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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Studying Microbial Adaptation in the Laboratory: Sensor & Control Upgrades for an Experimental Evolution Biofluidics System

Experimental evolution (EE) involves iteratively exposing a microbial community to specific stressors to study its response to changes in environment over time. EE work is commonly done manually in the laboratory, but, when there are many environmental variables to measure and adjust, it is highly labor intensive, prone to human error, and challenging to scale. Single-purpose automated continuous culturing chambers exist, but implement only limited stressor types. A more general-purpose design is desirable. The BeING Lab at Ames Research Center created the prototype Automated Adaptive Directed Evolution Chamber (AADEC) to address these problems, beginning with Escherichia coli tolerance of short-wave ultraviolet (UV-C) radiation and of temperature. In newer versions, AADEC monitors microbial activity and can adjust the UV-C and temperature levels automatically. An optical density measurement is used to determine how many cells are present in the growth medium—over time, this corresponds to how many survive and reproduce. Oxidation-reduction potential provides information on consumed metabolic energy, and pH and electrical conductivity on metabolic products. Dissolved oxygen content is used to determine aerobic vs anaerobic growth. A Raspberry Pi computer processes all this data to set the UV-C stressor level. AADEC’s auxiliary systems include peristaltic pumps to change media and agitation to counteract cell settling. These actuators can also act as additional stressors. With the Raspberry Pi monitoring sensors and adjusting actuators in real time, AADEC takes measurements and controls the environment much more accurately than can be done with a manual EE implementation. The third and latest AADEC iteration is the first to simplify design and usage with circuits on PCBs and the ability to pre-program experimental protocols. Still planned is expansion to a multi-well design for the study of varying cell cultures in parallel, which will enable researchers to retain and re-inoculate cultures exhibiting the desired trait most strongly while flushing out others. AADEC’s special capabilities make it a valuable tool for studying life under multiple stressors, enabling scientists to replicate changes in climate on microbes for study in a lab setting.

Microbial Adaptation↗
NASA NTRS2019

Employing Automated Experimental Evolution to Understand Survival Strategies of Lab-Grown Extremophiles

Experimental evolution (EE) exposes microbes to intentional stressors to improve resistance through artificial mutation. The resulting changes to metabolic pathways, protein structure, and genetic sequences, along with traditional genetic engineering tools, to can help understand the mechanisms of improved tolerance. An automated experimental set-up -- the Automated Adaptive Directed Evolution Chamber (AADEC) -- with minimal scope for human interference was developed at NASA Ames. A second- generation device integrating more real-time biochemical sensors has been developed recently. Added sensors include pH for indicating metabolic products, oxidation-reduction potential (ORP) for indicating available/consumed metabolic energy, dissolved oxygen (DO) for indicating aerobic/anaerobic growth cycles, and electrical conductivity (EC) as an additional indicator of metabolic products. With four additional sensors, the system is biochemically more informative in real-time. More importantly, each sensor parameter can be used as a selection pressure, individually or in combination with others, to artificially create and control inhospitable environments analogous to extremophile habitats for microbial growth in the lab. Potential stressors to be added in the future include thermal, reactive oxygen species, metal-ion concentrations, and varying nutrient availability.

Automated↗
NASA NTRS2017

An Autonomous System for Experimental Evolution of Microbial Cultures: Test Results Using Ultraviolet-C Radiation and Escherichia Coli.

At its core, the field of microbial experimental evolution seeks to elucidate the natural laws governing the history of microbial life by understanding its underlying driving mechanisms. However, observing evolution in nature is complex, as environmental conditions are difficult to control. Laboratory-based experiments for observing population evolution provide more control, but manually culturing and studying multiple generations of microorganisms can be time consuming, labor intensive, and prone to inconsistency. We have constructed a prototype, closed system device that automates the process of directed evolution experiments in microorganisms. It is compatible with any liquid microbial culture, including polycultures and field samples, provides flow control and adjustable agitation, continuously monitors optical density (OD), and can dynamically control environmental pressures such as ultraviolet-C (UV-C) radiation and temperature. Here, the results of the prototype are compared to iterative exposure and survival assays conducted using a traditional hood, UV-C lamp, and shutter system.

Microbial Cultures↗
NASA NTRS2018

Enabling Experimental Evolution: Multi-Parameter Sensor System Integration into a Culture/Stressor Biofluidics System

Experimental evolution (EE) exposes microbial communities to ecological stressors, simulating dynamics up to near-extinction events. Combined with comparative sequencing and other molecular tools, such data can inform the genetic and other biological mechanisms underlying extremophile adaptation, and other observed effects. Automating this type of experiment using biofluidics can mitigate many traditional obstacles, including delays in assay results and environment adjustment and the need for many replicates. A first-generation device for automating EE procedures, the Automated Adaptive Directed Evolution Chamber (AADEC), was developed at NASA Ames. UV-C radiation was the stressor, an LED-photodiode array measured optical density, magnetic agitation and peristaltic pump systems ensured nutrient availability, and Arduino microcontrollers provided control. Escherichia coli in LB kanamycin media was used for testing and performance verification. A manual laboratory procedure with timed exposure to UV-C was performed to typify tolerance acquisition. Approximately a 106 factor increase in survival ratio was recorded over multiple iterations. Currently, a second-generation device is being developed integrating more real-time sensors: redox potential (ORP), indicating available/consumed metabolic energy; dissolved oxygen (DO), indicating aerobic/anaerobic growth; pH, indicating metabolic products; and electrical conductivity (EC), another indicator of metabolic products. The EC sensor system was constructed and calibrated in-house and matched commercial sensors in the required range. A Raspberry Pi computer automated the electrical system, allowing real-time data acquisition. The fluidics card was made of CNC-milled polycarbonate for biocompatibility. Each sensor parameter can also be used as a selection pressure alone or in combination with others to create extreme microbial environments. As a proof of concept, this work demonstrated sensor operation in one pair of growth-sensor chambers. It can be expanded to a multi-chamber system to enable inter-culture comparisons and multi-population studies. The prior Arduino system will be ported to the RPi system. Future stressors to be added include thermal, reactive oxygen species, and varying nutrient availability.

Govinda Raj, Chinmayee↗
DOE OSTI2021

Genetic Basis of Chromate Adaptation and the Role of the Pre-existing Genetic Divergence during an Experimental Evolution Study with Desulfovibrio vulgaris Populations

Hexavalent chromium [Cr(VI)] is a common environmental pollutant. However, little is known about the genetic basis of microbial evolution under Cr(VI) stress and the influence of the prior evolution histories on the subsequent evolution under Cr(VI) stress. In this study, Desulfovibrio vulgaris Hildenborough (DvH), a model sulfate-reducing bacterium, was experimentally evolved for 600 generations. By evolving the replicate populations of three genetically diverse DvH clones, including ancestor (AN, without prior experimental evolution history), non-stress-evolved EC3-10, and salt stress-evolved ES9-11, the contributions of adaptation, chance, and pre-existing genetic divergence to the evolution under Cr(VI) stress were able to be dissected. Significantly decreased lag phases under Cr(VI) stress were observed in most evolved populations, while increased Cr(VI) reduction rates were primarily observed in populations evolved from EC3-10 and ES9-11. The pre-existing genetic divergence in the starting clones showed strong influences on the changes in lag phases, growth rates, and Cr(VI) reduction rates. Additionally, the genomic mutation spectra in populations evolved from different starting clones were significantly different. A total of 14 newly mutated genes obtained mutations in at least two evolved populations, suggesting their importance in Cr(VI) adaptation. An in-frame deletion mutation of one of these genes, the chromate transporter gene DVU0426, demonstrated that it played an important role in Cr(VI) tolerance. Overall, our study identified potential key functional genes for Cr(VI) tolerance and demonstrated the important role of pre-existing genetic divergence in evolution under Cr(VI) stress conditions.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2017

Developing Model Benchtop Systems for Microbial Experimental Evolution

Understanding how microbes impact an ecosystem has improved through advances of molecular and genetic tools, but creating complex systems that emulate natural biology goes beyond current technology. In fact, many chemical, biological, and metabolic pathways of even model organisms are still poorly characterized. Even then, standard laboratory techniques for testing microbial impact on environmental change can have many drawbacks; they are time-consuming, labor intensive, and are at risk of contamination. By having an automated process, many of these problems can be reduced or even eliminated. We are developing a benchtop system that can run for long periods of time without the need for human intervention, involve multiple environmental stressors at once, perform real-time adjustments of stressor exposure based on current state of the population, and minimize contamination risks. Our prototype device allows operators to generate an analogue of real world micro-scale ecosystems that can be used to model the effects of disruptive environmental change on microbial ecosystems. It comprises of electronics, mechatronics, and fluidics based systems to control, measure, and evaluate the before and after state of microbial cultures from exposure to environmental stressors. Currently, it uses four parallel growth chambers to perform tests on liquid cultures. To measure the population state, optical sensors (LED/photodiode) are used. Its primary selection pressure is UV-C radiation, a well-studied stressor known for its cell- and DNA-damaging effects and as a mutagen. Future work will involve improving the current growth chambers, as well as implementing additional sensors and environmental stressors into the system. Full integration of multiple culture testing will allow inter-culture comparisons. Besides the temperature and OD sensors, other types of sensors can be integrated such as conductivity, biomass, pH, and dissolved gasses such as CO and O. Additional environmental stressor systems like temperature (extreme heat or cold), metal toxicity, and other forms of radiation will increase the scale and testing range.

Developing↗
NASA NTRS1988

The effects of impact velocity on the evolution of experimental regoliths

Fragmental targets consisting of a coarse-grained gabbro were subjected to multiple impacts with stainless-steel spheres at 0.7, 1.4, and 1.9 km/s in order to investigate the effects of impact velocity on the generation and evolution of experimental regoliths. Although the low-velocity impactors were shown to be more efficient in terms of both mass comminution and the creating of new surfaces, the comminuted material formed by the faster projectiles possessed smaller mean grain sizes and larger proportions of fine-grained debris. The 2-4 mm material was found in all cases to exhibit a mass excess relative to the adjacent size fractions.

Cintala, Mark J.↗
DOE OSTI2026

Review of ECCS Acceptance Criteria and Experimental Basis Evolution Toward Fuel Fragmentation, Relocation, and Dispersal Studies

The U.S. nuclear industry is pursuing extensions of light water reactor (LWR) fuel burnup and enrichment limits to approximately 75 GWd/t and 10 wt.% 235 U to achieve economic and operational benefits. A central safety consideration in this effort is the behavior of high burnup (HBu) fuel during loss-of-coolant accidents (LOCAs), particularly fuel fragmentation, relocation, and dispersal (FFRD). Here, this work provides a historical and technical review of U.S. LOCA regulation and experimentation, clarifying how the evolution of Emergency Core Cooling System (ECCS) acceptance criteria in 10 CFR 50.46 has shaped both testing approaches and interpretations of fuel safety. The study revisits the original intent of the ECCS criteria, showing that the peak cladding temperature and equivalent cladding reacted limits were developed as surrogates to preserve a coolable geometry. The explicit inclusion of the coolable geometry criterion in the regulation was intended to emphasize the underlying safety philosophy and as a safeguard against unforeseen failure modes, an intent that remains directly relevant to modern concerns regarding FFRD. The review traces the lineage of HBu LOCA experiments to the Argonne National Laboratory furnace tests, from which subsequent programs at Studsvik, Halden, and Oak Ridge National Laboratory were derived. These tests employed a 5 °C/s heating rate inherited from early embrittlement studies, a stylized temperature history that does not represent actual LWR LOCA thermal-hydraulics. Comparison of these test conditions to pressurized water reactor large break LOCAs and separate effects data indicates that the existing HBu LOCA database may not be fully applicable to all LWR LOCA scenarios, from which a qualitative framework for applicability is proposed.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Laboratory evolution in Novosphingobium aromaticivorans enables rapid catabolism of a model lignin-derived aromatic dimer

Lignin contains a variety of interunit linkages, leading to a range of potential decomposition products that can be used as carbon and energy sources by microbes. β-O-4 linkages are the most common in native lignin, and associated catabolic pathways have been well characterized. However, the fate of the mono-aromatic intermediates that result from β-O-4 dimer cleavage has not been fully elucidated. Here, we used experimental evolution to identify mutant strains of Novosphingobium aromaticivorans with improved catabolism of a model aromatic dimer containing a β-O-4 linkage, guaiacylglycerol-β-guaiacyl ether (GGE). We identified several parallel causal mutations, including a single nucleotide polymorphism in the promoter of an uncharacterized gene that roughly doubled the growth yield with GGE. We characterized the associated enzyme and demonstrated that it oxidizes an intermediate in GGE catabolism, β-hydroxypropiovanillone, to vanilloyl acetaldehyde. Identification of this enzyme and its key role in GGE catabolism furthers our understanding of catabolic pathways for lignin-derived aromatic compounds.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2017

Low-Reynolds Number Aerodynamics of an 8.9 Percent Scale Semispan Swept Wing for Assessment of Icing Effects

Aerodynamic assessment of icing effects on swept wings is an important component of a larger effort to improve three-dimensional icing simulation capabilities. An understanding of ice-shape geometric fidelity and Reynolds and Mach number effects on the iced-wing aerodynamics is needed to guide the development and validation of ice-accretion simulation tools. To this end, wind-tunnel testing and computational flow simulations were carried out for an 8.9%-scale semispan wing based upon the Common Research Model airplane configuration. The wind-tunnel testing was conducted at the Wichita State University 7 ft x 10 ft Beech wind tunnel from Reynolds numbers of 0.8×10(exp 6) to 2.4×10(exp 6) and corresponding Mach numbers of 0.09 to 0.27. This paper presents the results of initial studies investigating the model mounting configuration, clean-wing aerodynamics and effects of artificial ice roughness. Four different model mounting configurations were considered and a circular splitter plate combined with a streamlined shroud was selected as the baseline geometry for the remainder of the experiments and computational simulations. A detailed study of the clean-wing aerodynamics and stall characteristics was made. In all cases, the flow over the outboard sections of the wing separated as the wing stalled with the inboard sections near the root maintaining attached flow. Computational flow simulations were carried out with the ONERA elsA software that solves the compressible, three-dimensional RANS equations. The computations were carried out in either fully turbulent mode or with natural transition. Better agreement between the experimental and computational results was obtained when considering computations with free transition compared to turbulent solutions. These results indicate that experimental evolution of the clean wing performance coefficients were due to the effect of three-dimensional transition location and that this must be taken into account for future data analysis. This research also confirmed that artificial ice roughness created with rapid-prototype manufacturing methods can generate aerodynamic performance effects comparable to grit roughness of equivalent size when proper care is exercised in design and installation. The conclusions of this combined experimental and computational study contributed directly to the successful implementation of follow-on test campaigns with numerous artificial ice-shape configurations for this 8.9% scale model.

Aerodynamics↗
NASA NTRS2017

Low-Reynolds Number Aerodynamics of an 8.9 Percent Scale Semispan Swept Wing for Assessment of Icing Effects

Aerodynamic assessment of icing effects on swept wings is an important component of a larger effort to improve three-dimensional icing simulation capabilities. An understanding of ice-shape geometric fidelity and Reynolds and Mach number effects on the iced-wing aerodynamics is needed to guide the development and validation of ice-accretion simulation tools. To this end, wind-tunnel testing and computational flow simulations were carried out for an 8.9 percent-scale semispan wing based upon the Common Research Model airplane configuration. The wind-tunnel testing was conducted at the Wichita State University 7 by 10 ft Beech wind tunnel from Reynolds numbers of 0.8×10(exp 6) to 2.4×10(exp 6) and corresponding Mach numbers of 0.09 to 0.27. This paper presents the results of initial studies investigating the model mounting configuration, clean-wing aerodynamics and effects of artificial ice roughness. Four different model mounting configurations were considered and a circular splitter plate combined with a streamlined shroud was selected as the baseline geometry for the remainder of the experiments and computational simulations. A detailed study of the clean-wing aerodynamics and stall characteristics was made. In all cases, the flow over the outboard sections of the wing separated as the wing stalled with the inboard sections near the root maintaining attached flow. Computational flow simulations were carried out with the ONERA elsA software that solves the compressible, threedimensional RANS equations. The computations were carried out in either fully turbulent mode or with natural transition. Better agreement between the experimental and computational results was obtained when considering computations with free transition compared to turbulent solutions. These results indicate that experimental evolution of the clean wing performance coefficients were due to the effect of three-dimensional transition location and that this must be taken into account for future data analysis. This research also confirmed that artificial ice roughness created with rapid-prototype manufacturing methods can generate aerodynamic performance effects comparable to grit roughness of equivalent size when proper care is exercised in design and installation. The conclusions of this combined experimental and computational study contributed directly to the successful implementation of follow-on test campaigns with numerous artificial ice-shape configurations for this 8.9 percent scale model.

Aerodynamics↗
DOE OSTI2023

Benchmarking Microscale Ductility Measurements (Final Report of the Project DE-NE0008799)

Conventional macroscale experimentation is generally considered to be straightforward with few limitations. Conversely, micro/nanoscale experimentation presents numerous challenges in loading device design, sample preparation and handling, as well as accurate understanding of grain size and local texture effects on recorded measurements. Despite these challenges, nanopillar compression, MEMs based micro-tension, and nanoindentation approaches have been able to provide fundamental contributions to the understanding of material behavior at small lengthscales. However, the overarching shortcoming of these micro/nanoscale experimentation approaches, is the inability to directly translate measurements evaluated at the nm and µm length scales (e.g., hardness) to macroscale tensile material behavior (i.e., elastic modulus, yield strength, and ductility). The objectives of the proposed study are, 1) to establish best practices for obtaining tensile microscale ductility measurements, and 2) to validate methodologies to for comparing microscale ductility measurements to macroscale ductility measurements. In order to achieve these objectives, a multi-lengthscale, multi-temperature testing protocol and simulation framework are executed first on copper as a model material to validate the following approach, and second on reactor grade Zircaloy-2. Experiments are conducted on specimens extracted from the same test piece to ensure nominally identical grain size and texture from specimens to specimen. Motivated by the need to isolate the contribution of size-effects on obtained mechanical property measurements, specimens are manufactured with thicknesses at the micro- (1-10 µm), meso- (10-100's µm), and macroscales (sub-sized ASTM E8). In-situ full-field deformation techniques (scanning electron microscopy (SEM) grid methods and optical DIC) are incorporated into testing at each specimen length-scale to capture plasticity localization and evolution. Experimental testing for all specimens is conducted at both room temperature and elevated temperatures to probe the role of thermal activation on plastic deformation accommodation processes. Simulation efforts focus on examining the mechanical behavior of microscale specimens using a finite element approach with explicitly resolved grain morphologies, and an embedded crystal plasticity model. The cost-efficient implementation method allows for the modeling of a statistically significant number of both real (i.e., digital twin) and generated microstructures to obtain an understanding of the interrelationships between specimen microstructure and geometric variables (grain size, texture, specimen geometry, etc.) on microscale mechanical behavior.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Uncertainty guided online ensemble for non-stationary data streams in fusion science

Machine Learning (ML) is poised to play a pivotal role in the development and operation of next-generation fusion devices. Fusion data shows non-stationary behavior with distribution drifts, resulted by both experimental evolution and machine wear-and-tear. ML models assume stationary distribution and fail to maintain performance when encountered with such non-stationary data streams. Online learning techniques have been leveraged in other domains, however it has been largely unexplored for fusion applications. In this paper, we investigate online learning for continuous adaptation to drifting data streams in the prediction of Toroidal Field (TF) coils deflection at the DIII-D fusion facility. We further address the short-term performance degradation inherent to standard online learning, which arises because ground truth is unavailable at prediction time. To mitigate this issue, we propose an uncertainty-guided online ensemble framework. The method leverages the Deep Gaussian Process Approximation (DGPA) for calibrated uncertainty estimation and uses these uncertainty measures to guide a meta-algorithm that aggregates predictions from learners trained over different historical horizons. Our results show that online learning reduces prediction error by 80% compared to a static model. The online ensemble and the proposed uncertainty-guided ensemble further reduce error by approximately 6%, and 10% respectively, relative to standard single-model online learning, while also providing calibrated uncertainty estimates to support operational decision-making.

AI↗
DOE OSTI2023

Unlocking the magic in mycelium: Using synthetic biology to optimize filamentous fungi for biomanufacturing and sustainability

Filamentous fungi drive carbon and nutrient cycling across our global ecosystems, through its interactions with growing and decaying flora and their constituent microbiomes. The remarkable metabolic diversity, secretion ability, and fiber-like mycelial structure that have evolved in filamentous fungi have been increasingly exploited in commercial operations. The industrial potential of mycelial fermentation ranges from the discovery and bioproduction of enzymes and bioactive compounds, the decarbonization of food and material production, to environmental remediation and enhanced agricultural production. Despite its fundamental impact in ecology and biotechnology, molds and mushrooms have not, to-date, significantly intersected with synthetic biology in ways comparable to other industrial cell factories (e.g. Escherichia coli,Saccharomyces cerevisiae, and Komagataella phaffii). In this review, we summarize a suite of synthetic biology and computational tools for the mining, engineering and optimization of filamentous fungi as a bioproduction chassis. A combination of methods across genetic engineering, mutagenesis, experimental evolution, and computational modeling can be used to address strain development bottlenecks in established and emerging industries. These include slow mycelium growth rate, low production yields, non-optimal growth in alternative feedstocks, and difficulties in downstream purification. In the scope of biomanufacturing, we then detail previous efforts in improving key bottlenecks by targeting protein processing and secretion pathways, hyphae morphogenesis, and transcriptional control. Bringing synthetic biology practices into the hidden world of molds and mushrooms will serve to expand the limited panel of host organisms that allow for commercially-feasible and environmentally-sustainable bioproduction of enzymes, chemicals, therapeutics, foods, and materials of the future.

59 BASIC BIOLOGICAL SCIENCES↗