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Plaisier Lab


Maximizing the integration of large-scale data to inform models of biological systems and developing experimental approaches that use these models to answer open questions for human diseases.

The Plaisier lab uses omics-level snapshots of complex biological systems to inform computational models that:  explore the state space of biological systems; discover mechanistic interactions driving the changes in biological states (e.g. health and disease); and predict interventions that push the system from disease states towards healthy states. We focus on developing and applying synergistic experimental and computational approaches that build predictive models derived from single cells, whole heterogeneous tissues, and patient clinical data. These predictive models are then used to gain insights into the underlying biology and identify the best possible interventions to achieve the desired biological state.

The Plaisier Lab has applied this strategy to the study of:

  • Tumor biology
    • Deadly brain cancer glioblastoma multiforme
    • Pan-Cancer studies of the immune landscape of cancer
    • Developing novel treatment approaches for mesothelioma
  • Immune response to Mycobacterium tuberculosis infection
    • Innate immune response to infection
    • Adaptive immune response to infection
  • Stem cell differentiation
    • Regulatory networks governing neuronal cell type differentiation