A Decision Matrix for Optimal Matching of Biological Systems to Microgravity Simulation Platforms

Keywords: Space Biology, Microgravity Simulators, Machine Learning, Decision Matrix, Biological Fidelity, Space Omics

TL;DR: This paper uses machine learning on spaceflight omics data to predict the biological fidelity of ground-based microgravity simulators , creating a decision matrix to help scientists optimize experimental design for space biology research.

Abstract:

Background: Long-duration space exploration exposes biological systems to numerous stressors, necessitating robust research into their molecular and physiological effects. Ground-based microgravity simulators are essential tools, yet their biological fidelity compared to true spaceflight is poorly characterized, leading to inconsistent findings and suboptimal resource allocation.
Methods: This study employs a three-phase, integrated approach to address this challenge. First, we con
ducted a systematic meta-analysis of existing spaceflight omics data from NASA’s GeneLab to define a quantitative Biological Fidelity Score (BFS). Second, we performed prospective multi-omics validation using diverse human cell models (osteocytes, lymphocytes, cardiac organoids) representing a spectrum of intrinsic biological characteristics. Finally, we developed and validated a Random Forest
machine learning model to predict simulation fidelity based on these characteristics.
Results: Our analysis revealed a conserved core stress response to spaceflight across multiple species, centered on oxidative stress and DNA damage pathways. We also identified pronounced tissue-specific adaptations, particularly in hepatic metabolism and a systemic desynchronization of circadian rhythms. Crucially, the fidelity of ground simulators varied dramatically, with BFS values ranging from
high (>0.75) in specific cellular contexts to extremely low (<0.05) in cross-species comparisons. Our predictive model successfully identified mechanosensitivity and system complexity as key determinants of simulation fidelity.
Conclusion: This work provides the first data-driven framework for quantitatively assessing the fidelity of microgravity simulators. The resulting predictive model and decision matrix offer a powerful tool to optimize experimental design, reduce research costs,
and ensure that critical spaceflight validation is prioritized for the most pressing biological questions, thereby accelerating discoveries vital for the future of human space exploration.

Submission Number: 243