Abstract. The maintenance of a differentiated cell type (i.e., cell identity), a feature established by cell type- specific transcriptional programs, is critical for cell function and tissue homeostasis. Yet, despite acquiring a specific function, the differentiated cell remains a plastic entity which retains the ability to functionally reprogram itself in response to incoming signals while simultaneously maintaining its identity. To maintain this plasticity, the cell relies on transcription factor (TF) networks that access, interpret and implement genomic information. Upon receiving and processing an incoming signal (whether developmental, environment or damage), the cell responds by rewiring its TF network. In some instances, the processing of the incoming signal rewires the TF network and destabilizes cell identity leading to a transitional cell state with reduced function known as senescence. Originally described as a stable proliferative arrest, senescence has recently emerged as a transitional cell state heavily linked to the aging process and development of diseases such as osteoarthritis (OA), cancer and fibrosis. The resolution of this transitional state can lead to outcomes linked with disease development, including cell death, stabilization of senescence or disease states (Graphical Abstract). With this understanding, we hypothesize that transitional senescence states represent critical intermediates that could be manipulated for therapeutic applications. Given the overarching contribution of senescent cells to pathological processes such as aging, cancer and fibrosis, manipulating senescence states has tremendous potential for restoring cellular and tissue function across many diseases. The chief focus of our research program is to define and manipulate the gene regulatory networks that dictate the transition through senescence states. To achieve this goal, we employ an unbiased approach that involves the generation of TF network models from bulk and single-cell time-series high-throughput sequencing epigenomic data from primary human cells representing various tissues undergoing senescence-associated transitions linked to disease development. Using the TF network model as a logical structure to validate and guide the manipulation of senescence states, we target critical nodes using reverse genetics, genome editing and pharmacological approaches, and confirm key findings in human samples using senescent cell isolation methods. Over the next 5 years, the main goals of our research program are: 1) To generate TF network models of primary human chondrocytes (to model OA) and hepatic stellate cells (HSCs, to model fibrosis) undergoing replicative and cytokine-induced senescence (RS and CIS), 2) To test the prognostic potential of senescence-linked epigenomic signatures using publicly available human datasets, and 3) To develop strategies to restore the function of senescent cells by modulation of their TF network. The overall vision of th...