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INTERNATIONAL JOURNAL OF RECENT TRENDS IN TECHNOLOGY AND ENGINEERING (IJRTTE)

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AI-Enabled Cloud Bio-Simulation Environments

R Gayathiri
Assistant Professor, Department of Electrical and Electronics Engineering, J.J. College of Engineering , India.
Gajalakshmi S
Department of EEE, New Prince Shri Bhavani College of Engineering and Technology , India.
N Manonmani
Professor, Department of EEE, Sri Krishna College of Engineering and Technology, India.

Keywords: Bio-Simulation, Cloud Computing, Artificial Intelligence, Reinforcement Learning, Multi-Omics Data Processing.

Abstract

It is becoming common that biological research is based on large-scale computational modeling, multi-omics integration, and high-fidelity simulation to comprehend complex molecular and cellular behaviors. Nonetheless, the conventional cloud systems are not configured to support the dynamism and data-intensive nature of the contemporary bio-simulation workloads. In this paper, we are going to introduce an AI-powered Cloud Bio-Simulation Environment (AICBSE), which combines deep learning surrogate models, reinforcement learning-based resource optimization, containerized microservices, and real-time feedback systems into one cloud-native system. The system facilitates the sustained ingestion and processing of genomic, proteomic, metabolomic, and imaging data, which can support adaptive simulation infective programs that reduces parameters in response to the biological responses. The experimental findings show that there is a great advancement in the speed of simulation, biological fidelity, and cloud resource efficiency with the architecture exhibiting quick proliferation among dispersed nodes with low latency and high throughput. Digital twins can be created in real-time, interactive models can be created and experiments conducted with a high throughput due to the integration of AI prediction models and dynamic cloud orchestration. On the whole, the suggested environment offers a paradigm shift with respect to speeding up the process of biological discovery, improving predictive modelling, and assisting next-generation applications in the drug development, personalized medicine and in silico research.
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Published
2025-12-27
Pages
1-11
Issue
Vol. 4 No. 4 (2025): IJRTTE - 04 - 04
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Articles

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