Integrating bioinformatic strategies in spatial life science research

Abstract

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.

Differential network analysis reveals the key role of the ECM-receptor pathway in α-particle-induced malignant transformation

“…Knowledge of spaceflight injury has grown immensely in recent years, benefiting from large-scale “omics” technologies, 15 making an understanding of molecular mechanisms at the systems level possible. In 2020, Gertz et al.…”

Kinetic modelling of the cellular metabolic responses underpinning in vitro glycolysis assays

“…Such correlation of ECA/ECAR can potentially be augmented using numerical models, which can potentially be used to predict the metabolic pathway kinetics beyond experimental sensitivity, predict the kinetics of the cascading metabolic processes leading to the end point and assign numerical values to the qualitative data for comparison. Recent developments in computational biology have resulted in an increasing trend of integration of bioinformatics and experimental biology [32][33][34][35]. The emergence of systems biology markup language (SBML) has facilitated the exchange of mathematical models of cellular processes across various platforms, which can be optimised or fitted to mimic the systemic behaviour [36][37][38].…”

“…Recent developments in computational biology have resulted in an increasing trend of integration of bioinformatics and experimental biology [ 32 , 33 , 34 , 35 ]. The emergence of systems biology markup language (SBML) has facilitated the exchange of mathematical models of cellular processes across various platforms, which can be optimised or fitted to mimic the systemic behaviour [ 36 , 37 , 38 ].…”