Agentic and Generative AI for In Silico Modeling of Human Pancreatic Tissue in T1D
Contact PI: Jia Liu, PhD, Harvard University (DP1 D147907)
Juan Alvarez, PhD, Co-Investigator, University of Pennsylvania
Xiao Wang, PhD, Co-Investigator, Massachusetts Institute of Technology
Start Date: August 15, 2026
Abstract
Type 1 diabetes (T1D) is characterized by the progressive autoimmune destruction of pancreatic β cells, accompanied by substantial spatial and temporal heterogeneity in tissue remodeling. Existing models fail to capture the multicellular complexity and spatial organization of disease progression, limiting our ability to understand pathogenesis or predict therapeutic responses. To address this critical gap, we propose to develop the first unified, agentic, and generative virtual tissue platform for T1D, integrating recent advances in spatial transcriptomics, deep learning-based multimodal data harmonization, and large language model (LLM)-based AI agent for flexible reasoning, contextual learning, and zero- and few-shot generalization.
The proposed research will extend our previously developed deep learning framework, FUSEMAP, to integrate single-cell genomics, spatial transcriptomics, protein profiling, and functional imaging data from both healthy and diseased pancreatic tissue. This integration will enable transcriptome-scale gene imputation, robust cross-sample and multimodal alignment, and segmentation of anatomically and pathologically relevant domains.
We will also advance our spatial transcriptomics AI agent into a Tissue Spatial AI Agent (TSAGENT)—a multimodal, generative AI system capable of autonomous data analysis, simulation of disease-associated perturbations, and mechanistic hypothesis generation. TSAGENT will incorporate patient metadata (e.g., age, sex, BMI, HbA1c) to model inter-individual variability and iteratively refine its inferences based on new data.
Together, FUSEMAP and TSAGENT will form the computational foundation of a dynamic virtual pancreas tissue model. This platform will be validated for mechanistic dissection of spatial heterogeneity in β-cell loss, immune-islet interactions, and tissue responses to therapeutic interventions.
Importantly, all models, software tools, and datasets will be disseminated through an open-access web portal, with extensive community outreach to promote adoption. By enabling continuous refinement through user contributions and supporting rigorous, interpretable in silico experiments, this work will establish a novel scientific infrastructure for understanding and treating T1D at the tissue level.
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