Rxiv mechanobio12h ago
📰 "Modeling Dynamics, Cell Type Specificity, and Perturbations in Gene Regulatory Networks"
https://arxiv.org/abs/2602.18854 #Dynamics #Q-Bio.Mn #Cell
Modeling Dynamics, Cell Type Specificity, and Perturbations in Gene Regulatory Networks

Gene regulatory networks (GRNs) define the regulatory relationships among molecules such as transcription factors, chromatin remodelers, and target genes. GRNs play a critical role in diverse biological processes, including development, disease manifestation, and evolution. However, fully characterizing these networks across multiple cell types and states remains a significant challenge. Recent advances in single-cell omics have dramatically enhanced our ability to measure biological systems at unprecedented resolution. These technologies have opened new avenues for computational methods to infer GRNs, offering deeper insights into cell type-specific mechanisms, causality, and dynamic regulatory processes. This review summarizes the current state of GRN inference from single cell omic datasets, with a particular focus on dynamics and perturbations, and outlines key open challenges that must be addressed to advance the field.

arXiv.org
Rxiv mechanobio12h ago
📰 "Geometric Limits of Mitotic Pressure Under Confinement"
https://arxiv.org/abs/2602.18909 #Physics.Bio-Ph #Cond-Mat.Soft #Mechanical #Q-Bio.Cb #Cell
Geometric Limits of Mitotic Pressure Under Confinement

Cells often divide under mechanical confinement, where surrounding structures restrict shape changes during cytokinesis. Although forces generated during confined division have been measured experimentally, it remains unclear how confinement geometry and mechanics determine the transmitted force. Here we develop a minimal mechanical theory of cell division under confinement. Modeling the cell as an incompressible volume bounded by an interface with effective isotropic tension, we show that confinement restricts the set of mechanically admissible furrow shapes. As the furrow radius decreases, it reaches it reaches a confinement-induced minimum. Beyond this point, further ingression does not alter the interface shape, and both pressure and axial force saturate. We analyze force and pressure in rigid, soft, and strong three-dimensional confinement and demonstrate that a single geometric mechanism underlies these distinct cases. After rescaling force and length by the appropriate geometric scale, cells of different size and surface tension collapse onto a single universal curve. The relevant length scale is the cell size for rigid and soft confinement, and the confinement size in fully enclosing three-dimensional confinement. In soft confinement, environmental stiffness and spindle-generated axial forces determine the operating force and pressure, while the geometric constraint fixes the maximal attainable levels. In summary, our results show that mitotic force transmission and mitotic pressure during cytokinesis are bounded by confinement geometry, with material properties and active forces selecting the operating point within these geometry-imposed limits.

arXiv.org
Rxiv mechanobio12h ago
📰 "Multi-scale modeling of Snail-mediated response to hypoxia in tumor progression"
https://arxiv.org/abs/2404.16769 #CellMigration #Q-Bio.Cb #Cell
Multi-scale modeling of Snail-mediated response to hypoxia in tumor progression

Tumor cell migration within the microenvironment is a crucial aspect for cancer progression and, in this context, hypoxia has a significant role. An inadequate oxygen supply acts as an environmental stressor inducing migratory bias and phenotypic changes. In this paper, we propose a novel multi-scale mathematical model to analyze the pivotal role of Snail protein expression in the cellular responses to hypoxia. Starting from the description of single-cell dynamics driven by the Snail protein, we construct the corresponding kinetic transport equation that describes the evolution of the cell distribution. Subsequently, we employ proper scaling arguments to formally derive the equations for the statistical moments of the cell distribution, which govern the macroscopic tumor dynamics. Numerical simulations of the model are performed in various scenarios with biological relevance to provide insights into the role of the multiple tactic terms, the impact of Snail expression on cell proliferation, and the emergence of hypoxia-induced migration patterns. Moreover, quantitative comparison with experimental data shows the model's reliability in measuring the impact of Snail transcription on cell migratory potential. Through our findings, we shed light on the potential of our mathematical framework in advancing the understanding of the biological mechanisms driving tumor progression.

arXiv.org
Rxiv mechanobio12h ago
📰 "A kinetic derivation of spatial distributed models for tumor-immune system interactions"
https://arxiv.org/abs/2410.17420 #Q-Bio.Cb #Dynamics #Q-Bio.To #Math.Ds #Cell
A kinetic derivation of spatial distributed models for tumor-immune system interactions

We propose a mathematical kinetic framework to investigate interactions between tumor cells and the immune system, focusing on the spatial dynamics of tumor progression and immune responses. We develop two kinetic models: one describes a conservative scenario where immune cells switch between active and passive states without proliferation, while the other incorporates immune cell proliferation and apoptosis. By considering specific assumptions about the microscopic processes, we derive macroscopic systems featuring linear diffusion, nonlinear cross-diffusion, and nonlinear self-diffusion. Our analysis provides insights into equilibrium configurations and stability, revealing clear correspondences among the macroscopic models derived from the same kinetic framework. Using dynamical systems theory, we examine the stability of equilibrium states and conduct numerical simulations to validate our findings. These results highlight the significance of spatial interactions in tumor-immune dynamics, paving the way for a structured exploration of therapeutic strategies and further investigations into immune responses in various pathological contexts.

arXiv.org
Vladimir Savić16h ago
"100% of my coding is done by Claude Code! Oh, and I also use $10 million in tokens a day." #q
Vladimir Savić17h ago
"One of the biggest PR scams of the 21st century is getting everyone to refer to LLMs as 'artificial intelligence'." #q
Rxiv mechanobio1d ago
📰 "Support Graph Preconditioners for Off-Lattice Cell-Based Models"
https://arxiv.org/abs/2410.04512 #CellMigration #Q-Bio.Cb #Math.Na #Cs.Na #Cell
Support Graph Preconditioners for Off-Lattice Cell-Based Models

Off-lattice agent-based models (or cell-based models) of multicellular systems are increasingly used to create in-silico models of in-vitro and in-vivo experimental setups of cells and tissues, such as cancer spheroids, neural crest cell migration, and liver lobules. These applications, which simulate thousands to millions of cells, require robust and efficient numerical methods. At their core, these models necessitate the solution of a large friction-dominated equation of motion, resulting in a sparse, symmetric, and positive definite matrix equation. The conjugate gradient method is employed to solve this problem, but this requires a good preconditioner for optimal performance. In this study, we develop a graph-based preconditioning strategy that can be easily implemented in such agent-based models. Our approach centers on extending support graph preconditioners to block-structured matrices. We prove asymptotic bounds on the condition number of these preconditioned friction matrices. We then benchmark the conjugate gradient method with our support graph preconditioners and compare its performance to other common preconditioning strategies.

arXiv.org
Rxiv mechanobio1d ago
📰 "Generative Distribution Embeddings: Lifting autoencoders to the space of distributions for multiscale representation learning"
https://arxiv.org/abs/2505.18150 #Q-Bio.Qm #Dynamics #Stat.Ml #Cs.Lg #Cell
Generative Distribution Embeddings: Lifting autoencoders to the space of distributions for multiscale representation learning

Many real-world problems require reasoning across multiple scales, demanding models which operate not on single data points, but on entire distributions. We introduce generative distribution embeddings (GDE), a framework that lifts autoencoders to the space of distributions. In GDEs, an encoder acts on sets of samples, and the decoder is replaced by a generator which aims to match the input distribution. This framework enables learning representations of distributions by coupling conditional generative models with encoder networks which satisfy a criterion we call distributional invariance. We show that GDEs learn predictive sufficient statistics embedded in the Wasserstein space, such that latent GDE distances approximately recover the $W_2$ distance, and latent interpolation approximately recovers optimal transport trajectories for Gaussian and Gaussian mixture distributions. We systematically benchmark GDEs against existing approaches on synthetic datasets, demonstrating consistently stronger performance. We then apply GDEs to six key problems in computational biology: learning donor-level representations from single-nuclei RNA sequencing data (6M cells), capturing clonal dynamics in lineage-traced RNA sequencing data (150K cells), predicting perturbation effects on transcriptomes (1M cells), predicting perturbation effects on cellular phenotypes (20M single-cell images), designing synthetic yeast promoters (34M sequences), and spatiotemporal modeling of viral protein sequences (1M sequences).

arXiv.org
Vladimir Savić1d ago

… yet some people call it progress. 😜

"We replaced a very deterministic and precise way of specifying what we want from our software (code) with an abstraction layer (.md instruction files) that sometimes work, sometimes don't." #q

grizzly-82d ago

Когда вы кого-то прощаете, это не значит, что у человека больше нет долгов за произошедшее. Это значит, что его долги вы отвязали от себя. И передали их в коллекторское агентство "Карма inc." #q #лопата #странные_мысли