The Science

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Multimodal Imaging - Kidney Tissue

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Cellular Projections

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Human Retina 3D

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Tissue Physiology, Damage, and Cancer

At the Center for Computational Systems Biology, our investigators study integrative mechanisms that drive tissue behavior and dysfunction in conditions such as pancreatitis, chronic lung diseases, inflammatory bowel diseases, and cancer. Leveraging advanced spatial technologies like imaging mass spectrometry, we analyze tissue structure and disease progression in cancer, diabetes, and infections. We use multimodal experiments, computational tools, and patient data to advance diagnostics and treatments. Our work also examines epithelial responses to wounds, integrating imaging and modeling to uncover healing mechanisms. High-throughput single-cell experiments combined with machine learning allow us to link environmental factors to tissue structure, inflammation, and cancer progression.

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Neuroscience as a Network Science

We focus on designing therapeutic strategies for brain disorders, including epilepsy and Alzheimer’s, by mapping hyper-excitable circuits and understanding cellular responses in pathological environments. By combining molecular profiling, imaging, and computational models, we aim to reveal mechanisms underlying seizure disorders, Alzheimer’s, and psychiatric conditions. Additionally, we study somatosensory circuits using transcriptomics and behavior analysis to better understand nerve injury and sensory dysfunction, guiding the development of targeted therapies.

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Algorithms, AI, and Machine Learning

We develop bioinformatics, statistical, and machine learning approaches to integrate multi-omics and phenotypic data, advancing gene regulatory network reconstruction, drug repurposing, and precision medicine on patient cohorts. Our efforts include innovative methods for single-cell and spatial transcriptomics, enabling new insights into complex biological systems. Our investigators also make advances in the field of computer vision, applying AI to ultra-high-resolution imaging and multimodal data to analyze diseases and their progression. Integrating imaging mass spectrometry with machine learning, we explore spatial biology to uncover patterns in aging, cancer, and kidney disease.

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Synthetic Biology

We harness synthetic biology and organoid technology to model and manipulate complex biological systems for therapeutic applications. By engineering co-culture systems and constructing synthetic signaling modules, we explore complex behaviors such as how gut microbes and immune cells interact to activate stem cells and promote epithelial repair. Using genome and epigenome editing, we dissect signaling networks involved in chronic disease conditions. Additionally, we utilize organoid systems derived from induced pluripotent stem cells or adult stem cells, enabling precise experimental validation of computational models and advancing our understanding of disease mechanisms at cellular and tissue scales.