Interpretable gene network inference with nonlinear causality
Interactions among genes orchestrate the growth and survival of all living systems. Such interactions are mediated by vast networks of time-dependent couplings, yet existing strategies for inferring gene networks from omics data do not account for the strong causal constraints acting on nonlinear dynamical systems. As a result, we show here that current gene network inference approaches fail when applied to time series from physical systems. We thus introduce RiCE, a physics-based algorithm for inferring causal interaction graphs using geometric information embedded within time series. We benchmark RiCE against 30 other gene network inference methods, including recent probabilistic machine learning methods, and obtain leading results across 15 datasets spanning diverse experimental modalities and tissue types. RiCE learns physically-interpretable parameters of complex biological systems, requiring low overhead to achieve leading performance. We show diverse applications of RiCE: identifying transition states during the epithelial-mesenchymal transition, classifying cell subtypes during immune cell activation, and determining transcriptional dynamics during endocrinogenesis. Our work demonstrates nonlinearity as a critical inductive bias to accurately infer gene-gene interactions, as well as a key quantitative metric for understanding the dynamic biology of the cell.
### Competing Interest Statement
The authors are affiliated with Medici Therapeutics in Cambridge, MA. All code, results, and data discussed in the manuscript are available at the provided GitHub link.
📰 "Guanine Nucleotide Exchange Factors and Small GTPases: Their Regulation and Functions, Diseases, and Therapeutic Targets"
https://doi.org/doi:10.1002/mco2.70362https://pubmed.ncbi.nlm.nih.gov/41020039/ #Cytoskeletal #Dynamics📰 "Targeting stiffness-dependent YAP/TAZ restores angiogenesis dynamics impaired by ALK1 knockout in silico"
https://www.biorxiv.org/content/10.1101/2025.09.26.678712v1?rss=1 #Cytoskeleton #Dynamics📰 "Emergent Isotropic-Nematic Transition in 3D Semiflexible Active Polymers"
https://arxiv.org/abs/2509.21599 #Cond-Mat.Stat-Mech
#Physics.Bio-Ph
#Cond-Mat.Soft
#Cytoskeletal #Dynamics
Emergent Isotropic-Nematic Transition in 3D Semiflexible Active Polymers
Active semiflexible filament collectives, ranging from motor-driven cytoskeletal filaments to slender organisms such as cyanobacteria and worm aggregates, abound in nature, yet how activity and flexibility jointly govern their organization, especially Isotropic-nematic (I-N) transition, remains poorly understood. Using large-scale Brownian dynamics simulations of 3D active semiflexible polymers with varying flexibility degrees, we show that tangential active forces systematically shift the I-N transition to higher densities, with the shift controlled by the flexibility degree and activity strength. Strikingly, activity alters the nature of the transition: discontinuous at low strengths, continuous at moderate strengths, and ultimately suppressed at high activity levels. At high densities, this suppression generates an active nematic state, sustained by continuous defect creation and annihilation but lacking global order. The delayed I-N transition originates from enhanced collective bending fluctuations, which reduce the effective persistence length and enlarge the effective confinement tube. At moderate activity levels, these fluctuations trigger large-scale excitations that stochastically drive temporal transitions between nematic and isotropic states. We summarize this behavior in non-equilibrium state diagrams of density and activity for different flexibility degrees.
📰 "50 mm $\times$ 50 mm Cesium Atomic Vapor Cell for Terahertz Imaging: Implementation and Application"
https://arxiv.org/abs/2509.22098 #Physics.Atom-Ph
#Physics.Optics
#Dynamics #Cell
50 mm $\times$ 50 mm Cesium Atomic Vapor Cell for Terahertz Imaging: Implementation and Application
Rydberg atomic sensors offer transformative potential for high-speed, high-sensitivity terahertz (THz) imaging. However, previous systems are hindered by restricted imaging areas, largely due to the compact dimension of atomic vapor cells and inefficient beam-shaping methodologies. We present a THz imaging system with a 50 mm $\times$ 50 mm area, enabled by a custom-engineered scaled-up atomic vapor cell and an optimized beam-shaping optical architecture. Experimental validation confirms that this system achieves near-diffraction-limited, high resolution THz imaging at 0.55 THz under ambient conditions. Furthermore, its capabilities are demonstrated through real-time visualization of the diffusion dynamics of a deionized water droplet in anhydrous ethanol. This work not only expands the boundaries of Rydberg atomic sensors but also establishes a critical foundation for advancing THz imaging technologies toward into real-world, large-scale applications.
📰 "Coarsening of biomimetic condensates in a self-stirring active fluid"
https://arxiv.org/abs/2509.21753 #Physics.Bio-Ph
#Cond-Mat.Soft
#Dynamics #Cell
Coarsening of biomimetic condensates in a self-stirring active fluid
Coarsening, the process where larger structures grow at the expense of smaller ones, is a fundamental aspect of multiphase systems. The cell cytoplasm exemplifies an out-of-equilibrium multiphase system, where phase-separated condensates nucleate and expand within an active fluid made of biopolymers and energy-dependent enzymes. In this study, we explore how condensates grow in a self-stirring active fluid by examining the coarsening of biomimetic condensates embedded in a 3D reconstituted cytoskeleton composed of microtubules and molecular motors. The strong agreement among experiments, an active hydrodynamic model, and computer simulations offers a comprehensive framework that explains why self-similarity is absent in active coarsening and identifies the origins of the continuously changing coarsening exponents for both active and passive condensates. The dynamics of coarsening are primarily determined by the statistics of binary droplet collisions, which depend on their size-dependent motility, regardless of whether they are active or passive. These results reveal a unifying control parameter for the coarsening process and size distribution of active condensates, broadening our understanding of phase separation in out-of-equilibrium systems and potentially impacting materials science and biology.
📰 "A phenotype-structured reaction-diffusion model of avascular glioma growth"
https://arxiv.org/abs/2509.22519 #Q-Bio.Pe
#Dynamics #Cell
A phenotype-structured reaction-diffusion model of avascular glioma growth
We consider a phenotype-structured reaction-diffusion model of avascular glioma growth. The model describes the interaction dynamics between tumour cells and oxygen, and takes into account anisotropic cell movement and oxygen diffusion related to structural anisotropy of the brain's extracellular environment. In this model, phenotypic heterogeneity of tumour cells is captured by a continuous phenotype-structuring variable, the value of which evolves due to phenotypic changes. We first analyse a one-dimensional version of the model and formally show, through a Hopf-Cole transformation, that it admits, in appropriate asymptotic regimes, phenotypically heterogeneous travelling wave solutions, wherein the locally prevailing cell phenotype varies across the wave due to the presence of oxygen gradients. This provides a mathematical formalisation for the emergence of intratumour phenotypic heterogeneity driven by differences in oxygen availability across the tumour. We then report on the results of both 1D simulations, which corroborate the results of formal asymptotic analyses, and 2D simulations, which also demonstrate the impact of anisotropy in cell movement and oxygen diffusion on tumour growth and on the phenotypic composition of the tumour edge. These results are complemented with additional results of 3D simulations, which are carried out on the geometry of the brain by using a hybrid finite difference-finite element method and integrating patient-specific magnetic resonance imaging data with diffusion tensor imaging data.
📰 "Blocking apoptosis promotes survival and alters developmental dynamics of human retinal ganglion cells in retinal organoids"
https://www.biorxiv.org/content/10.1101/2025.09.27.678991v1?rss=1 #Dynamics #Cell📰 "The two sides of resistance: aggressiveness and mitotic instability as the Achilles heel of Osimertinib-resistant NSCLC"
https://www.biorxiv.org/content/10.1101/2025.09.25.678204v1?rss=1 #Dynamics #Cell📰 "Population Consequences of Single-Cell Damage Dynamics: Theory and Experiment under Glucose Limitation in E. coli"
https://www.biorxiv.org/content/10.1101/2025.09.25.678661v1?rss=1 #Dynamics #Cell
Rxiv mechanobio