Computational Biology
The Koplev lab studies molecular mechanisms of tissue architecture in human gut, heart, and immune systems.
Research
Our interest includes healthy steady-states of fibroblasts, stem cells, and adaptive immune cells along with disease perturbation following cancer and cardiovascular diseases. We work on establishing links between genetics and gene regulation through transcription factors and endocrine signaling by developing computational methods analyzing live cell, single-cell, and spatial omics data.
Cells form the basic units of life but through coordinated interactions into higher-level tissue architectures. Studying this tissue structure of human and model organisms enables fundamental biological insight into how cells carry out emergent property functions during healthy and diseased conditions. Notable examples include tissue organization through mesenchymal fibroblasts, epithelial stem cells, lymphoid aggregate structures, and vascular networks. A key research interest is understanding how regulation by transcription factors and alternative splicing shape tissue architecture in steady-state conditions across organs and following disease perturbations such as cancer, inflammatory bowel disease, and cardiovascular diseases. Improved conceptual and data-driven models of how the genome is regulated in different cellular and microenvironmental contexts will enable a better understanding of the heritability of complex disease risks and phenotypic traits. Here, comparing cells and their tissue niches across human organs and diseases focussing on immune-related structures provides a unique view for investigating mammalian cellular biology. We aim to investigate:
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Whether shared risk of inflammatory diseases and cancer is determined by spatiotemporal tissue architecture.
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The roles of disease-associated cell types such as fibroblasts, stem cells, and immune cells.
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If alternative splicing of transcription factors rewires gene regulatory networks and cellular identity.
Advances in single-cell and spatial omics techniques have enabled unprecedented insights into the molecular biology of cellular and molecular processes. Computational biology plays an increasingly important role in interpreting such data, placing the application of novel machine learning and AI techniques at the forefront of modern biology research. Effective and creative application requires an interdisciplinary approach to science with diverse skillsets working together to achieve substantial discoveries and technical breakthroughs.
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