Authors
Chen, J., Volk, M., Shafaei, S., Tan, S.-I., Upadhyay, V., Singh, N., Zhong, X., Arneson, K., He, W., Wang, C., Lu, J., Sharma, A., McLaughlin, J., Berry, M., Harmon, S., Reyes, S., Heintz, G., Zhao, H.
Abstract
Autonomous experimentation, where an automated system designs, performs, analyzes, and iteratively improves experiments with minimal human intervention represents a transformative scientific research paradigm. However, it remains hampered by the disconnect between computational design and experimental execution. Here we present iCloudBiofoundry, a cloud-accessible, scalable, and self-evolving platform that bridges this divide. A scientific multi-agent system, iBioGenie, plans research workflows by converting natural language research objectives into testable hypotheses and experimentally grounded protocols using literature, databases, and computational tools. A physical AI layer, BiofoundryAI, translates them into executable workflows that run autonomously on distributed robotic biofoundries, and collects the data for iterative design. We demonstrate the end-to-end capabilities of iCloudBiofoundry across three representative case studies spanning enzyme retrieval, protein engineering, and metabolic engineering. By integrating agentic AI with physical AI, iCloudBiofoundry provides a cloud accessible platform that unifies computational design and robotic execution, enabling autonomous biological research.
Preprint server:
bioRxiv
The authors list and abstract were imported from bioRxiv on 03 Oct 2026.
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