Explore the vast range of titles available.

The Cellular-Potts model is a powerful and ubiquitous framework for developing computational models for simulating complex multicellular biological systems. Cellular-Potts models (CPMs) are often computationally expensive due to the explicit modeling of interactions among large numbers of individual model agents and diffusive fields described by partial differential equations (PDEs). In this work, we develop a convolutional neural network (CNN) surrogate model using a U-Net architecture that accounts for periodic boundary conditions. We use this model to accelerate the evaluation of a mechanistic CPM previously used to investigate in vitro vasculogenesis. The surrogate model was trained to predict 100 computational steps ahead (Monte-Carlo steps, MCS), accelerating simulation evaluations by a factor of 562 times compared to single-core CPM code execution on CPU. Over short timescales of up to 3 recursive evaluations, or 300 MCS, our model captures the emergent behaviors demonstrated by the original Cellular-Potts model such as vessel sprouting, extension and anastomosis, and contraction of vascular lacunae. This approach demonstrates the potential for deep learning to serve as a step toward efficient surrogate models for CPM simulations, enabling faster evaluation of computationally expensive CPM simulations of biological processes.

Vertex models are a widespread approach for describing the biophysics and behaviors of multicellular systems, especially of epithelial tissues. Vertex models describe a wide variety of developmental scenarios and behaviors like cell rearrangement and tissue folding. Often, these models are implemented as single-use or closed-source software, which inhibits reproducibility and decreases accessibility for researchers with limited proficiency in software development and numerical methods. We developed a physics-based vertex model methodology in Tissue Forge, an open-source, particle-based modeling and simulation environment. Our methodology describes the properties and processes of vertex model objects on the basis of vertices, which allows integration of vertex modeling with the particle-based formalism of Tissue Forge, enabling an environment for developing mixed-method models of multicellular systems. Our methodology in Tissue Forge inherits all features provided by Tissue Forge, delivering open-source, extensible vertex modeling with interactive simulation, real-time simulation visualization and model sharing in the C, C++ and Python programming languages and a Jupyter Notebook. Demonstrations show a vertex model of cell sorting and a mixed-method model of cell migration combining vertex- and particle-based models. Our methodology provides accessible vertex modeling for a broad range of scientific disciplines, and we welcome community-developed contributions to our open-source software implementation.

The connexin 43 (Cx43) hemichannel (HC) in the mechanosensory osteocytes is a major portal for the release of factors responsible for the anabolic effects of mechanical loading on bone formation and remodeling. However, little is known about how the Cx43 molecule responds to mechanical stimulation leading to the opening of the HC. Here, we demonstrate that integrin α5β1 interacts directly with Cx43 and that this interaction is required for mechanical stimulation-induced opening of the Cx43 HC. Direct mechanical perturbation via magnetic beads or conformational activation of integrin α5β1 leads to the opening of the Cx43 HC, and this role of the integrin is independent of its association with an extracellular fibronectin substrate. PI3K signaling is responsible for the shear stress-induced conformational activation of integrin α5β1 leading to the opening of the HC. These results identify an unconventional function of integrin that acts as a mechanical tether to induce opening of the HC and provide a mechanism connecting the effect of mechanical forces directly to anabolic function of the bone.

The morphogenic process of convergent thickening (CT) was originally described as the mediolateral convergence and radial thickening of the explanted ventral involuting marginal zone (IMZ) of Xenopus gastrulae (Keller and Danilchik, 1988). Here, we show that CT is expressed in all sectors of the pre-involution IMZ, which transitions to expressing convergent extension (CE) after involution. CT occurs without CE and drives symmetric blastopore closure in ventralized embryos. Assays of tissue affinity and tissue surface tension measurements suggest CT is driven by increased interfacial tension between the deep IMZ and the overlying epithelium. The resulting minimization of deep IMZ surface area drives a tendency to shorten the mediolateral (circumblastoporal) aspect of the IMZ, thereby generating tensile force contributing to blastopore closure (Shook et al., 2018). These results establish CT as an independent force-generating process of evolutionary significance and provide the first clear example of an oriented, tensile force generated by an isotropic, Holtfreterian/Steinbergian tissue affinity change.

Collectively migrating Xenopus mesendoderm cells are arranged into leader and follower rows with distinct adhesive properties and protrusive behaviors. In vivo, leading row mesendoderm cells extend polarized protrusions and migrate along a fibronectin matrix assembled by blastocoel roof cells. Traction stresses generated at the leading row result in the pulling forward of attached follower row cells. Mesendoderm explants removed from embryos provide an experimentally tractable system for characterizing collective cell movements and behaviors, yet the cellular mechanisms responsible for this mode of migration remain elusive. We introduce a novel agent-based computational model of migrating mesendoderm in the Cellular-Potts computational framework to investigate the respective contributions of multiple parameters specific to the behaviors of leader and follower row cells. Sensitivity analyses identify cohesotaxis, tissue geometry, and cell intercalation as key parameters affecting the migration velocity of collectively migrating cells. The model predicts that cohesotaxis and tissue geometry in combination promote cooperative migration of leader cells resulting in increased migration velocity of the collective. Radial intercalation of cells towards the substrate is an additional mechanism contributing to an increase in migratory speed of the tissue. Model outcomes are validated experimentally using mesendoderm tissue explants.

Mechanistic, multicellular, agent-based models are commonly used to investigate tissue, organ, and organism-scale biology at single-cell resolution. The Cellular-Potts Model (CPM) is a powerful and popular framework for developing and interrogating these models. CPMs become computationally expensive at large space- and time- scales making application and investigation of developed models difficult. Surrogate models may allow for the accelerated evaluation of CPMs of complex biological systems. However, the stochastic nature of these models means each set of parameters may give rise to different model configurations, complicating surrogate model development. In this work, we leverage denoising diffusion probabilistic models (DDPMs) to train a generative AI surrogate of a CPM used to investigate in vitro vasculogenesis. We describe the use of an image classifier to learn the characteristics that define unique areas of a 2-dimensional parameter space. We then apply this classifier to aid in surrogate model selection and verification. Our CPM model surrogate generates model configurations 20,000 timesteps ahead of a reference configuration and demonstrates approximately a 22x reduction in computational time as compared to native code execution. Our work represents a step towards the implementation of DDPMs to develop digital twins of stochastic biological systems.

Cells use a piston-driven mechanism, among others, to migrate [Also see Report by Petrie et al. ] Directed cell migration in multicellular organisms is essential to fundamental processes including embryogenesis, defense from infection, and tissue repair and regeneration. It is also key in cancer progression as metastatic cells disseminate throughout the body. Much research has focused on cells migrating on two-dimensional (2D) extracellular matrices (ECMs) because such experimental systems are easily accessible and observable. However, recent studies of cell motility in 3D environments are challenging the ubiquity and generality of 2D-based models. On page 1062 of this issue, Petrie et al. ( 1 ) propose that in a constrained 3D space, a cell can use its nucleus as a piston to polarize internal pressure and create protrusions that facilitate movement.

Background Members of the disintegrin metalloproteinase (ADAM) family play important roles in cellular and developmental processes through their functions as proteases and/or binding partners for other proteins. The amphibian Xenopus has long been used as a model for early vertebrate development, but genome-wide analyses for large gene families were not possible until the recent completion of the X. tropicalis genome sequence and the availability of large scale expression sequence tag (EST) databases. In this study we carried out a systematic analysis of the X. tropicalis genome and uncovered several interesting features of ADAM genes in this species. Results Based on the X. tropicalis genome sequence and EST databases, we identified Xenopus orthologues of mammalian ADAMs and obtained full-length cDNA clones for these genes. The deduced protein sequences, synteny and exon-intron boundaries are conserved between most human and X. tropicalis orthologues. The alternative splicing patterns of certain Xenopus ADAM genes, such as adams 22 and 28 , are similar to those of their mammalian orthologues. However, we were unable to identify an orthologue for ADAM7 or 8. The Xenopus orthologue of ADAM15, an active metalloproteinase in mammals, does not contain the conserved zinc-binding motif and is hence considered proteolytically inactive. We also found evidence for gain of ADAM genes in Xenopus as compared to other species. There is a homologue of ADAM10 in Xenopus that is missing in most mammals. Furthermore, a single scaffold of X. tropicalis genome contains four genes encoding ADAM28 homologues, suggesting genome duplication in this region. Conclusions Our genome-wide analysis of ADAM genes in X. tropicalis revealed both conservation and evolutionary divergence of these genes in this amphibian species. On the one hand, all ADAMs implicated in normal development and health in other species are conserved in X. tropicalis . On the other hand, some ADAM genes and ADAM protease activities are absent, while other novel ADAM proteins in this species are predicted by this study. The conservation and unique divergence of ADAM genes in Xenopus probably reflect the particular selective pressures these amphibian species faced during evolution.

Mechanistic, multicellular, agent-based models are commonly used to investigate tissue, organ, and organism-scale biology at single-cell resolution. The Cellular-Potts Model (CPM) is a powerful and popular framework for developing and interrogating these models. CPMs become computationally expensive at large space- and time- scales making application and investigation of developed models difficult. Surrogate models may allow for the accelerated evaluation of CPMs of complex biological systems. However, the stochastic nature of these models means each set of parameters may give rise to different model configurations, complicating surrogate model development. In this work, we leverage denoising diffusion probabilistic models to train a generative AI surrogate of a CPM used to investigate in vitro vasculogenesis. We describe the use of an image classifier to learn the characteristics that define unique areas of a 2-dimensional parameter space. We then apply this classifier to aid in surrogate model selection and verification. Our CPM model surrogate generates model configurations 20,000 timesteps ahead of a reference configuration and demonstrates approximately a 22x reduction in computational time as compared to native code execution. Our work represents a step towards the implementation of DDPMs to develop digital twins of stochastic biological systems.

The Cellular-Potts model is a powerful and ubiquitous framework for developing computational models for simulating complex multicellular biological systems. Cellular-Potts models (CPMs) are often computationally expensive due to the explicit modeling of interactions among large numbers of individual model agents and diffusive fields described by partial differential equations (PDEs). In this work, we develop a convolutional neural network (CNN) surrogate model using a U-Net architecture that accounts for periodic boundary conditions. We use this model to accelerate the evaluation of a mechanistic CPM previously used to investigate vasculogenesis. The surrogate model was trained to predict 100 computational steps ahead (Monte-Carlo steps, MCS), accelerating simulation evaluations by a factor of 590 times compared to CPM code execution. Over multiple recursive evaluations, our model effectively captures the emergent behaviors demonstrated by the original Cellular-Potts model of such as vessel sprouting, extension and anastomosis, and contraction of vascular lacunae. This approach demonstrates the potential for deep learning to serve as efficient surrogate models for CPM simulations, enabling faster evaluation of computationally expensive CPM of biological processes at greater spatial and temporal scales.