By operating in different thermodynamic modes, hair cell bundles in the inner ear dynamically regulate power exchange with mechanical signals at the smallest biological scales.

This tutorial for coarse-grained cellular growth modeling provides a hands-on, step-by-step guide for constructing and analyzing different aspects of cellular growth and covers essential concepts, recent literature, and key challenges for the field; ready to cook?

A cytoskeleton-inspired mechanical network with mechanosensitive proteins and motors performs contrastive learning from environmental cues via strain-rate dependent reactions and active forces, while maintaining learned responses despite continuous molecular turnover.

A study of bacterial movement across a range of both unconfined and highly confined environments establishes a quantitative link between behavioral rules and environmental context and reveals how E. coli can navigate complex habitats.

By analyzing bicoid regulation in the fruit fly embryo, this study shows that weak clustering of transcription factors can enable maximal transfer of information from molecular signals to downstream gene expression, and that timescales are crucial for determining the optimal clustering strength.

Multicellular clusters remain fluid and move together in low chemoattractant gradients, while above a threshold gradient clusters become unstable due to local, cluster-shape dependent velocity differentials that cause them to eventually break apart.

By studying three dimensional cell shapes and tissue structure in the developing fruit fly embryo, this study uncovers how epithelial cells exchange neighbors over their full apical-basal span during convergent extension — revealing new insights into the structure & dynamics of remodeling epithelia.

Resonant frequencies prioritize multi-looped structures or higher-level loops close to the driving source, while the network is prone to vessel shunting away from resonances — underscoring the importance of short-term pulsatility on long-term structures in adapting, periodically driven elastic flow networks like mammalian vasculature.

A mesoscopic model of finite-size networks for Poisson neurons with random connectivity, external noise, and disordered mean inputs accurately describes fluctuations and nonlinearities and outperforms prior models that neglected variance in connectivity.

This study proposes that olfactory systems can adapt to rapidly fluctuating odor backgrounds using a manifold learning mechanism and demonstrates how this mechanism can outperform traditional predictive filtering methods.

By analyzing the connection between promoter architecture and the ability to quickly decode transcription factor concentration, this study shows that, in the presence of non-cognate ligands, fast and accurate networks do not need to be optimally selective.

By studying elongated cells growing on a nematic elastomer substrate, this study finds that global alignment with the substrate is only achieved above a critical density — suggesting a collective mechanism for sensing substrate anisotropy.

In recurrently coupled networks of spiking neuron models, causal spike-timing-dependent plasticity permits assembly learning and allows significant overlap in assembly membership without the risk of post-training fusion and erasure of learned structure.

By investigating the growth of vesicles driven by an external reservoir, this study finds that active material exchange can drive a dramatic renormalization of a membrane’s effective mechanical properties and impact the trajectory of vesicle growth.

The co-localization of viral RNA and capsids within a condensate strongly enhances assembly rates, yields, and selectivity — suggesting that viral condensates provide a mechanism to ensure the robust and selective assembly of virions around viral genomes.

Using planarian gut branching as biological inspiration, this study develops a model that integrates tissue interface growth, morphogen dynamics, and organismal growth to explain how branched organ structures form and scale.

By examining collective patterning in a tractable model, this study reveals a trade-off between speed and accuracy and identifies counterintuitive properties of collective behavior, for instance optimized strategies do not always maximize information transfer.

By studying simple model cortical networks with dendritic calcium spikes, this study shows how dendritic nonlinearities and compartmentalized inputs can interact to shape population activity.

A study of the equilibrium phases of a generalized Lotka-Volterra model characterized by non-Gaussian species abundance distributions demonstrates how sparsity is a key feature for accurately modeling realistic ecological phenomena.

Chromatin-mediated coupling between cycles of DNA methylation and demethylation can lead to the emergence of phase-locked domains — regions of locally synchronized turnover activity whose coarsening is arrested by genomic heterogeneity.

Free energy calculations coupled with experimental data show that the intrinsic opening of big potassium channels is likely an inherent property of the vapor barrier generated by hydrophobic dewetting of the inner pore in the deactivated state.

By using temporal light patterns and photo-triggers to create virtual sensory landscapes for cells, the reverse opto-chemical engineering technique can be used to both monitor and remotely control the movement of cells.

A cell-level physical model reveals a critical switch fraction where neurodegenerative disease dynamics transition from slow, spontaneous seeding to rapid, runaway propagation.

Spontaneous retinal waves may be nature’s way to initialize connections — seeding visual circuit structure before experience.

A recurrent neural network model containing neurons with intrinsic plasticity measures how neuronal excitability varies with plasticity under different synaptic inputs — illuminating how these dynamics influence heterogeneity in neural responses.

Reverse-time stochastic dynamics, distinct from the forward-time dynamics widely used in stochastic thermodynamics, is fundamental for understanding the directed, goal-reaching dynamics that are a common feature of many biological processes.

By integrating anisotropic network models that incorporate membrane contact probability with perturbation response scanning, this study simulates conformational changes in mechanosensitive proteins — enabling the examination of various gating mechanisms.

By analyzing how multicomponent mixtures separate into distinct phases, this framework uncovers two scaling regimes, providing a quantitative way to characterize condensate phase behavior.

This statistical physics framework models peptidomes across species and shows that self and nonself peptides are nearly one and the same, implying that the immune system benefits by targeting antigens close to those represented in the organism’s own proteome.

By integrating genome editing data with a random forest approach algorithm, scientists uncovered a link between the cleavage efficiency of CRISPR and the R-loop unfolding rate, enhancing the prediction of on- and off-target gene editing efficiencies.

The proposed theory shows how animals could exploit variability in random stimulus sequences to enhance the reliability of inferences underlying survival-critical tasks.

This study details a data-driven implementation of Fisher’s geometric model — providing an empirically grounded approach for understanding evolutionary dynamics using deep learning methods.

Using a new method to characterize the mechanical properties and driving forces of microtubules in living cells, this study finds that polyglutamylation increases microtubule stiffness, while tube-like mitochondria have a much lower bending stiffness.

This study analyzes the interplay between mechanical activity, lateral adhesion, and hexatic order by developing a continuum model of epithelial layers — providing insights on the general principles governing pattern formation and stability.

This work introduces an extended coarsening model of chromosomal crossover placement, which provides a coherent explanation of experimental data across mutants by incorporating material exchanges between droplets, the synaptonemal complex, and the nucleoplasm.

Using a transparent soil mimic and the model bacterium E. coli, this study characterized how soil texture affects bacterial motility across pore scales, finding bacteria shift from run-and-tumble behavior in large pores to frequent trapping in small ones.

This quantitative framework can image and track neutrophil and macrophage dynamics in larval zebrafish, enabling a method to assess the motility, behavior, and migration patterns of distinct cell populations, especially in the vicinity of the gut.

Inspired by Crick’s molecular memory proposal, this study analyzes a nonequilibrium protein model and identifies molecular automata that can be exploited for computation, including in molecular stopwatches and error-tolerant memory.

All cell surfaces work together mechanically to control the morphology of fluid-filled lumina enclosed by epithelia during tissue development.

Experiments that examine cytoskeletal mechanics in keratin-deficient cells demonstrate that an intricate relationship between actin and keratin cortices in wild-type cells provides stability under high strain.

By operating in different thermodynamic modes, hair cell bundles in the inner ear dynamically regulate power exchange with mechanical signals at the smallest biological scales.

This review article proposes that Marr’s three levels of analysis — the computational problem, the algorithms used, and their molecular implementation — can be a useful framework for studying developmental processes.