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.

Hydrogel building blocks that are stimuli-responsive and self-adhesive could be utilized as a simple “do-it-yourself” construction set for soft machines and microfluidic devices.

We use topological data analysis and machine learning to study a seminal model of collective motion in biology [D'Orsogna et al., Phys. Rev. Lett. 96 (2006)]. This model describes agents interacting nonlinearly via attractive-repulsive social forces and gives rise to collective behaviors such as flocking and milling. To classify the emergent collective motion in a large library of numerical simulations and to recover model parameters from the simulation data, we apply machine learning techniques to two different types of input. First, we input time series of order parameters traditionally used in studies of collective motion. Second, we input measures based in topology that summarize the time-varying persistent homology of simulation data over multiple scales. This topological approach does not require prior knowledge of the expected patterns. For both unsupervised and supervised machine learning methods, the topological approach outperforms the traditional one.

Invading cancer cells adapt their migration phenotype in response to mechanical and biochemical cues from the extracellular matrix. For instance, mesenchymal migration is associated with strong cell-matrix adhesions and an elongated morphology, while amoeboid migration is associated with minimal cell-matrix adhesions and a rounded morphology. However, it remains challenging to elucidate the role of matrix mechanics and biochemistry since these are both dependent on extracellular matrix protein concentration. Here, we demonstrate a composite silk fibroin and collagen I hydrogel where stiffness and microstructure can be systematically tuned over a wide range. Using an overlay assay geometry, we show that the invasion of metastatic breast cancer cells exhibits a biphasic dependence on silk fibroin concentration at fixed collagen I concentration, first increasing as the hydrogel stiffness increases then decreasing as the pore size of silk fibroin decreases. Indeed, mesenchymal morphology exhibits a similar biphasic dependence on silk fibroin concentration, while amoeboid morphologies were favored when cell-matrix adhesions were less effective. We used exogenous biochemical treatments to perturb cells toward increased contractility and a mesenchymal morphology as well as to disrupt cytoskeletal function and promote an amoeboid morphology. Overall, we envision that this tunable biomaterial platform in a 96well plate format will be widely applicable to screen cancer cell migration against combinations of designer biomaterials and targeted inhibitors.

Interacting, self-propelled particles such as epithelial cells can dynamically self-organize into complex multicellular patterns, which are challenging to classify without \emph{a priori} information. Classically, different phases and phase transitions have...

Collective cell migration plays an important role in development. Here, we study the posterior lateral line primordium (PLLP) a group of about 100 cells, destined to form sensory structures, that migrates from head to tail in the zebrafish embryo. We model mutually inhibitory FGF-Wnt signalling network in the PLLP and link tissue subdivision (Wnt receptor and FGF receptor activity domains) to receptor-ligand parameters. We then use a 3D cell-based simulation with realistic cell-cell adhesion, interaction forces, and chemotaxis. Our model is able to reproduce experimentally observed motility with leading cells migrating up a gradient of CXCL12a, and trailing (FGF receptor active) cells moving actively by chemotaxis towards FGF ligand secreted by the leading cells. The 3D simulation framework, combined with experiments, allows an investigation of the role of cell division, chemotaxis, adhesion, and other parameters on the shape and speed of the PLLP. The 3D model demonstrates reasonable behaviour of control as well as mutant phenotypes.