STAGED: A Multi-Agent Neural Network for Learning Cellular Interaction Dynamics
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915396107370496 |
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| author | Rocha, Joao F. Xu, Ke Sun, Xingzhi Krishna, Ananya Bhaskar, Dhananjay Mongeon, Blanche Craig, Morgan Gerstein, Mark Krishnaswamy, Smita |
| author_facet | Rocha, Joao F. Xu, Ke Sun, Xingzhi Krishna, Ananya Bhaskar, Dhananjay Mongeon, Blanche Craig, Morgan Gerstein, Mark Krishnaswamy, Smita |
| contents | The advent of single-cell technology has significantly improved our understanding of cellular states and subpopulations in various tissues under normal and diseased conditions by employing data-driven approaches such as clustering and trajectory inference. However, these methods consider cells as independent data points of population distributions. With spatial transcriptomics, we can represent cellular organization, along with dynamic cell-cell interactions that lead to changes in cell state. Still, key computational advances are necessary to enable the data-driven learning of such complex interactive cellular dynamics. While agent-based modeling (ABM) provides a powerful framework, traditional approaches rely on handcrafted rules derived from domain knowledge rather than data-driven approaches. To address this, we introduce Spatio Temporal Agent-Based Graph Evolution Dynamics(STAGED) integrating ABM with deep learning to model intercellular communication, and its effect on the intracellular gene regulatory network. Using graph ODE networks (GDEs) with shared weights per cell type, our approach represents genes as vertices and interactions as directed edges, dynamically learning their strengths through a designed attention mechanism. Trained to match continuous trajectories of simulated as well as inferred trajectories from spatial transcriptomics data, the model captures both intercellular and intracellular interactions, enabling a more adaptive and accurate representation of cellular dynamics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_11660 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | STAGED: A Multi-Agent Neural Network for Learning Cellular Interaction Dynamics Rocha, Joao F. Xu, Ke Sun, Xingzhi Krishna, Ananya Bhaskar, Dhananjay Mongeon, Blanche Craig, Morgan Gerstein, Mark Krishnaswamy, Smita Machine Learning Multiagent Systems Quantitative Methods The advent of single-cell technology has significantly improved our understanding of cellular states and subpopulations in various tissues under normal and diseased conditions by employing data-driven approaches such as clustering and trajectory inference. However, these methods consider cells as independent data points of population distributions. With spatial transcriptomics, we can represent cellular organization, along with dynamic cell-cell interactions that lead to changes in cell state. Still, key computational advances are necessary to enable the data-driven learning of such complex interactive cellular dynamics. While agent-based modeling (ABM) provides a powerful framework, traditional approaches rely on handcrafted rules derived from domain knowledge rather than data-driven approaches. To address this, we introduce Spatio Temporal Agent-Based Graph Evolution Dynamics(STAGED) integrating ABM with deep learning to model intercellular communication, and its effect on the intracellular gene regulatory network. Using graph ODE networks (GDEs) with shared weights per cell type, our approach represents genes as vertices and interactions as directed edges, dynamically learning their strengths through a designed attention mechanism. Trained to match continuous trajectories of simulated as well as inferred trajectories from spatial transcriptomics data, the model captures both intercellular and intracellular interactions, enabling a more adaptive and accurate representation of cellular dynamics. |
| title | STAGED: A Multi-Agent Neural Network for Learning Cellular Interaction Dynamics |
| topic | Machine Learning Multiagent Systems Quantitative Methods |
| url | https://arxiv.org/abs/2507.11660 |