Success Stories: Incremental Progress and Scientific Breakthroughs in Life Science Research

Ruyters, Günter; Braun, Markus; Stang, Katrin Maria

doi:10.1007/978-3-030-74022-1_3

Cited by 1 publication

(1 citation statement)

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“…To protect the astronauts from these environment factors and complete space missions, there is a pressing need to understand what and how changes occur in the living systems, which will contribute to providing appropriate countermeasures to reduce the adverse effects. Furthermore, space life science research could provide insights into organismal health on Earth, such as muscle loss and osteoporosis in the elderly, as well as the impact of isolation on mental health [ 10 ]. The biological adaptation changes caused by the space environment that have been identified so far are complex and lack systematic collation, especially in terms of cascading relationships.…”

Section: Introductionmentioning

confidence: 99%

Integrating bioinformatic strategies in spatial life science research

Hao

Liang

Liu

et al. 2022

Briefings in Bioinformatics

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As space exploration programs progress, manned space missions will become more frequent and farther away from Earth, putting a greater emphasis on astronaut health. Through the collaborative efforts of researchers from various countries, the effect of the space environment factors on living systems is gradually being uncovered. Although a large number of interconnected research findings have been produced, their connection seems to be confused, and many unknown effects are left to be discovered. Simultaneously, several valuable data resources have emerged, accumulating data measuring biological effects in space that can be used to further investigate the unknown biological adaptations. In this review, the previous findings and their correlations are sorted out to facilitate the understanding of biological adaptations to space and the design of countermeasures. The biological effect measurement methods/data types are also organized to provide references for experimental design and data analysis. To aid deeper exploration of the data resources, we summarized common characteristics of the data generated from longitudinal experiments, outlined challenges or caveats in data analysis and provided corresponding solutions by recommending bioinformatics strategies and available models/tools.

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Section: Introductionmentioning

confidence: 99%