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Cells interact as dynamically evolving ecosystems. While recent single-cell and spatial multi-omics technologies quantify individual cell characteristics, predicting their evolution requires mathematical modeling. We propose a conceptual framework—a cell behavior hypothesis grammar—that uses natural language statements (cell rules) to create mathematical models. This enables systematic integration of biological knowledge and multi-omics data to generate in silico models, enabling virtual “thought experiments” that test and expand our understanding of multicellular systems and generate new testable hypotheses. This paper motivates and describes the grammar, offers a reference implementation, and demonstrates its use in developing both de novo mechanistic models and those informed by multi-omics data. We show its potential through examples in cancer and its broader applicability in simulating brain development. This approach bridges biological, clinical, and systems biology research for mathematical modeling at scale, allowing the community to predict emergent multicellular behavior. 

ABSTRACT Branched epithelial networks are generated through an iterative process of elongation and bifurcation. We sought to understand bifurcation of the mammary epithelium. To visualize this process, we utilized three-dimensional (3D) organotypic culture and time-lapse confocal microscopy. We tracked cell migration during bifurcation and observed local reductions in cell speed at the nascent bifurcation cleft. This effect was proximity dependent, as individual cells approaching the cleft reduced speed, whereas cells exiting the cleft increased speed. As the cells slow down, they orient both migration and protrusions towards the nascent cleft, while cells in the adjacent branches orient towards the elongating tips. We next tested the hypothesis that TGF-β signaling controls mammary branching by regulating cell migration. We first validated that addition of TGF-β1 (TGFB1) protein increased cleft number, whereas inhibition of TGF-β signaling reduced cleft number. Then, consistent with our hypothesis, we observed that pharmacological inhibition of TGF-β1 signaling acutely decreased epithelial migration speed. Our data suggest a model for mammary epithelial bifurcation in which TGF-β signaling regulates cell migration to determine the local sites of bifurcation and the global pattern of the tubular network. 

Abstract To prevent damage to the host or its commensal microbiota, epithelial tissues must match the intensity of the immune response to the severity of a biological threat. Toll-like receptors allow epithelial cells to identify microbe associated molecular patterns. However, the mechanisms that mitigate biological noise in single cells to ensure quantitatively appropriate responses remain unclear. Here we address this question using single cell and single molecule approaches in mammary epithelial cells and primary organoids. We find that epithelial tissues respond to bacterial microbe associated molecular patterns by activating a subset of cells in an all-or-nothing (i.e. digital) manner. The maximum fraction of responsive cells is regulated by a bimodal epigenetic switch that licenses the TLR2 promoter for transcription across multiple generations. This mechanism confers a flexible memory of inflammatory events as well as unique spatio-temporal control of epithelial tissue-level immune responses. We propose that epigenetic licensing in individual cells allows for long-term, quantitative fine-tuning of population-level responses.