Behavior and natural selection
Genetics, environment, behavior, and natural selection processes
GENETICS↗
SEARCH · Engineering Papers
Results for “Selection (Genetics)”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Selection in and of populations
Evolution processes, and genetic selection in and out of populations
EVOLUTION↗
Microbiological Horticultural Internship Final Abstract
GMO dwarf plum (Prunus domestica) is being evaluated as a candidate food crop for long duration space flight missions. A project was undertaken to develop a protocol for transferring selected genetic lines of GMO plum (previously maintained in pots and propagated by cuttings at NASA's Kennedy Space Center in Florida) into in vitro tissue culture. In vitro culture may reduce the space, materials, and labor required to maintain the current lines of GMO plum and better preserve them for future study. Fresh plant material from three selected GMO plum lines (NASA-5, NASA-10, and NASA-11) and a non-modified control line (Control-5) were processed aseptically into in vitro culture on four separate occasions. The impact of multiple treatments on the successful growth of GMO plum tissue in vitro were tested: Parent explant tissue type (leaf petioles, stem nodes containing buds and internodes without buds), tissue sterilization method [soaking in 10 bleach only (5 min for petioles or 10 min for nodesinternodes), or soaking in 70 EtOH (30 sec) followed by 10 bleach (5 min for petioles and 10 min for nodesinternodes)], and media type [three Murashige and Skoog-based medias (SGM, SRM, and SRM+2,4-D) and one recipe containing woody plant media (WPM)]. 22.2 of the plates containing tissue sterilized with bleach alone developed microbial contamination after two weeks, while only 11.8 of plates containing tissue sterilized sequentially with EtOH and bleach developed contamination. Node bud tissue from all four genetic lines of plum produced leafy plantlets on SGM and SRM media after 4-6 weeks. The most numerous and well-developed plantlets were present on SGM. Upon reaching suitable size, plantlets were transferred to larger media containers for further growth. Some node bud growth occurred on SRM+2,4-D and WPM 2.5 weeks after plating, however as of yet no pieces on SRM+2,4-D have adequate development for transferring. Tissue pieces from NASA-5 plated on WPM are developing leaves and will be ready for transferring soon. Petioles and internode tissue lacking bud meristem failed to produce any plantlets on any plates, however petioles developed large masses of undifferentiated callus tissue on SRM+2,4-D media. These callused pieces were then transferred to SRM+TDZ media, which resulted in even larger callus growth but no differentiation. All four selected plum lines were successfully transitioned into in vitro culture. Nodes from NASA-5 and NASA-10 lines produced the most numerous and well-developed leafy plantlets in vitro, while those from NASA-11 and Control-5 were generally smaller, slower growing and less numerous. The best method overall was to use young stem node tissue with buds, surface sterilize the pieces sequentially with 70 EtOH and 10 bleach, and then plate them onto SGM media. Future areas of study will include introducing additional genetic lines of GMO plum into in vitro culture, attempting to induce shoot growth in petiole callus tissue, testing methods (such as cold storage) that extend the time interval between transferring explants into new media, and testing viability of plantlets transferred from in vitro culture back to traditional pot culture.
bioregenerative life support↗
JavaGenes: Evolving Graphs with Crossover
Genetic algorithms usually use string or tree representations. We have developed a novel crossover operator for a directed and undirected graph representation, and used this operator to evolve molecules and circuits. Unlike strings or trees, a single point in the representation cannot divide every possible graph into two parts, because graphs may contain cycles. Thus, the crossover operator is non-trivial. A steady-state, tournament selection genetic algorithm code (JavaGenes) was written to implement and test the graph crossover operator. All runs were executed by cycle-scavagging on networked workstations using the Condor batch processing system. The JavaGenes code has evolved pharmaceutical drug molecules and simple digital circuits. Results to date suggest that JavaGenes can evolve moderate sized drug molecules and very small circuits in reasonable time. The algorithm has greater difficulty with somewhat larger circuits, suggesting that directed graphs (circuits) are more difficult to evolve than undirected graphs (molecules), although necessary differences in the crossover operator may also explain the results. In principle, JavaGenes should be able to evolve other graph-representable systems, such as transportation networks, metabolic pathways, and computer networks. However, large graphs evolve significantly slower than smaller graphs, presumably because the space-of-all-graphs explodes combinatorially with graph size. Since the representation strongly affects genetic algorithm performance, adding graphs to the evolutionary programmer's bag-of-tricks should be beneficial. Also, since graph evolution operates directly on the phenotype, the genotype-phenotype translation step, common in genetic algorithm work, is eliminated.
Globus, Al↗
Structural modification of polysaccharides: A biochemical-genetic approach
Polysaccharides have a wide range of industrial and biomedical applications. An industry trend is underway towards the increased use of bacteria to produce polysaccharides. Long term goals of this work are the adaptation and enhancement of saccharide properties for electronic and optic applications. In this report we illustrate the application of enzyme-bearing bacteriophage on strains of the enteric bacterium Klebsiella pneumoniae, which produces a polysaccharide with the relatively rare rheological property of drag-reduction. This has resulted in the production of new polysaccharides with enhanced rheological properties. Our laboratory is developing techniques for processing and structurally modifying bacterial polysaccharides and oligosaccharides which comprise their basic polymeric repeat units. Our research has focused on bacteriophage which produce specific polysaccharide degrading enzymes. This has lead to the development of enzymes generated by bacteriophage as tools for polysaccharide modification and purification. These enzymes were used to efficiently convert the native material to uniform-sized high molecular weight polymers, or alternatively into high-purity oligosaccharides. Enzyme-bearing bacteriophage also serve as genetic selection tools for bacteria that produce new families of polysaccharides with modified structures.
Kern, Roger G.↗
Biochemical And Genetic Modification Of Polysaccharides
Bacteriophages producing endopolysaccharase-type enzymes used to produce, isolate, and purify high yields of modified polysaccharides from polysaccharides produced by, and incorporated into capsules of, certain bacteria. Bacteriophages used in conversion of native polysaccharide materials into polymers of nearly uniform high molecular weight or, alternatively, into highly pure oligosaccharides. Also used in genetic selection of families of polysaccharides structurally related to native polysaccharide materials, but having altered properties. Resulting new polysaccharides and oligosaccharides prove useful in variety of products, including pharmaceutical chemicals, coating materials, biologically active carbohydrates, and drag-reducing additives for fluids.
Kern, Roger G.↗
Considerations in miniaturizing simplified agro-ecosystems for advanced life support
Miniaturizing the Earth's biogeochemical cycles to support human life during future space missions is the goal of the NASA research and engineering program in advanced life support. Mission requirements to reduce mass, volume, and power have focused efforts on (1) a maximally simplified agro-ecosystem of humans, food crops, and microbes; and, (2) a design for optimized productivity of food crops with high light levels over long days, with hydroponics, with elevated carbon dioxide and other controlled environmental factors, as well as with genetic selection for desirable crop properties. Mathematical modeling contributes to the goals by establishing trade-offs, by analyzing the growth and development of experimental crops, and by pointing to the possibilities of directed phasic control using modified field crop models to increase the harvest index.
Non-NASA Center↗
Excess nutrients in hydroponic solutions alter nutrient content of rice, wheat, and potato
Environment has significant effects on the nutrient content of field-grown crop plants. Little is known, however, about compositional changes caused by controlled environments in which plants receive only artificial radiation and soilless, hydroponic culture. This knowledge is essential for developing a safe, nutritious diet in a Controlled Ecological Life-Support System (CELSS). Three crops that are candidates for inclusion in a CELSS (rice, wheat, and white potato) were grown both in the field and in controlled environments where the hydroponic nutrient solution, photosynthetic photon flux (PPF), and CO2 level were manipulated to achieve rapid growth rates. Plants were harvested at maturity, separated into discrete parts, and dried prior to analysis. Plant materials were analyzed for proximate composition (protein, fat, ash, and carbohydrate), total nitrogen (N), nitrate, minerals, and amino-acid composition. The effect of environment on nutrient content varied by crop and plant part. Total N and nonprotein N (NPN) contents of plant biomass generally increased under controlled-environment conditions compared to field conditions, especially for leafy plant parts and roots. Nitrate levels were increased in hydroponically-grown vegetative tissues, but nitrate was excluded from grains and tubers. Mineral content changes in plant tissue included increased phosphorus and decreased levels of certain micronutrient elements under controlled-environment conditions. These findings suggest that cultivar selection, genetic manipulation, and environmental control could be important to obtain highly nutritious biomass in a CELSS.
NASA Discipline Number 61-20↗
A Method for Aircraft Concept Selection Using Multicriteria Interactive Genetic Algorithms
The problem of aircraft concept selection has become increasingly difficult in recent years as a result of a change from performance as the primary evaluation criteria of aircraft concepts to the current situation in which environmental effects, economics, and aesthetics must also be evaluated and considered in the earliest stages of the decision-making process. This has prompted a shift from design using historical data regression techniques for metric prediction to the use of physics-based analysis tools that are capable of analyzing designs outside of the historical database. The use of optimization methods with these physics-based tools, however, has proven difficult because of the tendency of optimizers to exploit assumptions present in the models and drive the design towards a solution which, while promising to the computer, may be infeasible due to factors not considered by the computer codes. In addition to this difficulty, the number of discrete options available at this stage may be unmanageable due to the combinatorial nature of the concept selection problem, leading the analyst to arbitrarily choose a sub-optimum baseline vehicle. These concept decisions such as the type of control surface scheme to use, though extremely important, are frequently made without sufficient understanding of their impact on the important system metrics because of a lack of computational resources or analysis tools. This paper describes a hybrid subjective/quantitative optimization method and its application to the concept selection of a Small Supersonic Transport. The method uses Genetic Algorithms to operate on a population of designs and promote improvement by varying more than sixty parameters governing the vehicle geometry, mission, and requirements. In addition to using computer codes for evaluation of quantitative criteria such as gross weight, expert input is also considered to account for criteria such as aeroelasticity or manufacturability which may be impossible or too computationally expensive to consider explicitly in the analysis. Results indicate that concepts resulting from the use of this method represent designs which are promising to both the computer and the analyst, and that a mapping between concepts and requirements that would not otherwise be apparent is revealed.
Buonanno, Michael↗
A Parallel Approach To Optimum Actuator Selection With a Genetic Algorithm
Recent discoveries in smart technologies have created a variety of aerodynamic actuators which have great potential to enable entirely new approaches to aerospace vehicle flight control. For a revolutionary concept such as a seamless aircraft with no moving control surfaces, there is a large set of candidate locations for placing actuators, resulting in a substantially larger number of combinations to examine in order to find an optimum placement satisfying the mission requirements. The placement of actuators on a wing determines the control effectiveness of the airplane. One approach to placement Maximizes the moments about the pitch, roll, and yaw axes, while minimizing the coupling. Genetic algorithms have been instrumental in achieving good solutions to discrete optimization problems, such as the actuator placement problem. As a proof of concept, a genetic has been developed to find the minimum number of actuators required to provide uncoupled pitch, roll, and yaw control for a simplified, untapered, unswept wing model. To find the optimum placement by searching all possible combinations would require 1,100 hours. Formulating the problem and as a multi-objective problem and modifying it to take advantage of the parallel processing capabilities of a multi-processor computer, reduces the optimization time to 22 hours.
Rogers, James L.↗
Optimum Actuator Selection with a Genetic Algorithm for Aircraft Control
The placement of actuators on a wing determines the control effectiveness of the airplane. One approach to placement maximizes the moments about the pitch, roll, and yaw axes, while minimizing the coupling. For example, the desired actuators produce a pure roll moment without at the same time causing much pitch or yaw. For a typical wing, there is a large set of candidate locations for placing actuators, resulting in a substantially larger number of combinations to examine in order to find an optimum placement satisfying the mission requirements and mission constraints. A genetic algorithm has been developed for finding the best placement for four actuators to produce an uncoupled pitch moment. The genetic algorithm has been extended to find the minimum number of actuators required to provide uncoupled pitch, roll, and yaw control. A simplified, untapered, unswept wing is the model for each application.
Rogers, James L.↗
Genetic algorithm based input selection for a neural network function approximator with applications to SSME health monitoring
A genetic algorithm is used to select the inputs to a neural network function approximator. In the application considered, modeling critical parameters of the space shuttle main engine (SSME), the functional relationship between measured parameters is unknown and complex. Furthermore, the number of possible input parameters is quite large. Many approaches have been used for input selection, but they are either subjective or do not consider the complex multivariate relationships between parameters. Due to the optimization and space searching capabilities of genetic algorithms they were employed to systematize the input selection process. The results suggest that the genetic algorithm can generate parameter lists of high quality without the explicit use of problem domain knowledge. Suggestions for improving the performance of the input selection process are also provided.
Peck, Charles C.↗
From Competence to Efficiency: A Tale of GA Progress
Genetic algorithms (GAs) - search procedures based on the mechanics of natural selection and genetics - have grown in popularity for the solution of difficult optimization problems. Concomitant with this growth has been a rising cacaphony of complaint asserting that too much time must be spent by the GA practitioner diddling with codes, operators, and GA parameters; and even then these GA cassandras continue, and the user is still unsure that the effort will meet with success. At the same time, there has been a rising interest in GA theory by a growing community - a theorocracy - of mathematicians and theoretical computer scientists, and these individuals have turned their efforts increasingly toward elegant abstract theorems and proofs that seem to the practitioner to offer little in the way of answers for GA design or practice. What both groups seem to have missed is the largely unheralded 1993 assembly of integrated, applicable theory and its experimental confirmation. This theory has done two key things. First, it has predicted that simple GAs are severely limited in the difficulty of problems they can solve, and these limitations have been confirmed experimentally. Second, it has shown the path to circumventing these limitations in nontraditional GA designs such as the fast messy GA. This talk surveys the history, methodology, and accomplishment of the 1993 applicable theory revolution. After arguing that these accomplishments open the door to universal GA competence, the paper shifts the discussion to the possibility of universal GA efficiency in the utilization of time and real estate through effective parallelization, temporal decomposition, hybridization, and relaxed function evaluation. The presentation concludes by suggesting that these research directions are quickly taking us to a golden age of adaptation.
Goldberg, David E.↗
Engineering plants for spaceflight environments
The conversion efficiency of radiation into biomass and yield has steadily increased for centuries because of continued improvement in both plant genetics and environmental control. Considerable effort has gone into improving the environment for plant growth in space, but work has only begun to engineer plants for spaceflight. Genetic manipulation offers tremendous potential to improve our ability to study gravitational effects. Genetic manipulation will also be necessary to build an efficient regenerative life support system. We cannot fully characterize plant response to the spaceflight environment without understanding and manipulating their genetic composition. Identification and selection of the existing germplasm is the first step. There are thousands of cultivars of each of our major crop plants, each specifically adapted to a unique environment on our planet. Thousands of additional lines are held in national germplasm collections to maintain genetic diversity. Spaceflight imposes the need to tap this diversity. Existing lines need to be evaluated in the environment that is characteristic of closed-system spaceflight conditions. Many of the plant growth challenges we confront in space can be better solved through genetic change than by hardware engineering. Ten thousand years of plant breeding has demonstrated the value of matching genetics with the environment. For example, providing continuous light can increase plant growth in space, but this often induces calcium deficiencies because Ca is not supplied by guttation during a dark period. This deficiency cannot be eliminated through increased root-zone and foliar Ca applications. It can be solved, in wheat, through genetic selection of lines that do not have the deficiency. Subsequent comparison of lines with and without the Ca deficiency has also helped us understand the nature of the problem.
Non-NASA Center↗
Improving Search Properties in Genetic Programming
With the advancing computer processing capabilities, practical computer applications are mostly limited by the amount of human programming required to accomplish a specific task. This necessary human participation creates many problems, such as dramatically increased cost. To alleviate the problem, computers must become more autonomous. In other words, computers must be capable to program/reprogram themselves to adapt to changing environments/tasks/demands/domains. Evolutionary computation offers potential means, but it must be advanced beyond its current practical limitations. Evolutionary algorithms model nature. They maintain a population of structures representing potential solutions to the problem at hand. These structures undergo a simulated evolution by means of mutation, crossover, and a Darwinian selective pressure. Genetic programming (GP) is the most promising example of an evolutionary algorithm. In GP, the structures that evolve are trees, which is a dramatic departure from previously used representations such as strings in genetic algorithms. The space of potential trees is defined by means of their elements: functions, which label internal nodes, and terminals, which label leaves. By attaching semantic interpretation to those elements, trees can be interpreted as computer programs (given an interpreter), evolved architectures, etc. JSC has begun exploring GP as a potential tool for its long-term project on evolving dextrous robotic capabilities. Last year we identified representation redundancies as the primary source of inefficiency in GP. Subsequently, we proposed a method to use problem constraints to reduce those redundancies, effectively reducing GP complexity. This method was implemented afterwards at the University of Missouri. This summer, we have evaluated the payoff from using problem constraints to reduce search complexity on two classes of problems: learning boolean functions and solving the forward kinematics problem. We have also developed and implemented methods to use additional problem heuristics to fine-tune the searchable space, and to use typing information to further reduce the search space. Additional improvements have been proposed, but they are yet to be explored and implemented.
Janikow, Cezary Z.↗
The Identification of Scientific Programs to Utilize the Space Environment
A program to identify and develop ideas for scientific experimentation on the long duration exposure facility (LDEF) was completed. Four research proposals were developed: (1) Ultra pure germanium gamma ray radiation detectors in the space environment, intended to develop and demonstrate an X-ray and gamma-ray spectroscopy system incorporating a temperature cyclable high-purity germanium detector and diode heat pipe cryogenic system for cooling, (2) growth, morphogenesis and metabolism of plant embryos in the zero-gravity environment, to investigate if the space environment induces mutations in the embryogenic cells so that mutants of commercial significance with desirable attributes may be obtained, (3) effect of zero gravity on the growth and pathogenicity of selected zoopathic fungi. It is possible that new kinds of treatment for candidiasis, and tichophytosis could eventuate from the results of the proposed studies, and (4) importance of gravity to survival strategies of small animals. Gravitational effects may be direct or mediate the selection of genetic variants that are preadapted to weightlessness.
Kulacki, F. A.↗
Phylogenetic relationships of the Fox (Forkhead) gene family in the Bilateria
The Forkhead or Fox gene family encodes putative transcription factors. There are at least four Fox genes in yeast, 16 in Drosophila melanogaster (Dm) and 42 in humans. Recently, vertebrate Fox genes have been classified into 17 groups named FoxA to FoxQ. Here, we extend this analysis to invertebrates, using available sequences from D. melanogaster, Anopheles gambiae (Ag), Caenorhabditis elegans (Ce), the sea squirt Ciona intestinalis (Ci) and amphioxus Branchiostoma floridae (Bf), from which we also cloned several Fox genes. Phylogenetic analyses lend support to the previous overall subclassification of vertebrate genes, but suggest that four subclasses (FoxJ, L, N and Q) could be further subdivided to reflect their relationships to invertebrate genes. We were unable to identify orthologs of Fox subclasses E, H, I, J, M and Q1 in D. melanogaster, A. gambiae or C. elegans, suggesting either considerable loss in ecdysozoans or the evolution of these subclasses in the deuterostome lineage. Our analyses suggest that the common ancestor of protostomes and deuterostomes had a minimum complement of 14 Fox genes.
NASA Program Fundamental Space Biology↗
Genetic models in applied physiology: selected contribution: effects of spaceflight on immunity in the C57BL/6 mouse. I. Immune population distributions
There are several aspects of the spaceflight environment that may lead to changes in immunity: mission-related psychological stress, radiation, and changes in gravity. On December 5, 2001, the space shuttle Endeavor launched for a 12-day mission to examine these effects on C57BL/6 mice for the first time. On their return, assays were performed on the spleen, blood, and bone marrow. In response to flight, there were no significant differences in the general circulating leukocyte proportions. In contrast, there was an increase in splenic lymphocyte percentages, with a corresponding decrease in granulocytes. There was an overall shift in splenic lymphocytes away from T cells toward B cells, and a decrease in the CD4-to-CD8 ratios due to a decrease in T helpers. In contrast, there were proportional increases in bone marrow T cells, with decreases in B cells. Although the blast percentage and count were decreased in flight mice, the CD34(+) population was increased. The data were more consistent with a shift in bone marrow populations rather than a response to changes in the periphery. Many of the results are similar to those using other models. Clearly, spaceflight can influence immune parameters ranging from hematopoiesis to mature leukocyte mechanisms.
Non-NASA Center↗