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An AI Agentic Framework for Understanding Low-Dose Radiation Effects on Human Lung Epithelial Cells
DescriptionWhile data modalities like scRNASeq, histology, and DNA methylation offer valuable insights into cellular responses to external perturbations, learning from such datasets is often limited by the user’s ability to analyze large data, and familiarity with the existing knowledge base and tools. Moreover, there is a tendency to favor well established mechanisms even for understanding new biology, which can limit the exploration of novel or unexpected biological pathways. Manual curation of literature, pathway databases, and public datasets is time consuming, and traditional analysis pipelines are typically static, tool specific, and lack self correcting capabilities, and are thus difficult to scale.
To overcome these challenges, we present Agentic Lab, an AI agentic framework for accelerating biomedical discovery through automated, collaborative scientific inquiry via a set of specialized agents. Agents are entities that use prompts for understanding their tasks, LLMs for reasoning over those, and tools for interaction with the outside environment. Unlike conventional linear workflows, these agents continuously reason, search, reflect, and adapt. For example, an unexpected gene expression pattern can automatically trigger new literature searches, hypothesis refinement, or reanalysis of data, and coding errors and missing packages can be automatically detected and fixed. Agentic Lab formulates a research workflow by first using a Principal Investigator (PI) Agent as the entry point, which interprets the user defined task and, with the assistance of a Browsing Agent that retrieves knowledge from scientific repositories, user provided files, and web links, formulates a research workflow. The PI Agent then assigns specific tasks to specialized agents. Code Writer and Executor Agents generate, run, and debug codes, and a Critic Agent ensures robustness through continuous evaluation of results and processes. This framework integrates literature curation, hypothesis generation, code development, and data analysis in iterative cycles, with the option for the user to intervene at any point. The framework is driven entirely by open-weight LLMs that can be hosted locally using limited resources, enabling local, privacy-preserving execution without reliance on costly APIs. Our approach combines smart prompting, tool augmentation, and human-in-the-loop validation to maximize the performance of smaller models in complex biomedical discovery
We apply this framework to study low-dose (LD) radiation effects, where the carcinogenic risks below 10 mGy remain poorly understood despite widespread exposure from natural background (e.g., radon, cosmic rays), medical imaging, and nuclear industries. Using scRNA-seq data from the human lung epithelial BEAS-2B cell line exposed to Cs-137 gamma radiation at low (10 mGy), medium (100 mGy), and high (1 Gy) doses, we investigate transcriptional changes across dose levels to identify differences in underlying biological mechanisms. We use Geneformer, a pre-trained transformer-based single cell foundation model, to generate contextual gene and cell embeddings for in-silico perturbation (ISP) studies and identify key drivers of cell state transitions associated with LD exposure. By analyzing shifts in the latent embedding space, we map dysregulated genes and pathways implicated in stress response, and early malignant transformation. Agentic Lab interacts with HPC environments to submit jobs to carry out fine tuning of pretrained Genefomer models and ISP.
To overcome these challenges, we present Agentic Lab, an AI agentic framework for accelerating biomedical discovery through automated, collaborative scientific inquiry via a set of specialized agents. Agents are entities that use prompts for understanding their tasks, LLMs for reasoning over those, and tools for interaction with the outside environment. Unlike conventional linear workflows, these agents continuously reason, search, reflect, and adapt. For example, an unexpected gene expression pattern can automatically trigger new literature searches, hypothesis refinement, or reanalysis of data, and coding errors and missing packages can be automatically detected and fixed. Agentic Lab formulates a research workflow by first using a Principal Investigator (PI) Agent as the entry point, which interprets the user defined task and, with the assistance of a Browsing Agent that retrieves knowledge from scientific repositories, user provided files, and web links, formulates a research workflow. The PI Agent then assigns specific tasks to specialized agents. Code Writer and Executor Agents generate, run, and debug codes, and a Critic Agent ensures robustness through continuous evaluation of results and processes. This framework integrates literature curation, hypothesis generation, code development, and data analysis in iterative cycles, with the option for the user to intervene at any point. The framework is driven entirely by open-weight LLMs that can be hosted locally using limited resources, enabling local, privacy-preserving execution without reliance on costly APIs. Our approach combines smart prompting, tool augmentation, and human-in-the-loop validation to maximize the performance of smaller models in complex biomedical discovery
We apply this framework to study low-dose (LD) radiation effects, where the carcinogenic risks below 10 mGy remain poorly understood despite widespread exposure from natural background (e.g., radon, cosmic rays), medical imaging, and nuclear industries. Using scRNA-seq data from the human lung epithelial BEAS-2B cell line exposed to Cs-137 gamma radiation at low (10 mGy), medium (100 mGy), and high (1 Gy) doses, we investigate transcriptional changes across dose levels to identify differences in underlying biological mechanisms. We use Geneformer, a pre-trained transformer-based single cell foundation model, to generate contextual gene and cell embeddings for in-silico perturbation (ISP) studies and identify key drivers of cell state transitions associated with LD exposure. By analyzing shifts in the latent embedding space, we map dysregulated genes and pathways implicated in stress response, and early malignant transformation. Agentic Lab interacts with HPC environments to submit jobs to carry out fine tuning of pretrained Genefomer models and ISP.
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