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At least 127 records · Page 7

Parallel Variable Population Multi-Objective Optimizer (pvpmoo) v1.0

This is a parallel variable population multi-objective optimizer with an adaptive unified differential evolution algorithm or a genetic algorithm. It can also be used for single objective optimization. Some features of this code include: 1) The population size varies from generation to generation to save the total # of objective function evaluations. 2) The population is uniformly distributed to a number of parallel processors for simultaneous objective function evaluation. 3) The objective function evaluation can be attained from an external simulation program with control variables in its input file and objectives calculated from its output files. 4) The optimizer includes an adaptive unified differential evolution algorithm and a real value genetic algorithm. The parameters in the unified differential evolution algorithm can be chosen to attain any mutation schemes in the published literature.

Qiang, Ji↗

Inferences from protein and nucleic acid sequences - Early molecular evolution, divergence of kingdoms and rates of change

Description of new sensitive, objective methods for establishing the probable common ancestry of very distantly related sequences and the quantitative evolutionary change which has taken place. These methods are applied to four families of proteins and nucleic acids and evolutionary trees will be derived where possible. Of the three families containing duplications of genetic material, two are nucleic acids: transfer RNA and 5S ribosomal RNA. Both of these structures are functional in the synthesis of coded proteins, and prototypes must have been present in the cell at the inception of the fundamental coding process that all living things share. There are many types of tRNA which recognize the various nucleotide triplets and the 20 amino acids. These types are thought to have arisen as a result of many gene duplications. Relationships among these types are discussed. The 5S ribosomal RNA, presently functional in both eukaryotes and prokaryotes, is very likely descended from an early form incorporating almost a complete duplication of genetic material. The amount of evolution in the various lines can again be compared. The other two families containing duplications are proteins; ferredoxin and cytochrome c.

Dayhoff, M. O.↗

The stability of the RNA bases: implications for the origin of life

High-temperature origin-of-life theories require that the components of the first genetic material are stable. We therefore have measured the half-lives for the decomposition of the nucleobases. They have been found to be short on the geologic time scale. At 100 degreesC, the growth temperatures of the hyperthermophiles, the half-lives are too short to allow for the adequate accumulation of these compounds (t1/2 for A and G approximately 1 yr; U = 12 yr; C = 19 days). Therefore, unless the origin of life took place extremely rapidly (<100 yr), we conclude that a high-temperature origin of life may be possible, but it cannot involve adenine, uracil, guanine, or cytosine. The rates of hydrolysis at 100 degreesC also suggest that an ocean-boiling asteroid impact would reset the prebiotic clock, requiring prebiotic synthetic processes to begin again. At 0 degreesC, A, U, G, and T appear to be sufficiently stable (t1/2 >/= 10(6) yr) to be involved in a low-temperature origin of life. However, the lack of stability of cytosine at 0 degreesC (t1/2 = 17, 000 yr) raises the possibility that the GC base pair may not have been used in the first genetic material unless life arose quickly (<10(6) yr) after a sterilization event. A two-letter code or an alternative base pair may have been used instead.

Non-NASA Center↗

Inferring demographic and selective histories from population genomic data using a 2-step approach in species with coding-sparse genomes: an application to human data

Abstract The demographic history of a population, and the distribution of fitness effects (DFE) of newly arising mutations in functional genomic regions, are fundamental factors dictating both genetic variation and evolutionary trajectories. Although both demographic and DFE inference has been performed extensively in humans, these approaches have generally either been limited to simple demographic models involving a single population, or, where a complex population history has been inferred, without accounting for the potentially confounding effects of selection at linked sites. Taking advantage of the coding-sparse nature of the genome, we propose a 2-step approach in which coalescent simulations are first used to infer a complex multi-population demographic model, utilizing large non-functional regions that are likely free from the effects of background selection. We then use forward-in-time simulations to perform DFE inference in functional regions, conditional on the complex demography inferred and utilizing expected background selection effects in the estimation procedure. Throughout, recombination and mutation rate maps were used to account for the underlying empirical rate heterogeneity across the human genome. Importantly, within this framework it is possible to utilize and fit multiple aspects of the data, and this inference scheme represents a generalized approach for such large-scale inference in species with coding-sparse genomes.

Soni, Vivak (ORCID:0000000294969562)↗

A modular GUI-based program for genetic algorithm-based feedback-assisted wavefront shaping

Abstract We have developed a modular graphical user interface (GUI)-based program for use in genetic algorithm-based feedback-assisted wavefront shaping. The program uses a class-based structure to separate out the universal modules (e.g. GUI, multithreading, optimization algorithms) and hardware-specific modules (e.g. code for different SLMs and cameras). This modular design makes the program easily adaptable to a wide range of lab equipment, while providing easy access to a GUI, multithreading, and three optimization algorithms (phase-stepping, simple genetic, and microgenetic).

97 MATHEMATICS AND COMPUTING↗

Genetic Algorithm Optimization of a Cost Competitive Hybrid Rocket Booster

Performance, reliability and cost have always been drivers in the rocket business. Hybrid rockets have been late entries into the launch business due to substantial early development work on liquid rockets and later on solid rockets. Slowly the technology readiness level of hybrids has been increasing due to various large scale testing and flight tests of hybrid rockets. A remaining issue is the cost of hybrids vs the existing launch propulsion systems. This paper will review the known state of the art hybrid development work to date and incorporate it into a genetic algorithm to optimize the configuration based on various parameters. A cost module will be incorporated to the code based on the weights of the components. The design will be optimized on meeting the performance requirements at the lowest cost.

Story, George↗

Genetic Algorithm Optimization of a Cost Competitive Hybrid Rocket Booster

Performance, reliability and cost have always been drivers in the rocket business. Hybrid rockets have been late entries into the launch business due to substantial early development work on liquid rockets and solid rockets. Slowly the technology readiness level of hybrids has been increasing due to various large scale testing and flight tests of hybrid rockets. One remaining issue is the cost of hybrids versus the existing launch propulsion systems. This paper will review the known state-of-the-art hybrid development work to date and incorporate it into a genetic algorithm to optimize the configuration based on various parameters. A cost module will be incorporated to the code based on the weights of the components. The design will be optimized on meeting the performance requirements at the lowest cost.

Story, George↗

Cleanroom Microbes Survive Drying, Vacuum, and Proton Irradiation

Introduction : The goal of planetary protection at NASA is to mitigate the risk of contaminating sensitive target bodies with biological life. While many cleaning procedures have been put in place to reduce bioburden on spacecraft, microbes are experts at evolving to survive harsh conditions. Specifically, the dry, low-nutrient environment of a cleanroom (commonly used for assembly of spacecraft) can represent an environment where extremophiles can survive. Methods : Scientists at NASA MSFC wished to gather a snapshot of the microbial population within a variety of cleanrooms on site. A study was undertaken to collect air, surface, and floor samples from clean-rooms and isolate unique morphologies. From this study, 95 isolates were collected and saved in a microbial library. About 86% of these were identified at least to a genus level. Following identification, 24 microbes were selected, based on a literature review, as potential extremophiles. These were grown in liquid cultures, diluted to a set optical density, washed with water, and then applied to a sterilized Kapton coupon. Droplets were allowed to dry overnight in a biosafety cabinet. Coupons were then installed in a pelletron and pumped down to high vacuum (~1E-6 Torr). Samples were then subjected 100 keV protons at a fluence of 2x10 15 p+/cm 2 up to 4x10 15 p+/cm 2 . Following exposure, samples were returned to the microbiology lab where they were pro-cessed by submerging in water, vortexing, and then plating either droplets or spread plates. Recovery data collected was qualitative with a ranking or +, minor, or – for growth. Some selected radiotolerant strains were sequenced using the Illumina sequencing platform. The resulting genomes were annotated with the Rapid Annotations using Subsystems Technology (RAST) server and analyzed for conserved and unique stress response relevant genomic signatures to identify clues related to specific tolerances. Results and Discussion : After five rounds of proton radiation, we narrowed our isolates to five, non-spore forming bacteria that demonstrated survival: Arthrobacter koreensis, Paenarthrobacter nitroguajacolicus, Mycetocola manganoxydans , and an Erwinia sp. Furthermore, we exposed these four microbes to 254 nm wavelength light at an intensity of 80 W/m 2 at a distance of ~18 cm for 10 minutes. Only A. koreensis demonstrated survival following UV exposure. Finally, we performed whole genome sequencing on the four strains to look for genetic markers of stress resistance. When we compared the genomes of the four strains, we found that genes coding for GGDEF and EAL domains with PAS/PAC sensors were only found in A. koreensis . These domains, modulated by PAS/PAC sensors, are hypothesized to facilitate survival under drying, desiccation, and proton irradiation. Drying and Desiccation : PAS domains sense hydration changes and modulate GGDEF and EAL domain activity to adjust c-di-GMP levels, enhancing resistance to desiccation. For instance, in Pseudomonas aeruginosa , the PAS domain of RbdA modulates activity under varying hydration conditions, affecting stress responses [1]. Proton Irradiation : Proton irradiation causes oxidative stress, leading to ROS generation. PAS domains detect this stress and modulate GGDEF and EAL domains to manage oxidative stress responses. In Shewanella , EAL domain proteins modulated by PAS sensors help bacteria adapt to extreme conditions [2]. These genes upregulate other stress response genes, protecting membrane function, protein stability, DNA repair, and antioxidant defenses. The modulation of c-di-GMP by PAS domains is crucial for bacterial adaptation to stress conditions, enabling dynamic physio-logical adjustments [3]. Understanding these mechanisms provides insights into bacterial stress responses and strategies for controlling bacterial growth [4]. Conclusions : These findings indicate that clean-rooms harbor extremophile microbes that may be able to survive conditions in deep space. Furthermore, while we identified certain stress-response genes that may be at least partly responsible for the phenotypes observed in this study, there are likely unidentified genes or characteristics about A. koreensis , and other bacteria, that may allow them to survive in harsh environments. Future studies will focus on identifying these unknown genes and characteristics, further elucidating the mechanisms of extremophile survival and potentially informing the development of new biotechnologies for space exploration and other extreme environments.

Chelsi Cassilly↗

Deterministic Design Optimization of Structures in OpenMDAO Framework

Nonlinear programming algorithms play an important role in structural design optimization. Several such algorithms have been implemented in OpenMDAO framework developed at NASA Glenn Research Center (GRC). OpenMDAO is an open source engineering analysis framework, written in Python, for analyzing and solving Multi-Disciplinary Analysis and Optimization (MDAO) problems. It provides a number of solvers and optimizers, referred to as components and drivers, which users can leverage to build new tools and processes quickly and efficiently. Users may download, use, modify, and distribute the OpenMDAO software at no cost. This paper summarizes the process involved in analyzing and optimizing structural components by utilizing the framework s structural solvers and several gradient based optimizers along with a multi-objective genetic algorithm. For comparison purposes, the same structural components were analyzed and optimized using CometBoards, a NASA GRC developed code. The reliability and efficiency of the OpenMDAO framework was compared and reported in this report.

Coroneos, Rula M.↗

Deterministic Design Optimization of Structures in OpenMDAO Framework

Nonlinear programming algorithms play an important role in structural design optimization. Several such algorithms have been implemented in OpenMDAO framework developed at NASA Glenn Research Center (GRC). OpenMDAO is an open source engineering analysis framework, written in Python, for analyzing and solving Multi-Disciplinary Analysis and Optimization (MDAO) problems. It provides a number of solvers and optimizers, referred to as components and drivers, which users can leverage to build new tools and processes quickly and efficiently. Users may download, use, modify, and distribute the OpenMDAO software at no cost. This paper summarizes the process involved in analyzing and optimizing structural components by utilizing the framework s structural solvers and several gradient based optimizers along with a multi-objective genetic algorithm. For comparison purposes, the same structural components were analyzed and optimized using CometBoards, a NASA GRC developed code. The reliability and efficiency of the OpenMDAO framework was compared and reported in this report.

Coroneos, Rula M.↗

Full ribosomal operon sequencing of anaerobic gut fungi (phylum Neocallimastigomycota ): insights on its markers and phylogenetic resolution

The phylogenetic affiliations of anaerobic gut fungi (Neocallimastigomycota) are typically evaluated using single-gene markers. However, this approach often fails to resolve relationships between closely related lineages. To address this issue and identify alternative markers, we created a curated database comprising the complete ribosomal operon sequences of 156 isolates, representing 20 of the 22 recognized genera and two new genus-level clades. Using long-read sequencing, we obtained ~9 kbp operon sequences and developed a robust analysis pipeline. Incorporating both coding genes and non-coding regions (excluding IGS1) improved phylogenetic resolution. This phylogenetic approach successfully resolved the Cyllamyces and Caecomyces clades (hard-to-distinguish genetically), as well as seven analysed Piromyces species. We also scanned the operon for markers that are suitable for short-read sequencing platforms, with the aim of enhancing biodiversity and phylogenetic studies. Notably, the ETS1 genetic region also enabled the distinction between these lineages, indicating its phylogenetic value within the ribosomal operon. The resulting database is a valuable resource for expanding and strengthening phylogenetic frameworks.

High-throughput sequencing↗

FluxRETAP: a REaction TArget Prioritization genome-scale modeling technique for selecting genetic targets

MOTIVATION: Metabolic engineering is rapidly evolving as a result of new advances in synthetic biology tools and automation platforms that enable high throughput strain construction, as well as the development of machine learning tools (ML) for biology. However, selecting genetic engineering targets that effectively guide the metabolic engineering process is still challenging. ML can provide predictive power for synthetic biology, but current technical limitations prevent the independent use of ML approaches without previous biological knowledge. RESULTS: Here, we present FluxRETAP, a simple and computationally inexpensive method that leverages the prior mechanistic knowledge embedded in genome-scale models for suggesting targets for genetic overexpression, downregulation or deletion, with the final goal of increasing the production of a desired metabolite. This method can provide a list of desirable engineering targets that can be combined with current ML pipelines. FluxRETAP captured 100% of reaction targets experimentally verified to improve Escherichia coli isoprenol production, 50% of targets that experimentally improved taxadiene production in E. coli and ∼60% of genetic targets from a verified minimal constrained cut-set in Pseudomonas putida, while providing additional high priority targets that could be tested. Overall, FluxRETAP is an efficient algorithm for identifying a prioritized list of testable genetic and reaction targets. AVAILABILITY AND IMPLEMENTATION: FluxRETAP is implemented in python and released under the creative commons license. The implementation and code are freely available at: https://github.com/JBEI/FluxRETAP.

Czajka, Jeffrey J↗

Genetic algorithm optimization of nuclear criticality experiment for reduction of intermediate-energy 239 Pu nuclear data uncertainties

Nuclear criticality experiments are conducted to investigate specific nuclear data important for safe handling and storage of fissile materials, reactor design and operation, and the validation of radiation transport codes. Incorrect or uncertain nuclear data can prohibitively impact operational safety limits, reactor licensing, and predictive simulation capability; therefore, integral measurements from criticality experiments are necessary and should be performed frequently. To maximize the impact of the integral measurements, it is important to consider experiment geometry, material selection, and component dimensions. When taking these considerations into account, the experiment design process becomes iterative and very time intensive. This work utilizes a genetic algorithm to efficiently explore potential nuclear criticality experiment designs for the Laboratory Directed Research & Development project PARADIGM (PARallel Approach of Differential and InteGral Measurements) at Los Alamos National Laboratory. In this paper, the building blocks of the genetic algorithm are discussed in detail, the genetic algorithm methodology is verified, and the genetic algorithm is used to produce three candidate experiment models for the final PARADIGM design. The three candidate models produced by the genetic algorithm consist of copper-reflected assemblies containing 14 repeating units of alumina, graphite, boron, and plutonium plates. Furthermore, in addition to the optimization results, final design considerations are also discussed for designs with a height and/or weight very close to or slightly above assembly machine operational limits.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Studying Functions of All Yeast Genes Simultaneously

A method of studying the functions of all the genes of a given species of microorganism simultaneously has been developed in experiments on Saccharomyces cerevisiae (commonly known as baker's or brewer's yeast). It is already known that many yeast genes perform functions similar to those of corresponding human genes; therefore, by facilitating understanding of yeast genes, the method may ultimately also contribute to the knowledge needed to treat some diseases in humans. Because of the complexity of the method and the highly specialized nature of the underlying knowledge, it is possible to give only a brief and sketchy summary here. The method involves the use of unique synthetic deoxyribonucleic acid (DNA) sequences that are denoted as DNA bar codes because of their utility as molecular labels. The method also involves the disruption of gene functions through deletion of genes. Saccharomyces cerevisiae is a particularly powerful experimental system in that multiple deletion strains easily can be pooled for parallel growth assays. Individual deletion strains recently have been created for 5,918 open reading frames, representing nearly all of the estimated 6,000 genetic loci of Saccharomyces cerevisiae. Tagging of each deletion strain with one or two unique 20-nucleotide sequences enables identification of genes affected by specific growth conditions, without prior knowledge of gene functions. Hybridization of bar-code DNA to oligonucleotide arrays can be used to measure the growth rate of each strain over several cell-division generations. The growth rate thus measured serves as an index of the fitness of the strain.

Stolc, Viktor↗

Comparison of Evolutionary (Genetic) Algorithm and Adjoint Methods for Multi-Objective Viscous Airfoil Optimizations

A comparison between an Evolutionary Algorithm (EA) and an Adjoint-Gradient (AG) Method applied to a two-dimensional Navier-Stokes code for airfoil design is presented. Both approaches use a common function evaluation code, the steady-state explicit part of the code,ARC2D. The parameterization of the design space is a common B-spline approach for an airfoil surface, which together with a common griding approach, restricts the AG and EA to the same design space. Results are presented for a class of viscous transonic airfoils in which the optimization tradeoff between drag minimization as one objective and lift maximization as another, produces the multi-objective design space. Comparisons are made for efficiency, accuracy and design consistency.

Pulliam, T. H.↗

Signatures of Selection for Resistance/Tolerance to Perkinsus olseni in Grooved Carpet Shell Clam ( Ruditapes decussatus ) Using a Population Genomics Approach

ABSTRACT The grooved carpet shell clam ( Ruditapes decussatus ) is a bivalve of high commercial value distributed throughout the European coast. Its production has suffered a decline caused by different factors, especially by the parasite Perkinsus olsenii . Improving production of R . decussatus requires genomic resources to ascertain the genetic factors underlying resistance/tolerance to P. olseni i . In this study, the first reference genome of R . decussatus was assembled through long‐ and short‐read sequencing (1677 contigs; 1.386 Mb) and further scaffolded at chromosome level with Hi‐C (19 superscaffolds; 95.4% of assembly). Repetitive elements were identified (32%) and masked for annotation of 38,276 coding‐ and 13,056 non‐coding genes. This genome was used as a reference to develop a 2bRAD‐Seq 13,438 SNP panel for a genomic screening on six shellfish beds distributed across the Atlantic Ocean and Mediterranean Sea. Beds were selected by perkinsosis prevalence and the infection level was individually evaluated in all the samples. Genetic diversity was significantly higher in the Mediterranean than in the Atlantic region. The main genetic breakage was detected between those regions (F ST = 0.224), being the Mediterranean more heterogeneous than the Atlantic. Several loci under divergent selection (394 outliers; 261 genomic windows) were detected across shellfish beds. Samples were also inspected to detect signals of selection for resistance/tolerance to P. olseni i by using infection‐level and population‐genomics approaches, and 90 common divergent outliers for resistance/tolerance to perkinsosis were identified and used for gene mining. Candidate genes and markers identified provide invaluable information for controlling perkinsosis and for improving production of the grooved carpet shell clam.

Sambade, Inés M. [Department of Zoology, Genetics ↗

Exploring genetic diversity, population structure, and subgenome differences in the allopolyploid Camelina sativa : implications for future breeding and research studies

Abstract Camelina (Camelina sativa), an allohexaploid species, is an emerging aviation biofuel crop that has been the focus of resurgent interest in recent decades. To guide future breeding and crop improvement efforts, the community requires a deeper comprehension of subgenome dominance, often noted in allopolyploid species, “alongside an understanding of the genetic diversity” and population structure of material present within breeding programs. We conducted population genetic analyses of a C. sativa diversity panel, leveraging a new genome, to estimate nucleotide diversity and population structure, and analyzed for patterns of subgenome expression dominance among different organs. Our analyses confirm that C. sativa has relatively low genetic diversity and show that the SG3 subgenome has substantially lower genetic diversity compared to the other two subgenomes. Despite the low genetic diversity, our analyses identified 13 distinct subpopulations including two distinct wild populations and others putatively representing founders in existing breeding populations. When analyzing for subgenome composition of long non-coding RNAs, which are known to play important roles in (a)biotic stress tolerance, we found that the SG3 subgenome contained significantly more lincRNAs compared to other subgenomes. Similarly, transcriptome analyses revealed that expression dominance of SG3 is not as strong as previously reported and may not be universal across all organ types. From a global analysis, SG3 “was only significant higher expressed” in flower, flower bud, and fruit organs, which is an important discovery given that the crop yield is associated with these organs. Collectively, these results will be valuable for guiding future breeding efforts in camelina.

Agriculture↗

Deep Diversity: Novel Approach to Overcoming the PCR Bias Encountered During Environmental Analysis of Microbial Populations for Alpha-Diversity

Alpha-diversity studies are of crucial importance to environmental microbiologists. The polymerase chain reaction (PCR) method has been paramount for studies interrogating microbial environmental samples for taxon richness. Phylogenetic studies using this technique are based on the amplification and comparison of the 16S rRNA coding regions. PCR, due disproportionate distribution of microbial species in the environment, increasingly favors the amplification of the most predominant phylotypes with every subsequent reaction cycle. The genetic and chemical complexity of environmental samples are intrinsic factors that exacerbate an inherit bias in PCR-based quantitative and qualitative studies of microbial communities. We report that treatment of a genetically complex total genomic environmental DNA extract with Propidium Monoazide (PMA), a DNA intercalating molecule capable of forming a covalent cross-linkage to organic moieties upon light exposure, disproportionally inactivates predominant phylotypes and results in the exponential amplification of previously shadowed microbial ?-diversity quantified as a 19.5% increase in OUTs reported via phylogenetic screening using PhyloChip.

phylogenetic microarray technologies↗