PROJECT SUMMARY – Responses of cells to signaling perturbations exhibit complex dynamics that vary across diverse cell types or even genetically identical cells exposed to uniform conditions. Heterogeneity arises partly because of the cells’ ability to process the signaling input in the context of their states controlled by their epigenome, a regulatory network involving transcription factors and chromatin architecture which, although contains heritable information about gene expression programs, remains plastic. Such plasticity enables cells to transition from one phenotype to another and exhibit fractional responses to perturbations. Despite this knowledge, what remains largely unknown is: (i) the nature of these dynamically fluctuating phenotypically consequential cell states, and how they determine the context for signal transduction, (ii) mechanisms that control the diversity of these states, their multi-stability or plasticity, and variation across cell types, and (iii) mechanisms by which information embedded in these states is integrated with that from signaling input to elicit state-specific decisions. Our proposed research is significant because it addresses these key gaps in our understanding through the study of AP-1 transcription factor network, which we recently showed to serve as a key node in linking MAPK signal transduction to diverse patterns of cell state plasticity. Individual cells can adopt a variety of AP-1 states regulated by a combination of competitive homo- and heterodimeric interactions between individual AP-1 proteins, whose levels and stability are also subject to transcriptional (auto)regulation and posttranslational modification by MAP kinases. We have shown that the diversity of recurrent AP-1 states and their single-cell frequencies vary across differentiation states and predict their phenotypic plasticity prior to and following MAPK perturbations. We propose to determine how combinatorial patterns of AP-1 network shape cell state heterogeneity and plasticity via analyzing a panel of model-guided, AP-1 perturbed, isogenic cells to decompose the impact of diverse AP-1 dimers on chromatin features, networks of transcriptional coregulators, the resulting gene expression programs, and single-cell responses to MAPK perturbations. We will build the first generation of experimentally validated models of AP-1 regulated expression system based on their dimerization-controlled, co-regulated, competitive interactions, and use them to reveal network features underlying the diversity of multi-stable AP-1 states in different cell populations. Side-by-side virtual and wet-lab single-cell experiments will uncover pharmacologically tractable targets to modulate population heterogeneity via stabilizing or destabilizing certain AP-1 states. We will use multiplexed single-cell imaging in combination with multivariate modeling to determine how the trajectory of adaptive responses to MAPK perturbations and their phenotypic outcomes ar...