Phenotypic Screen Data Integration Infers Regulators of Fibroblasts

Fibrosis is dysregulation of the normal physiological wound healing response. Fibroblasts are the cell type that primarily governs how tissues heal and form scars in response to injury signals and other biochemical and biomechanical cues. Fibrosis causes pathological complications across many disease areas, including cardiac, kidney, liver, and lung diseases. Exploring and mapping fibroblast signaling is imperative to understanding and developing therapeutics for fibrotic diseases. This thesis focuses on exploring large data sets for fibroblast phenotype, cell signaling, and fibrosis, and using these data sets to expand a computational intracellular fibroblast cell signaling model. In this dissertation work, I applied expanded versions of this model to make novel predictions about fibroblast signaling during fibrosis, identify new regulatory mechanisms of the fibroblast phenotype, and explore new phenotypic markers for fibroblasts.