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Abstract Bacteria utilize cell-cell signaling to coordinate gene expression in populations of cells. Bacterial signal exchange was originally interpreted as a mechanism bacteria use to regulate gene expression in response to changes in cell density, denoted as quorum sensing. Bacterial communication is now known to encompass the exchange of multiple chemical signals between different species of bacteria. Such signal crosstalk within communities of bacteria can have unexpected consequences. Some bacterial species even utilize more than one orthogonal signaling molecule, enabling such species to simultaneously communicate within distinct subsets of species. Such cells utilizing two sets of signals act as a bridge to link gene expression states within the community. Here, a mathematical model was implemented to investigate the consequences of multi-signal communication within heterogeneous bacterial communities. The model was inspired by simple neural networks, with nodes representing bacterial species and directed weights between nodes accounting for the impacts of inter-species signal exchange on gene expression. The activity state of such a network is defined as the gene expression state of each species within the community. Using the model, the stability of the activity states of such networks to changes in signal concentration and population size were quantified. Networks exchanging one set of signals were compared to network exchanging two orthogonal sets of signals. A multilayer neural network model was developed to analyze such networks exchanging orthogonal sets of signals. The model reveals that signal crosstalk increased the activity of the network. These networks were largely resilient to perturbation, however networks were more sensitive to perturbations of the largest population size. Bacterial species utilizing two orthogonal signals, within multilayer networks, had the potential to couple activity states of species that cannot directly communicate. These results give insight into strategies for manipulating signal exchange to predict and control gene expression within bacterial communities. 

Ascertaining the collective viability of cells in different cell culture conditions has typically relied on averaging colorimetric indicators and is often reported out in simple binary readouts. Recent research has combined viability assessment techniques with image-based deep-learning models to automate the characterization of cellular properties. However, further development of viability measurements to assess the continuity of possible cellular states and responses to perturbation across cell culture conditions is needed. In this work, we demonstrate an image processing algorithm for quantifying features associated with cellular viability in 3D cultures without the need for assay-based indicators. We show that our algorithm performs similarly to a pair of human experts in whole-well images over a range of days and culture matrix compositions. To demonstrate potential utility, we perform a longitudinal study investigating the impact of a known therapeutic on pancreatic cancer spheroids. Using images taken with a high content imaging system, the algorithm successfully tracks viability at the individual spheroid and whole-well level. The method we propose reduces analysis time by 97% in comparison with the experts. Because the method is independent of the microscope or imaging system used, this approach lays the foundation for accelerating progress in and for improving the robustness and reproducibility of 3D culture analysis across biological and clinical research.