Regulators of the actin cytoskeleton such Rho GTPases can modulate forces developed in cells by promoting actomyosin contraction. At the same time, through mechanosensing, tension is known to affect the activity of Rho GTPases. What happens when these effects act in concert? Using a minimal model (1 GTPase coupled to a Kelvin-Voigt element), we show that two-way feedback between signaling ('RhoA') and mechanical tension (stretching) leads to a spectrum of cell behaviors, including contracted or relaxed cells, and cells that oscillate between these extremes. When such 'model cells' are connected to one another in a row or in a 2D sheet ('epithelium'), we observe waves of contraction/relaxation and GTPase activity sweeping through the tissue. The minimal model lends itself to full bifurcation analysis, and suggests a mechanism that explains behavior observed in the context of development and collective cell behavior.

The development of powerful natural language models has improved the ability to learn meaningful representations of protein sequences. In addition, advances in high-throughput mutagenesis, directed evolution and next-generation sequencing have allowed for the accumulation of large amounts of labelled fitness data. Leveraging these two trends, we introduce Regularized Latent Space Optimization (ReLSO), a deep transformer-based autoencoder, which features a highly structured latent space that is trained to jointly generate sequences as well as predict fitness. Through regularized prediction heads, ReLSO introduces a powerful protein sequence encoder and a novel approach for efficient fitness landscape traversal. Using ReLSO, we explicitly model the sequence-function landscape of large labelled datasets and generate new molecules by optimizing within the latent space using gradient-based methods. We evaluate this approach on several publicly available protein datasets, including variant sets of anti-ranibizumab and green fluorescent protein. We observe a greater sequence optimization efficiency (increase in fitness per optimization step) using ReLSO compared with other approaches, where ReLSO more robustly generates high-fitness sequences. Furthermore, the attention-based relationships learned by the jointly trained ReLSO models provide a potential avenue towards sequence-level fitness attribution information. Articles NaTURe MachINe INTeLLIgeNceAn alternative to working in the sequence space is to learn a low-dimensional, semantically rich representation of peptides and proteins. These latent representations collectively form the latent space, which is easier to navigate. With this approach, a therapeutic candidate can be optimized using its latent representation, in a procedure called latent space optimization.Here we propose ReLSO, a deep transformer-based approach to protein design, which combines the powerful encoding ability of a transformer model with a bottleneck that produces information-rich, low-dimensional latent representations. The latent space in ReLSO, besides being low dimensional, is regularized to be (1) smooth with respect to structure and fitness by way of fitness prediction from the latent space, (2) continuous and interpolatable between training data points and (3) pseudoconvex on the basis of negative sampling outside the data. This highly designed latent space enables optimization directly in latent space using gradient ascent on the fitness and converges to an optimum that can then be decoded back into the sequence space.Key contributions of ReLSO include the following.

Migratory cells transition between dispersed individuals and multicellular collectives during development, wound healing, and cancer. These transitions are associated with coordinated behaviors as well as arrested motility at high cell densities, but remain poorly understood at lower cell densities. Here, we show that dispersed mammary epithelial cells organize into arrested, fractal-like clusters at low density in reduced epidermal growth factor (EGF). These clusters exhibit a branched architecture with a fractal dimension of Df=1.7, reminiscent of diffusion-limited aggregation of nonliving colloidal particles. First, cells display diminished motility in reduced EGF, which permits irreversible adhesion upon cell–cell contact. Subsequently, leader cells emerge that guide collectively migrating strands and connect clusters into space-filling networks. Thus, this living system exhibits gelation-like arrest at low cell densities, analogous to the glass-like arrest of epithelial monolayers at high cell densities. We quantitatively capture these behaviors with a jamming-like phase diagram based on local cell density and EGF. These individual to collective transitions represent an intriguing link between living and nonliving systems, with potential relevance for epithelial morphogenesis into branched architectures.