Characterization of the random positioning machine as a microgravity simulator for biological applications

Andrada Pica, Giuseppe Uras, Ilaria Giuseppina Porco, Alessia Manca, Antonella Pantaleo and Ugo Della Croce

PLOS ONE, 2026, vol. 21, issue 6, 1-16

Abstract: Simulated microgravity platforms provide essential tools for studying gravitational effects on biological systems under controlled laboratory conditions. The Random Positioning Machine (RPM) is among the most commonly used ground-based simulators, yet quantitative evaluations of its mechanical performance and biological effects remain limited. This study provides a comprehensive mechanical and biological characterization of an RPM device capable of operating in randomized, unidirectional, and single-axis clinostat modes. Angular velocity profiles were experimentally recorded using magneto-inertial measurement units mounted on the RPM frames. These data informed a computational model that simulated gravity vector dispersion and centrifugal acceleration across different operational configurations. Additionally, SH-SY5Y neuronal cells were exposed to simulated microgravity under each mode to evaluate cellular responses. The computational analysis demonstrated that all RPM modes effectively achieved simulated microgravity conditions, with time-averaged gravity values ranging from 10−2 to 10−3 g. Centrifugal accelerations remained below 0.08 g across all conditions. Biologically, SH-SY5Y cells exposed to simulated microgravity exhibited reduced confluency and increased α-synuclein inclusions in all RPM configurations, with milder effects observed in clinostat mode. The integration of experimental, computational, and biological analyses establishes a quantitative framework for assessing and optimizing RPM-based microgravity simulations. The findings confirm that both RPM and clinostat modes can reproduce key features of microgravity, while highlighting the role of motion characteristics in shaping biological responses. The proposed computational model represents a predictive tool to support the design and reproducibility of future ground-based microgravity studies.

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0351320

DOI: 10.1371/journal.pone.0351320

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