223,381 research outputs found
Towards synthetic biological approaches to resource utilization on space missions.
This paper demonstrates the significant utility of deploying non-traditional biological techniques to harness available volatiles and waste resources on manned missions to explore the Moon and Mars. Compared with anticipated non-biological approaches, it is determined that for 916 day Martian missions: 205 days of high-quality methane and oxygen Mars bioproduction with Methanobacterium thermoautotrophicum can reduce the mass of a Martian fuel-manufacture plant by 56%; 496 days of biomass generation with Arthrospira platensis and Arthrospira maxima on Mars can decrease the shipped wet-food mixed-menu mass for a Mars stay and a one-way voyage by 38%; 202 days of Mars polyhydroxybutyrate synthesis with Cupriavidus necator can lower the shipped mass to three-dimensional print a 120 m(3) six-person habitat by 85% and a few days of acetaminophen production with engineered Synechocystis sp. PCC 6803 can completely replenish expired or irradiated stocks of the pharmaceutical, thereby providing independence from unmanned resupply spacecraft that take up to 210 days to arrive. Analogous outcomes are included for lunar missions. Because of the benign assumptions involved, the results provide a glimpse of the intriguing potential of 'space synthetic biology', and help focus related efforts for immediate, near-term impact
Life-Detection Technologies for the Next Two Decades
Since its inception six decades ago, astrobiology has diversified immensely
to encompass several scientific questions including the origin and evolution of
Terran life, the organic chemical composition of extraterrestrial objects, and
the concept of habitability, among others. The detection of life beyond Earth
forms the main goal of astrobiology, and a significant one for space
exploration in general. This goal has galvanized and connected with other
critical areas of investigation such as the analysis of meteorites and early
Earth geological and biological systems, materials gathered by sample-return
space missions, laboratory and computer simulations of extraterrestrial and
early Earth environmental chemistry, astronomical remote sensing, and in-situ
space exploration missions. Lately, scattered efforts are being undertaken
towards the R&D of the novel and as-yet-space-unproven life-detection
technologies capable of obtaining unambiguous evidence of extraterrestrial
life, even if it is significantly different from Terran life. As the suite of
space-proven payloads improves in breadth and sensitivity, this is an apt time
to examine the progress and future of life-detection technologies.Comment: 6 pages, the white paper was submitted to and cited by the National
Academy of Sciences in support of the Astrobiology Science Strategy for the
Search for Life in the Univers
Degeneracy: a design principle for achieving robustness and evolvability
Robustness, the insensitivity of some of a biological system's
functionalities to a set of distinct conditions, is intimately linked to
fitness. Recent studies suggest that it may also play a vital role in enabling
the evolution of species. Increasing robustness, so is proposed, can lead to
the emergence of evolvability if evolution proceeds over a neutral network that
extends far throughout the fitness landscape. Here, we show that the design
principles used to achieve robustness dramatically influence whether robustness
leads to evolvability. In simulation experiments, we find that purely redundant
systems have remarkably low evolvability while degenerate, i.e. partially
redundant, systems tend to be orders of magnitude more evolvable. Surprisingly,
the magnitude of observed variation in evolvability can neither be explained by
differences in the size nor the topology of the neutral networks. This suggests
that degeneracy, a ubiquitous characteristic in biological systems, may be an
important enabler of natural evolution. More generally, our study provides
valuable new clues about the origin of innovations in complex adaptive systems.Comment: Accepted in the Journal of Theoretical Biology (Nov 2009
Limitations in Predicting the Space Radiation Health Risk for Exploration Astronauts
Despite years of research, understanding of the space radiation environment
and the risk it poses to long-duration astronauts remains limited. There is a
disparity between research results and observed empirical effects seen in human
astronaut crews, likely due to the numerous factors that limit terrestrial
simulation of the complex space environment and extrapolation of human clinical
consequences from varied animal models. Given the intended future of human
spaceflight, with efforts now to rapidly expand capabilities for human missions
to the moon and Mars, there is a pressing need to improve upon the
understanding of the space radiation risk, predict likely clinical outcomes of
interplanetary radiation exposure, and develop appropriate and effective
mitigation strategies for future missions. To achieve this goal, the space
radiation and aerospace community must recognize the historical limitations of
radiation research and how such limitations could be addressed in future
research endeavors. We have sought to highlight the numerous factors that limit
understanding of the risk of space radiation for human crews and to identify
ways in which these limitations could be addressed for improved understanding
and appropriate risk posture regarding future human spaceflight.Comment: Accepted for publication by Nature Microgravity (2018
Space Biosciences Division
In the Space Biosciences Division at NASA's Ames Research Center, we perform the biological research and technology development necessary to tackle the challenges of living in the extreme environments of space and to enable NASA's long-term human exploration mission. This brochure provides a broad overview for our research and development capabilities, several case study examples, and finally real-world applications and collaborative partnerships
The genotype-phenotype relationship in multicellular pattern-generating models - the neglected role of pattern descriptors
Background: A deep understanding of what causes the phenotypic variation arising from biological patterning
processes, cannot be claimed before we are able to recreate this variation by mathematical models capable of
generating genotype-phenotype maps in a causally cohesive way. However, the concept of pattern in a
multicellular context implies that what matters is not the state of every single cell, but certain emergent qualities
of the total cell aggregate. Thus, in order to set up a genotype-phenotype map in such a spatiotemporal pattern
setting one is actually forced to establish new pattern descriptors and derive their relations to parameters of the
original model. A pattern descriptor is a variable that describes and quantifies a certain qualitative feature of the
pattern, for example the degree to which certain macroscopic structures are present. There is today no general
procedure for how to relate a set of patterns and their characteristic features to the functional relationships,
parameter values and initial values of an original pattern-generating model. Here we present a new, generic
approach for explorative analysis of complex patterning models which focuses on the essential pattern features
and their relations to the model parameters. The approach is illustrated on an existing model for Delta-Notch
lateral inhibition over a two-dimensional lattice.
Results: By combining computer simulations according to a succession of statistical experimental designs,
computer graphics, automatic image analysis, human sensory descriptive analysis and multivariate data modelling,
we derive a pattern descriptor model of those macroscopic, emergent aspects of the patterns that we consider
of interest. The pattern descriptor model relates the values of the new, dedicated pattern descriptors to the
parameter values of the original model, for example by predicting the parameter values leading to particular
patterns, and provides insights that would have been hard to obtain by traditional methods.
Conclusion: The results suggest that our approach may qualify as a general procedure for how to discover and
relate relevant features and characteristics of emergent patterns to the functional relationships, parameter values
and initial values of an underlying pattern-generating mathematical model
Review of Metaheuristics and Generalized Evolutionary Walk Algorithm
Metaheuristic algorithms are often nature-inspired, and they are becoming
very powerful in solving global optimization problems. More than a dozen of
major metaheuristic algorithms have been developed over the last three decades,
and there exist even more variants and hybrid of metaheuristics. This paper
intends to provide an overview of nature-inspired metaheuristic algorithms,
from a brief history to their applications. We try to analyze the main
components of these algorithms and how and why they works. Then, we intend to
provide a unified view of metaheuristics by proposing a generalized
evolutionary walk algorithm (GEWA). Finally, we discuss some of the important
open questions.Comment: 14 page
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