated mathematical expressions that present challenges for commercial optimization solvers. Here, this paper presents a matheuristic to solve a mixed-integer optimization model that informs decisions regarding the design and dispatch of a utility-connected microgrid. We deploy a genetic algorithm to search the system design space and a linear program to solve the economic dispatch problem. The model is a component of a web tool that requires solutions within a few minutes. Our method yields objective function values within 5% of an exogenously produced optimal in fewer than 30 seconds for 90% of our test cases compared to only 10% of our test cases by a traditional optimization solver in the same amount of time.

24 POWER TRANSMISSION AND DISTRIBUTION↗