A Quantitative Framework for Quality Assessment of Simulated Microgravity for Biological Systems: Biological Microgravity Evaluation Framework (BMEF)

Currently, 2 dimensional and 3-dimensional clinostats (2D and 3D Clinostats), random positioning machines (RPMs) and other ground-based simulation techniques are very important tools for space biology research. However, their performance and validity are almost always assessed by observations or isolated parameters that often inadequately explain the mechanical microenvironment experienced by biological samples like cells and tissues. This leads to lack of uniformity and reproducibility specially in the case of biological experiments. The cells might act differently within same conditions of simulated microgravity in different experiments due to lack of a standardised quantitative framework to asses every aspect of simulated microgravity beyond vector averaging. In this study we compute Biological Microgravity Evaluation Framework (BMEF), which is a comprehensive computational step by step method to evaluate mechanical stability, directional isotropy, vector orientation coverage and biologically relevant exposure generated during a simulated microgravity study. In this current study we have used a 3D clinostat with partial RPM features, where the inner frame changed its orientation randomly within a 10 s timeframe. Acceleration data through a 3-axis accelerometer, at 24 min, 48 min, 96 min and 120 min was obtained and gravity vector dynamics were characterised. This quantitative framework combines available physical parameters, resultant mechanical loading, Directional Bias Vector (DBV), Orientation Coverage Index (OCI), and Orientation Uniformity Index (OUI) with novel biologically relevant parameters, Mechanical Quality Score (MQS), Effective Isotropic Exposure Time (EIET) to generate a Biological Microgravity Quality Index (BMQI). We observed that orientation of sampling became denser with duration of experiments and EIET increased proportionally with exposure duration while BMQI converged rapidly in the initial phase of the study then remained stable thereafter. This framework is intended to provide a quantitative descriptor of mechanical exposure conditions for future biological experiments.