PRX life最新文献

Epithelial tissues are driven out of thermodynamic equilibrium by internally generated forces, causing complex patterns of motion. Even when both the forces and motion are measurable, it is not yet possible to relate the two, because the sources of energy injection and dissipation are often unclear. Here, we study how energy is transferred by developing a method to measure the effective viscosity from the shear stresses and strain rates within an epithelial cell monolayer. Interestingly, there emerged multicellular regions in which the relationship between shear stress and shear strain rate was negatively proportional, indicating a negative effective viscosity. The negative effective viscosity occurred in regions wherein cell stresses were less efficient at producing tissue deformations compared to regions of positive effective viscosity. Regions of negative effective viscosity consistently exhibited greater cell speed and vorticity, and the cells had elevated metabolic activity, reflecting an increased energy demand in these cells. Our study shows that negative effective viscosity is a useful means of quantifying the flow of energy in living matter.

To maintain normal functionality, it is necessary for a multicellular organism to generate robust responses to external temporal signals. However, the underlying mechanisms to coordinate the collective dynamics of cells remain poorly understood. Here, we study the calcium activity of biological neuron networks excited by periodic ATP stimuli. We use micropatterning to control the cells' physical connectivity. We find that whereas isolated cells become more synchronized in their calcium activity at long driving periods, connected cells become less synchronized, despite expressing more gap junctions which enable calcium exchange. To understand this result, we use a mathematical model in which a bifurcation analysis has previously shown coupling-induced desynchronization in an oscillatory network. Using parameters close to this bifurcation but in the excitable regime, we find that this desynchronization persists and can explain the experimental observations. The model further predicts that co-culturing with gap-junction-deficient cells should restore synchronization, which experiments confirm. Combining quantitative experiments, the physical and biological manipulation of cells, and mathematical modeling, our results suggest that cell-to-cell connectivity significantly affects how populations encode an external temporal signal as it slows down: Sparse networks synchronize due to longer entrainment, whereas highly connected networks can desynchronize due to dynamic frustration.

How ecosystems respond to environmental perturbations is a fundamental question in ecology, made especially challenging due to the strong coupling between species and their environment. Here, we introduce a theoretical framework for calculating the steady-state response of ecosystems to environmental perturbations in generalized consumer-resource models. Our construction is applicable to a wide class of systems, including models with nonreciprocal interactions, cross-feeding, and nonlinear growth/consumption rates. Within our framework, all ecological variables are embedded into four distinct vector spaces, and ecological interactions are represented by geometric transformations between these spaces. We show that, near a steady state, such geometric transformations directly map environmental perturbations-in resource availability and mortality rates-to shifts in niche structure. We illustrate these ideas in a variety of settings including a minimal model for pH-induced toxicity in bacterial denitrification. We end by discussing the biological implications of our framework. We show that it is extremely difficult to distinguish cooperative and competitive interactions by measuring the responses of species to external perturbations.

When cells in a primary tumor work together to invade into nearby tissue, this can lead to cell dissociations-cancer cells breaking off from the invading front-leading to metastasis. What controls the dissociation of cells and whether they break off singly or in small groups? Can this be determined by cell-cell adhesion or chemotactic cues given to cells? We develop a physical model for this question, based on experiments that mimic aspects of cancer cell invasion using microfluidic devices with microchannels of different widths. Experimentally, most dissociation events ("ruptures") involve single cells breaking off, but we observe some ruptures of large groups (~20 cells) in wider channels. The rupture probability is nearly independent of channel width. We recapitulate the experimental results with a phase-field cell motility model by introducing three different cell states (follower, guided, and high-motility "leader" cells) based on their spatial position. These leader cells may explain why single-cell rupture is the universal most probable outcome. Our simulation results show that cell-channel adhesion is necessary for cells in narrow channels to invade, and strong cell-cell adhesion leads to fewer but larger ruptures. Chemotaxis also influences the rupture behavior: Strong chemotaxis strength leads to larger and faster ruptures. Finally, we study the relationship between biological jamming transitions and cell dissociations. Our results suggest unjamming is necessary but not sufficient to create ruptures.

Proteins fold to a specific functional conformation with a densely packed core that controls their stability. Despite their importance, we lack a quantitative explanation for why all protein cores, regardless of their overall fold, possess the same average packing fraction $⟨\varphi ⟩\approx 0.55$ . However, important developments in the physics of jamming in particulate systems can shed light on the packing of protein cores. Here, we extend the framework of jamming to describe core packing in collapsed polymers, as well as in all-atom models of folded proteins. First, we show in a spherical bead-spring polymer model (with and without bond-angle constraints) that as the hydrophobic interactions increase relative to thermal fluctuations, a jamming-like transition occurs when the core packing fraction exceeds ${\varphi }_c$ with the same power-law scaling behavior for the potential energy $V_r$ , excess contact number $\Delta N$ , and characteristic frequency of the vibrational density of states ${\omega }^{*}$ versus $\Delta \varphi =\varphi -{\varphi }_c$ as that for jammed particulate systems. Then, we develop an all-atom model for proteins and find that, above ${\varphi }_c~0.55$ , protein cores undergo a jamming-like transition, but with anomalous power-law scaling for $V_r$ , $\Delta N$ , and ${\omega }^{*}$ versus $\Delta \varphi$ . The all-atom protein model remains close to the native protein structure during jamming and accurately refolds from partially unfolded states.

Sound produces surface waves along the cochlea's basilar membrane. To achieve the ear's astonishing frequency resolution and sensitivity to faint sounds, dissipation in the cochlea must be canceled via active processes in hair cells, effectively bringing the cochlea to the edge of instability. But how can the cochlea be globally tuned to the edge of instability with only local feedback? To address this question, we use a discretized version of a standard model of basilar membrane dynamics but with an explicit contribution from active processes in hair cells. Surprisingly, we find the basilar membrane supports two qualitatively distinct sets of modes: a continuum of localized modes and a small number of collective extended modes. Localized modes sharply peak at their resonant position and are largely uncoupled. As a result, they can be amplified almost independently from each other by local hair cells via feedback reminiscent of self-organized criticality. However, this amplification can destabilize the collective extended modes; avoiding such instabilities places limits on possible molecular mechanisms for active feedback in hair cells. Our work illuminates how and under what conditions individual hair cells can collectively create a critical cochlea.

The metastasis of solid tumors hinges on cancer cells navigating through complex three-dimensional tissue environments, characterized by mechanical heterogeneity and biological diversity. This process is closely linked to the dynamic migration behavior exhibited by cancer cells, which dictates the invasiveness of tumors. In our study, we investigate tumor spheroids composed of breast cancer cells embedded in three-dimensional (3D) collagen matrices. Through a combination of quantitative experiments, artificial-intelligence-driven image processing, and mathematical modeling, we uncover rapid transitions in cell phenotypes and phenotype-dependent motility among disseminating cells originating from tumor spheroids. Persistent invasion leads to continuous remodeling of the extracellular matrix surrounding the spheroids, altering the landscape of migration phenotypes. Consequently, filopodial cells emerge as the predominant phenotype across diverse extracellular matrix conditions. Our findings unveil the complex mesoscale dynamics of invading tumor spheroids, shedding light on the complex interplay between migration phenotype plasticity, microenvironment remodeling, and cell motility within 3D extracellular matrices.

Phenotype transitions occur in many biological processes such as differentiation and reprogramming. A fundamental question is how cells coordinate switching of gene expression clusters. By analyzing single-cell RNA sequencing data within the framework of transition path theory, we studied the genome-wide expression program switching in five different cell transition processes. For each process we reconstructed a reaction coordinate describing the transition progression, and inferred the gene regulatory network along this reaction coordinate. In all processes we observed a common pattern: the overall effective number and strength of regulation between different communities increase first and then decrease. This change is accompanied by similar changes in gene regulatory network frustration-defined as the overall conflict between the regulation received by genes and their expression states. Complementing previous studies suggesting that biological networks are modularized to contain perturbation effects locally, our analyses on the five cell transition processes likely reveal a general principle: during a cell phenotypic transition, intercommunity interactions increase to concertedly coordinate global gene expression reprogramming and canalize to specific cell phenotype, as Waddington visioned.

The relationships between protein sequences, structures, and functions are determined by complex codes that scientists aim to decipher. While structures contain key information about proteins' biochemical functions, they are often experimentally difficult to obtain. In contrast, protein sequences are abundant but are a step removed from function. In this paper, we propose residue level alignment (RLA)-a self-supervised objective for aligning sequence and structure embedding spaces. By situating sequence and structure encoders within the same latent space, RLA enriches the sequence encoder with spatial information. Moreover, our framework enables us to measure the similarity between a sequence and structure by comparing their RLA embeddings. We show how RLA similarity scores can be used for binder design by selecting true binders from sets of designed binders. RLA scores are informative even when they are calculated given only the backbone structure of the binder and no binder sequence information, which simulates the information available in many early-stage binder design libraries. RLA performs similarly to benchmark methods and is orders of magnitude faster, making it a valuable new screening tool for binder design pipelines.

Living systems continually respond to signals from the surrounding environment. Survival requires that their responses adapt quickly and robustly to the changes in the environment. One particularly challenging example is olfactory navigation in turbulent plumes, where animals experience highly intermittent odor signals while odor concentration varies over many length- and timescales. Here, we show theoretically that Drosophila olfactory receptor neurons (ORNs) can exploit proximity to a bifurcation point of their firing dynamics to reliably extract information about the timing and intensity of fluctuations in the odor signal, which have been shown to be critical for odor-guided navigation. Close to the bifurcation, the system is intrinsically invariant to signal variance, and information about the timing, duration, and intensity of odor fluctuations is transferred efficiently. Importantly, we find that proximity to the bifurcation is maintained by mean adaptation alone and therefore does not require any additional feedback mechanism or fine-tuning. Using a biophysical model with calcium-based feedback, we demonstrate that this mechanism can explain the measured adaptation characteristics of Drosophila ORNs.