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Field
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-BiFC knockin mouse models to 1) determine how PDGF ligand identity and concentration affect receptor dimerization and internalization using flow cytometry and CITE-seq in combination with spatial
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program seeks to increase our understanding of the immunological and molecular mechanisms involved in autoimmune dysregulation in Type 1 diabetes (T1D). Through the use of novel murine models of T1D, human
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to answer: How temporal and structural sparsity can be incorporated into the 3D dynamics reconstruction model? How sparse can a measurement be and still capture a high-resolution dynamic event? How do
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the development of bespoke solutions for cancer imaging based on spatial analysis and kinetic modelling techniques. This strand would involve adapting in-house models for Positron Emission Tomography data to other
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-level computational model of the human cell. The program is led by Jan Ellenberg, Director of SciLifeLab and Mathias Ulhen, Director of the Human Protein Atlas, together with a collaborative, multi
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The International Institute of Molecular Mechanisms and Machines Polish Academy of Sciences | Poland | about 2 months ago
mechanisms governing transcriptional regulation and their dysregulation in cancer. Our long-term goal is to understand how the spatial organization of transcriptional regulators influences gene expression
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, or interoception. Adipose tissue biology, thermogenesis, metabolic physiology, glucose and lipid metabolism, obesity and diabetes models, or mouse metabolic phenotyping. Single-cell genomics, spatial transcriptomics
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team. Perform spatial integration (alignment, upscaling/downscaling) for comparative analyses. Assess the distribution of modeled exposures and health impacts across population subgroups. Contribute
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to have a background and a strong interest in one or several of the following areas: glial biology, molecular neuroscience, single cell and spatial genomics, gene delivery, animal models of CNS injury
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, pharmacologic, genomics profiling (e.g. single-cell, spatial gene expression), datasets into rigorous mechanistic and preclinical studies, ultimately aimed at improving outcomes for children with cancer