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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.

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At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
NASA NTRS1986

Investigations with methanobacteria and with evolution of the genetic code

Mycoplasma capricolum was found by Osawa et al. to use UGA as the code of tryptophan and to contain 75% A + T in its DNA. This change could have been from evolutionary pressure to replace C + G by A + T. Numerous studies have been reported of evolution of proteins as measured by amino acid replacements that are observed when homologus proteins, such as hemoglobins from various vertebrates, are compared. These replacements result from nucleotide substitutions in amino acid codons in the corresponding genes. Simultaneously, silent nucleotide substitutions take place that can be studied when sequences of the genes are compared. These silent evolutionary changes take place mostly in third positions of codons. Two types of nucleotide substitutions are recognized: pyrimidine-pyrimidine and purine-purine interchanges (transitions) and pyriidine-purine interchanges (transversions). Silent transitions are favored when a corresponding transversion would produce an amino acid replacement. Conversely, silent transversions are favored by probability when transitions and transversions will both be silent. Extensive examples of these situations have been found in protein genes, and it is evident that transversions in silent positions predominate in family boxes in most of the examples studied. In associated research a streptomycete from cow manure was found to produce an extracellular enzyme capable of lysing the pseudomurein-contining methanogen Methanobacterium formicicum.

Jukes, T. H.↗
DOE OSTI2025

Factors underlying a latitudinal gradient in the S/G lignin monomer ratio in natural poplar variants

The chemical composition of wood plays a pivotal role in the adaptability and structural integrity of trees. However, few studies have investigated the environmental factors that determine lignin composition and its biological significance in plants. Here, we examined the lignin syringyl-to-guaiacyl (S/G) ratio in members of a Populus trichocarpa population sourced from their native habitat and conducted a genome wide association study to identify genes linked to lignin formation. Our results revealed many significant associations, suggesting that lignin biosynthesis is a complex polygenic trait. Additionally, we found an increase in the S/G ratio from northern to southern geographic origin of the trees sampled, along with a corresponding metabolic and transcriptional reprogramming of xylem cell wall biosynthesis. Further molecular analysis identified a mutation in a cell wall laccase genetically associated with higher S/G ratios that predominate in trees from warmer lower latitudes. Collectively, our findings suggest that lignin heterogeneity arises from an evolutionary process enabling poplar adaptation to different climatic challenges.

adaptation↗
NASA NTRS1994

Multidisciplinary design optimization using genetic algorithms

Multidisciplinary design optimization (MDO) is an important step in the conceptual design and evaluation of launch vehicles since it can have a significant impact on performance and life cycle cost. The objective is to search the system design space to determine values of design variables that optimize the performance characteristic subject to system constraints. Gradient-based optimization routines have been used extensively for aerospace design optimization. However, one limitation of gradient based optimizers is their need for gradient information. Therefore, design problems which include discrete variables can not be studied. Such problems are common in launch vehicle design. For example, the number of engines and material choices must be integer values or assume only a few discrete values. In this study, genetic algorithms are investigated as an approach to MDO problems involving discrete variables and discontinuous domains. Optimization by genetic algorithms (GA) uses a search procedure which is fundamentally different from those gradient based methods. Genetic algorithms seek to find good solutions in an efficient and timely manner rather than finding the best solution. GA are designed to mimic evolutionary selection. A population of candidate designs is evaluated at each iteration, and each individual's probability of reproduction (existence in the next generation) depends on its fitness value (related to the value of the objective function). Progress toward the optimum is achieved by the crossover and mutation operations. GA is attractive since it uses only objective function values in the search process, so gradient calculations are avoided. Hence, GA are able to deal with discrete variables. Studies report success in the use of GA for aircraft design optimization studies, trajectory analysis, space structure design and control systems design. In these studies reliable convergence was achieved, but the number of function evaluations was large compared with efficient gradient methods. Applicaiton of GA is underway for a cost optimization study for a launch-vehicle fuel-tank and structural design of a wing. The strengths and limitations of GA for launch vehicle design optimization is studied.

Unal, Resit↗
NASA NTRS2006

In-Space Radiator Shape Optimization using Genetic Algorithms

Future space exploration missions will require the development of more advanced in-space radiators. These radiators should be highly efficient and lightweight, deployable heat rejection systems. Typical radiators for in-space heat mitigation commonly comprise a substantial portion of the total vehicle mass. A small mass savings of even 5-10% can greatly improve vehicle performance. The objective of this paper is to present the development of detailed tools for the analysis and design of in-space radiators using evolutionary computation techniques. The optimality criterion is defined as a two-dimensional radiator with a shape demonstrating the smallest mass for the greatest overall heat transfer, thus the end result is a set of highly functional radiator designs. This cross-disciplinary work combines topology optimization and thermal analysis design by means of a genetic algorithm The proposed design tool consists of the following steps; design parameterization based on the exterior boundary of the radiator, objective function definition (mass minimization and heat loss maximization), objective function evaluation via finite element analysis (thermal radiation analysis) and optimization based on evolutionary algorithms. The radiator design problem is defined as follows: the input force is a driving temperature and the output reaction is heat loss. Appropriate modeling of the space environment is added to capture its effect on the radiator. The design parameters chosen for this radiator shape optimization problem fall into two classes, variable height along the width of the radiator and a spline curve defining the -material boundary of the radiator. The implementation of multiple design parameter schemes allows the user to have more confidence in the radiator optimization tool upon demonstration of convergence between the two design parameter schemes. This tool easily allows the user to manipulate the driving temperature regions thus permitting detailed design of in-space radiators for unique situations. Preliminary results indicate an optimized shape following that of the temperature distribution regions in the "cooler" portions of the radiator. The results closely follow the expected radiator shape.

Hull, Patrick V.↗
DOE OSTI2022

Genetic time traveling: sequencing old herbarium specimens, including the oldest herbarium specimen sequenced from kingdom Fungi, reveals the population structure of an agriculturally significant rust

Summary Sequencing herbarium specimens can be instrumental in answering ecological, evolutionary, and taxonomic inquiries. We developed a protocol for sequencing herbarium specimens of rust fungi (Pucciniales) and proceeded to sequence specimens ranging from 4 to 211 yr old from five different genera. We then obtained sequences from an economically important biological control agent, Puccinia suaveolens , to highlight the potential of sequencing herbarium specimens in an ecological sense and to evaluate the following hypotheses: (1) The population structure of a plant pathogen changes over time, and (2) introduced pathogens are more diverse in their native range. Our efforts resulted in sequences from 87 herbarium specimens that revealed a high level of diversity with a population structure that exhibited spatial–temporal patterns. The specimens sequenced from Europe showed more diversity than the ones from North America, uncovering an invasion pattern likely related to its European native host in North America. Additionally, to the best of our knowledge, the specimen from France collected in c. 1811 is the oldest herbarium specimen sequenced from kingdom Fungi. In conclusion, sequencing old herbarium specimens is an important tool that can be extrapolated to better understand plant–microbe evolution and to evaluate old type specimens to solidify the taxonomy of plant pathogenic fungi.

Plant Sciences↗
NASA NTRS2017

Exploring Connectivity in Sequence Space of Functional RNA

Emergence of replicable genetic molecules was one of the marking points in the origin of life, evolution of which can be conceptualized as a walk through the space of all possible sequences. A theoretical concept of fitness landscape helps to understand evolutionary processes through assigning a value of fitness to each genotype. Then, evolution of a phenotype is viewed as a series of consecutive, single-point mutations. Natural selection biases evolution toward peaks of high fitness and away from valleys of low fitness. whereas neutral drift occurs in the sequence space without direction as mutations are introduced at random. Large networks of neutral or near-neutral mutations on a fitness landscape, especially for sufficiently long genomes, are possible or even inevitable. Their detection in experiments, however, has been elusive. Although a few near-neutral evolutionary pathways have been found, recent experimental evidence indicates landscapes consist of largely isolated islands. The generality of these results, however, is not clear, as the genome length or the fraction of functional molecules in the genotypic space might have been insufficient for the emergence of large, neutral networks. Thorough investigation on the structure of the fitness landscape is essential to understand the mechanisms of evolution of early genomes. RNA molecules are commonly assumed to play the pivotal role in the origin of genetic systems. They are widely believed to be early, if not the earliest, genetic and catalytic molecules, with abundant biochemical activities as aptamers and ribozymes, i.e. RNA molecules capable, respectively, to bind small molecules or catalyze chemical reactions. Here, we present results of our recent studies on the structure of the sequence space of RNA ligase ribozymes selected through in vitro evolution. Several hundred thousands of sequences active to a different degree were obtained by way of deep sequencing. Analysis of these sequences revealed several large clusters defined such that every sequence in a cluster can be reached from any other sequence in the same cluster through a series of single point mutations. Sequences in a single cluster appear to adopt more than one secondary structure. The mechanism of refolding within a single cluster was examined. To shed light on possible evolutionary paths in the space of ribozymes, the connectivity between clusters was investigated. The effect of length of RNA molecules on the structure of the fitness landscape and possible evolutionary paths was examined by way of comparing functional sequences of 20 and 80 nucleobases in length. It was found that sequences of different lengths shared secondary structure motifs that were presumed responsible for catalytic activity, with increasing complexity and global structural rearrangements emerging in longer molecules.

Wei, Chenyu↗
DOE OSTI2026

GBOpt: Grain boundary structure optimization using Monte Carlo and evolutionary algorithms

Polycrystalline materials are made of many small crystals separated by grain boundaries (GBs), whose atomic structure strongly influences material properties. Because the structure of a GB determines its properties, the optimal structure must be known in order to determine those impacts. There are many ways of placing atoms in the GB region, but the optimal structure is defined as the one that gives the lowest value of a target property (typically energy). GB structure optimization has been successfully demonstrated using stochastic and evolutionary methods, but no reusable, community-maintained open-source workflow has been developed. GBOpt (Grain Boundary Optimization) is an open-source Python package that creates that workflow, where we have presently implemented two approaches: Markov Chain Monte Carlo, and genetic algorithm based on elite selection. We demonstrate this capability by successfully reproducing the known optimal structures of a specific GB in two materials, and point interested readers to the GitHub repository for additional examples, including optimization for different properties. Both of the implemented approaches recovered the known structures, with the genetic algorithm approach finding the optimal structure faster on average.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Predictive Models of Genetic Redundancy in Arabidopsis thaliana

Abstract Genetic redundancy refers to a situation where an individual with a loss-of-function mutation in one gene (single mutant) does not show an apparent phenotype until one or more paralogs are also knocked out (double/higher-order mutant). Previous studies have identified some characteristics common among redundant gene pairs, but a predictive model of genetic redundancy incorporating a wide variety of features derived from accumulating omics and mutant phenotype data is yet to be established. In addition, the relative importance of these features for genetic redundancy remains largely unclear. Here, we establish machine learning models for predicting whether a gene pair is likely redundant or not in the model plant Arabidopsis thaliana based on six feature categories: functional annotations, evolutionary conservation including duplication patterns and mechanisms, epigenetic marks, protein properties including posttranslational modifications, gene expression, and gene network properties. The definition of redundancy, data transformations, feature subsets, and machine learning algorithms used significantly affected model performance based on holdout, testing phenotype data. Among the most important features in predicting gene pairs as redundant were having a paralog(s) from recent duplication events, annotation as a transcription factor, downregulation during stress conditions, and having similar expression patterns under stress conditions. We also explored the potential reasons underlying mispredictions and limitations of our studies. This genetic redundancy model sheds light on characteristics that may contribute to long-term maintenance of paralogs, and will ultimately allow for more targeted generation of functionally informative double mutants, advancing functional genomic studies.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2000

Towards Evolving Electronic Circuits for Autonomous Space Applications

The relatively new field of Evolvable Hardware studies how simulated evolution can reconfigure, adapt, and design hardware structures in an automated manner. Space applications, especially those requiring autonomy, are potential beneficiaries of evolvable hardware. For example, robotic drilling from a mobile platform requires high-bandwidth controller circuits that are difficult to design. In this paper, we present automated design techniques based on evolutionary search that could potentially be used in such applications. First, we present a method of automatically generating analog circuit designs using evolutionary search and a circuit construction language. Our system allows circuit size (number of devices), circuit topology, and device values to be evolved. Using a parallel genetic algorithm, we present experimental results for five design tasks. Second, we investigate the use of coevolution in automated circuit design. We examine fitness evaluation by comparing the effectiveness of four fitness schedules. The results indicate that solution quality is highest with static and co-evolving fitness schedules as compared to the other two dynamic schedules. We discuss these results and offer two possible explanations for the observed behavior: retention of useful information, and alignment of problem difficulty with circuit proficiency.

Lohn, Jason D.↗
NASA NTRS2003

Conservation of Endo16 expression in sea urchins despite evolutionary divergence in both cis and trans-acting components of transcriptional regulation

Evolutionary changes in transcriptional regulation undoubtedly play an important role in creating morphological diversity. However, there is little information about the evolutionary dynamics of cis-regulatory sequences. This study examines the functional consequence of evolutionary changes in the Endo16 promoter of sea urchins. The Endo16 gene encodes a large extracellular protein that is expressed in the endoderm and may play a role in cell adhesion. Its promoter has been characterized in exceptional detail in the purple sea urchin, Strongylocentrotus purpuratus. We have characterized the structure and function of the Endo16 promoter from a second sea urchin species, Lytechinus variegatus. The Endo16 promoter sequences have evolved in a strongly mosaic manner since these species diverged approximately 35 million years ago: the most proximal region (module A) is conserved, but the remaining modules (B-G) are unalignable. Despite extensive divergence in promoter sequences, the pattern of Endo16 transcription is largely conserved during embryonic and larval development. Transient expression assays demonstrate that 2.2 kb of upstream sequence in either species is sufficient to drive GFP reporter expression that correctly mimics this pattern of Endo16 transcription. Reciprocal cross-species transient expression assays imply that changes have also evolved in the set of transcription factors that interact with the Endo16 promoter. Taken together, these results suggest that stabilizing selection on the transcriptional output may have operated to maintain a similar pattern of Endo16 expression in S. purpuratus and L. variegatus, despite dramatic divergence in promoter sequence and mechanisms of transcriptional regulation.

NASA Discipline Evolutionary Biology↗
NASA NTRS1998

Automatic Molecular Design using Evolutionary Techniques

Molecular nanotechnology is the precise, three-dimensional control of materials and devices at the atomic scale. An important part of nanotechnology is the design of molecules for specific purposes. This paper describes early results using genetic software techniques to automatically design molecules under the control of a fitness function. The fitness function must be capable of determining which of two arbitrary molecules is better for a specific task. The software begins by generating a population of random molecules. The population is then evolved towards greater fitness by randomly combining parts of the better individuals to create new molecules. These new molecules then replace some of the worst molecules in the population. The unique aspect of our approach is that we apply genetic crossover to molecules represented by graphs, i.e., sets of atoms and the bonds that connect them. We present evidence suggesting that crossover alone, operating on graphs, can evolve any possible molecule given an appropriate fitness function and a population containing both rings and chains. Prior work evolved strings or trees that were subsequently processed to generate molecular graphs. In principle, genetic graph software should be able to evolve other graph representable systems such as circuits, transportation networks, metabolic pathways, computer networks, etc.

Globus, Al↗
DOE OSTI2020

The origin and evolution of viruses inferred from fold family structure

The canonical frameworks of viral evolution describe viruses as cellular predecessors, reduced forms of cells, or entities that escaped cellular control. The discovery of giant viruses has changed these standard paradigms. Their genetic, proteomic and structural complexities resemble those of cells, prompting a redefinition and reclassification of viruses. In a previous genome-wide analysis of the evolution of structural domains in proteomes, with domains defined at the fold superfamily level, we found the origins of viruses intertwined with those of ancient cells. In this review, we extend these data-driven analyses to the study of fold families confirming the co-evolution of viruses and ancient cells and the genetic ability of viruses to foster molecular innovation. The results support our suggestion that viruses arose by genomic reduction from ancient cells and validate a co-evolutionary ‘symbiogenic’ model of viral origins.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2001

Searching for Organics, Fossils, and Biology on Mars

One of the goals of Astrobiology is to understand life on a fundamental level. All life on Earth is constructed from the same basic biochemical building blocks consisting of 20 amino acids with left handed symmetry, five nucleotides, a few sugars of right handed symmetry and some lipids. Using the metaphor of computers this is equivalent to saying that all life shares the same hardware. Beyond hardware similarity, it is now known that all life has fundamentally the same software. The genetic code of life is common to all organisms. Some have argued that the "hammer of evolution is heavy" and life anywhere is likely to be composed of identical biochemical and genetic patterns. However, in a system as complex as biochemistry it is likely that there are numerous local optima and the details of the optimum found by evolutionary selection on another world would likely depend on the initial conditions and random developments in the early biological history on that world. To address these fundamental questions in Astrobiology we need a second example of life: a second genesis.

McKay, Christopher P.↗
DOE OSTI2026

Telomere-to-telomere assemblies of chromosome 10 reveal complex adaptive variation of 3-ketoacyl-CoA-synthases in Populus trichocarpa likely driven by Helitrons

The model woody plant Populus trichocarpa displays an atypical alkene-diverse wax cuticle likely driven by copy number variation (CNV) of 3-ketoacyl-CoA synthases ( KCS ), which has been difficult to confirm with short-read assemblies. Long-read sequencing enables the development of telomere-to-telomere resources to detect cryptic variation, including CNVs, which are currently missed. Integrating this information can improve genomic prediction for breeding and provide insights into the evolutionary basis of important traits. Our analysis of 78 long-read haplotypes from chromosome 10 identified more than twice as many KCS genes as previously reported, and numerous intragenic non-synonymous substitutions. Random Forest predictive models highlighted the importance of Potri.010G079500 in producing very long chain alkenes; however, its absence did not predict previously reported alkene-deficient phenotypes. Instead, alkene levels are best predicted by the combinations of KCS copies. Additionally, amino acid substitutions clustered around ligand and donor binding pockets, suggesting they contribute to differing wax cuticle composition. Finally, each KCS gene and copy was linked to a Helitron transposon. A phylogenetic analysis suggests Helitrons are the evolutionary mechanism for generating KCS tandem arrays. Long-read generated telomere-to-telomere assemblies of P. trichocarpa chromosome 10 revealed large-effect loci critical to genetic studies that are unattainable from short-reads. This new resource produced novel insights into genome structure and function, and a novel mechanism for generating tandem gene duplication. Our results highlight that, given current challenges in annotation and assembly, detailed and focused long-read sequences are key to interpreting complex genomic regions that contain tandem copy number variants.

09 BIOMASS FUELS↗
DOE OSTI2026

Convergent evolution of aerobic fermentation through divergent mechanisms acting on the same key glycolytic genes

Expression profiling by high throughput sequencing The model yeast Saccharomyces cerevisiae has evolved aerobic fermentation in part through a whole genome duplication and subsequent retention of glycolytic genes. To identify independent yeast lineages with a high glycolytic rate phenotype we developed an assay measuring extracellular acidification rates (ECAR) to evaluate the glycolytic rates across diverse yeast species. This assay identified a novel group of yeasts in the genus Saturnispora with rapid glycolytic rates. Through comparative approaches, we found that several glycolytic genes encoding hexokinases and enzymes in lower glycolysis had higher expression and modifications in promoter sequences in rapid ECAR species. Intriguingly, many of the upregulated genes are the same genes that are duplicated in S. cerevisiae. When the transcription factor required for their activation was deleted, the mutants had a slow glycolytic rate and more respiratory phenotype. This work shows how the aerobic fermentation phenotype has convergently evolved in Saturnispora through transcriptional rewiring. This divergent mechanism nevertheless impacts the same glycolytic genes, which suggests that there are evolutionary constraints on how aerobic fermentation can arise.

Horianopoulos, Linda C. [Department of Food Scienc↗
NASA NTRS2004

A Comparison of Techniques for Scheduling Earth-Observing Satellites

Scheduling observations by coordinated fleets of Earth Observing Satellites (EOS) involves large search spaces, complex constraints and poorly understood bottlenecks, conditions where evolutionary and related algorithms are often effective. However, there are many such algorithms and the best one to use is not clear. Here we compare multiple variants of the genetic algorithm: stochastic hill climbing, simulated annealing, squeaky wheel optimization and iterated sampling on ten realistically-sized EOS scheduling problems. Schedules are represented by a permutation (non-temperal ordering) of the observation requests. A simple deterministic scheduler assigns times and resources to each observation request in the order indicated by the permutation, discarding those that violate the constraints created by previously scheduled observations. Simulated annealing performs best. Random mutation outperform a more 'intelligent' mutator. Furthermore, the best mutator, by a small margin, was a novel approach we call temperature dependent random sampling that makes large changes in the early stages of evolution and smaller changes towards the end of search.

Globus, Al↗
DOE OSTI2022

Distant-acting Enhancers in Development and Disease

Multiple converging lines of evidence from experimental and human genetic studies indicate that both common and rare sequence variants involving enhancers play major roles in Mendelian and complex human disease phenotypes. We use a combination of sequence-based molecular approaches including chromatin mapping and synthesis-enabled modification of enhancer sequences, large-scale transgenic mouse studies (http://enhancer.lbl.gov), and CRISPR genome editing in the mouse model to study the in vivofunction of enhancers in developmental, evolutionary, and disease-related processes. Using examples from our ongoing work, I will illustrate how these methods provide insight into the function and evolution of distant-acting regulatory sequences and offer a starting point for understanding their role in human disease. I will discuss how improved in vivo mouse assays can be used to study candidate pathogenic human mutations in enhancers at scale, revealing the extent to which human genetic studies alone can be insufficient to identify truly causal mutations in enhancers. I will also show how such assays can be used to explore the sensitivity of enhancers that are extremely conserved ("ultraconserved") in evolution to mutations, revealing a remarkable functional robustness to sequence changes that contrasts with apparent evolutionary constraints.

Visel, A↗