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Abstract

Collective cell movement is critical to the emergent properties of many multicellular systems, including microbial self-organization in biofilms, wound healing, and cancer metastasis. However, even the best-studied systems lack a complete picture of how diverse physical and chemical cues act upon individual cells to ensure coordinated multicellular behavior. Myxococcus xanthus is a model bacteria famous for its coordinated multicellular behavior resulting in dynamic pattern formation. For example, when starving, millions of cells coordinate their movement to organize into fruiting bodies – aggregates containing tens of thousands of bacteria. Relating these complex self-organization patterns to the behavior of individual cells is a complex, reverse-engineering problem that cannot be solved solely by experimental research. In collaboration with experimental colleagues, we use a combination of image processing, agent-based modeling, soft-matter physics, and kinetic theory to uncover the mechanisms of emergent collective behaviors.

In more recent developments, to quantify and compare self-organization dynamics systematically, we also employ the power of AI. We trained a deep-learning model to find a low-dimensional representation of brightfield microscopic images. Using a ResNet as an encoder, we compressed each image crop into a 13-dimensional feature vector. To validate this compression, we employed a StyleGAN2 as a generator to reconstruct high-resolution images from the feature vectors. During training, a Siamese network assessed the phenotypic similarity between input and reconstructed images. As a result, we obtained a model capable of reconstructing images that appear indistinguishable from input images in terms of phenotype. With the 13-dimensional features extracted, we developed a distance metric and 2-D graphical representation to compare 24-hour movies based on their self-organization dynamics. Our findings demonstrate the potential of deep learning methods in extracting emergent patterns in cellular systems, paving the way for future research in bacterial self-organization and system biology.