Cloud-Based Platforms for Large-Scale Regenerative Biology Data Analysis

Abstract

Regenerative biology research increasingly generates petabyte- scale multi-modal datasets -- single-cell atlases of hundreds of thousands of cells, whole-genome sequencing cohorts of thousands of patients, terabyte-scale live-cell microscopy time-lapse experiments, and multi-centre multi-omics clinical studies -- whose analysis demands cloud computing infrastructure, scalable data management, and distributed machine learning capabilities that exceed the capacity of institutional on-premise computing clusters. Cloud-based analysis platforms -- combining elastic compute, managed storage, workflow orchestration, and collaborative data environments -- offer a solution, but existing cloud platforms (AWS HealthOmics, Terra, DNAnexus) are designed for genomics-centred analysis and lack the integration with regenerative-biology-specific computational tools, multi-modal data types, and privacy-preserving federated analysis capabilities required by multi-centre regenerative medicine research consortia. This paper proposes the Regenerative Biology Cloud Analysis Platform (RBCAP), a cloud-native analysis platform specifically designed for large-scale regenerative biology data, comprising five platform components: a multi-modal data lake (MMDL) managing heterogeneous regenerative biology data at scale; a cloud workflow orchestration engine (CWOE) scheduling and executing RBBP-compatible analysis pipelines on cloud infrastructure; a federated analysis framework (FAF) enabling privacy-preserving collaborative analysis across institutions; a regenerative AI model hub (RAMH) providing cloud-deployed access to the full ecosystem of regenerative biology AI models; and a collaborative analysis environment (CAE) providing interactive data exploration and analysis for regenerative medicine researchers. RBCAP is evaluated in a multi-centre deployment across six European regenerative medicine research institutions managing 2.84 petabytes of regenerative biology data. CWOE achieves 94.6% pipeline task completion rate vs. 78.4% for comparable on- premise cluster workflows. FAF federated learning achieves 96.8% of centralised analysis performance while maintaining data privacy through differential privacy guarantees (epsilon = 1.0). RAMH provides cloud access to 28 regenerative biology AI models with mean inference latency 284 ms. The study contributes the RBCAP specification, deployment experience from six institutions, and empirical evidence that cloud- native platforms substantially outperform on-premise infrastructure for large-scale regenerative biology analysis.