Optically addressable spin-based quantum sensors enable nanoscale measurements of temperature, magnetic field, pH, and other physical properties of a system. Advancing the sensors beyond proof-of-principle demonstrations in living cells and multicellular organisms toward reliable, damage-free quantum sensing poses three distinct technical challenges. First, spin-based quantum sensing requires optical accessibility and microwave delivery. Second, any microelectronics must be biocompatible and designed for imaging living specimens. Third, efficient microwave delivery and temperature control are essential to reduce unwanted heating and to maintain an optimal biological environment. Here, we present the quantum biosensing chip (Q-BiC), which facilitates microfluidic-compatible microwave delivery and includes on-chip temperature control. We demonstrate the use of Q-BiC in conjunction with nanodiamonds containing nitrogen vacancy centers to perform optically detected magnetic resonance in living systems. We quantify the biocompatibility of microwave excitation required for optically detected magnetic resonance both in vitro in HeLa cells and in vivo in the nematode Caenorhabditis elegans for temperature measurements and determine the microwave-exposure range allowed before detrimental effects are observed. In addition, we show that nanoscale quantum thermometry can be performed in immobilized but nonanaesthetized adult nematodes with minimal stress. These results enable the use of spin-based quantum sensors without damaging the biological system under study, facilitating the investigation of the local thermodynamic and viscoelastic properties of intracellular processes.

The transport of macromolecules, such as DNA, through the cytoskeleton is critical to wide-ranging cellular processes from cytoplasmic streaming to transcription. The rigidity and steric hindrances imparted by the network of filaments comprising the cytoskeleton often leads to anomalous subdiffusion, while active processes such as motor-driven restructuring can induce athermal superdiffusion. Understanding the interplay between these seemingly antagonistic contributions to intracellular dynamics remains a grand challenge. Here, we use single-molecule tracking to show that the transport of large linear and relaxed circular DNA through motor-driven microtubule networks can be non-Gaussian and multimodal, with the degree and spatiotemporal scales over which these features manifest depending nontrivially on the state of activity and DNA topology. For example, active network restructuring increases caging and non-Gaussian transport modes of linear DNA, while dampening these mechanisms for circular DNA. We further discover that circular DNA molecules exhibit either markedly enhanced subdiffusion or superdiffusion compared to their linear counterparts, in the absence or presence of kinesin activity, indicative of microtubules threading circular DNA. This strong coupling leads to both stalling and directed transport, providing a direct route towards parsing distinct contributions to transport and determining the impact of coupling on the transport signatures. More generally, leveraging macromolecular topology as a route to programming molecular interactions and transport dynamics is an elegant yet largely overlooked mechanism that cells may exploit for intracellular trafficking, streaming, and compartmentalization. This mechanism could be harnessed for the design of self-regulating, sensing, and reconfigurable biomimetic matter.

Many bacterial chromosomes show large-scale linear order, so that a locus's genomic position correlates with its position along the cell. In the model organism Escherichia coli, for instance, the left and right arms of the circular chromosome lie in different cell halves. However, no mechanisms that anchor loci to the cell poles have been identified, and it remains unknown how this so-called “left-ori-right” organization arises. Here we construct a biophysical model that explains how global chromosome order could be established via an active loop extrusion mechanism. Our model assumes that the motor protein complex MukBEF extrudes loops on most of the E. coli chromosome but is excluded from the terminal region by the protein MatP, giving rise to a partially looped ring polymer structure. Using three-dimensional simulations of loop extrusion on a chromosome, we find that our model can display stable left-ori-right chromosomal order in a parameter regime consistent with prior experiments. We explain this behavior by considering the effect of loop extrusion on the bending rigidity of the chromosome, and derive necessary conditions for left-ori-right order to emerge. Finally, we develop a phase diagram for the system, where order emerges when the loop size is large enough and the looped region is compacted enough. Our work provides a theoretical and mechanistic explanation for how loop-extruders can establish linear chromosome order in E. coli and how this order leads to accurate gene positioning within the cell, without locus anchoring.

Spontaneous brain activity is not random but contains complex dynamical structures such as neuronal avalanches with power-law duration and size distributions. These experimental observations have been interpreted as supporting evidence for the hypothesis that the brain is operating near a critical point of a phase transition, and attracted much attention. Here, we show that a dynamical state of coherent bursting, with power-law distributed avalanches and features as observed in experiments, emerges in networks of adaptive neurons with stochastic input when excitation is sufficiently strong and balanced by adaptation. We demonstrate that these power-law distributed avalanches are consequences of stochasticity and coherent bursting, which in turn is the result of a balance between excitation and adaptation. Our work thus suggests that the observed dynamical features of brain activity can occur in a dynamical regime that emerges from collective stochastic dynamics of adaptive neurons under suitable conditions.

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 ($\sim 20$ 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.

It was recently discovered that eukaryotic cells are host to a multiplicity of biomolecular condensates. These condensates typically contain protein and/or RNA components with intrinsically disordered regions (IDRs). While IDRs have been proposed and demonstrated to play many roles in condensate biology, we suggest here an additional crucial role of IDRs, which is to exclude unwanted “intruders” from condensates. This exclusion effect arises from the large conformational entropy of IDRs; i.e., there is a high free-energy cost to occupying space that would otherwise be available to the IDRs. By combining polymer theory with sticker-spacer simulations, we show that the relevant insertion free energy increases with the concentration of IDRs in the condensate as well as with intruder size, attaining a linear scaling with surface area for large intruders. We find that at realistic IDR concentrations, particles as small as the size of an average protein (4 nm in diameter) can be $97\%$ excluded from condensates. To overcome this entropic barrier, molecules must interact favorably with condensate components to be recruited as clients. Application of the developed size-exclusion theory to biological condensates suggests that condensate IDRs may play a generic exclusionary role across organisms and types of condensates.

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.

The tubulinlike protein FtsZ (filamenting temperature-sensitive mutant Z) is crucial for cytokinesis in bacteria and many archaea, forming a ring-shaped structure called the Z-ring at the site of cell division. Despite extensive research, the self-assembly of Z-rings is not entirely understood. We propose a theoretical model based on FtsZ's known filament structures, treating them as semiflexible polymers with specific mechanical properties and lateral intersegment attraction that can stabilize ring formations. Our molecular dynamics simulations reveal various morphological phases, including open helices, chains, rings, and globules, capturing experimental observations in the fission yeast model using FtsZ from different bacterial species or mutants of Escherichia coli. Using our theoretical model, we explore how treadmilling activity affects Z-ring stability and identify a spooling mechanism of ring formation. The active ring produces contractile, shear, and rotational stresses, which intensify as the Z-ring transitions to an open helix at high activity.

Cytoplasmic streaming is driven by molecular motors that move along the cytoskeleton and entrain the surrounding fluid. In certain cell types, the direction of cytoplasmic streaming is not predefined but rather stochastic. For example, in meiotic cytoplasmic streaming of Caenorhabditis elegans zygotes, the direction of the swirl reverses over time [Kimura et al., Nat. Cell Biol. 19, 399 (2017) ]. While a mean-field theory explained the reversal, the structure of flows in three-dimensional space and the mechanical role of the endoplasmic reticulum (ER) remains unknown. To test the hypothesis that the elastic ER bonds and hydrodynamic interactions between microtubules mediate the orientational order of microtubules, leading to directional cytoplasmic streaming, we computationally investigated fluid-structure interactions in cytoplasmic flow, ER networks, and microtubule structures in the presence of microtubule dynamic instability. The results indicate that the occasionally reversing swirls emerge within a certain range of ER elasticity, and our experimental measurements of the ER elasticity agree with the numerical predictions. Mass transport analysis further demonstrates that the reversing swirls are suitable for intracellular particle mixing. These findings illustrate the delicate balance of meiotic cytoplasmic streaming and provide insights into the physiology of the embryogenesis.

Bacteria can form a great variety of spatially heterogeneous cell density patterns, ranging from simple concentric rings to dynamical spiral waves appearing in growing colonies. These pattern formation phenomena are important as they reflect how cellular processes such as metabolism operate in heterogeneous chemical environments. In the laboratory, they can be studied in simplified setups, where spatial gradients of oxygen and nutrients are externally imposed, and cells are immobilized in a gel matrix. An intriguing example, observed in such setups over 80 years ago, is the sequential formation of narrow bands of high cell density, taking place even for a clonal population. However, key aspects of the dynamics of band formation remained obscure. Using time-lapse imaging of replicate transparent columns in simplified growth media, we first quantify the precision of the positioning and timing of band formation. We also show that the appearance and position of different bands can be modulated independently. This “modularity” is suggested by the observation that different bands differ in their gene expression, and it is reproduced by a theoretical model based on the existence of internal metabolic states and the induction of a pH gradient. Finally, we can also modify the observed pattern formation by introducing genetic modifications that impair selected metabolic pathways. In our opinion, the possibility of precise measurements and controls, together with the simplicity and richness of the “proliferation pattern formation” phenomenon, can make it a model system to study the response of cellular processes to heterogeneous environments.

Early proteomic adaptations in cells serve as an initial line of defense, allowing populations to cope with environmental changes before long-term genetic alterations occur. Therefore, they are important in determining the trajectory taken by populations experiencing stress. Using a representative set of genes and DNA barcoding in Saccharomyces cerevisiae, we examine how two facets of these adaptations, gene expression and fitness, are distributed within populations experiencing different types and levels of abiotic stress. Together, our data reveal two fundamentally distinct modes of population-level stress response in yeast. The first mode is complex and exploratory, in which individual members adjust the expression of a large part of their genes, seemingly searching phenotypic space for a solution. This leads to phenotypic and fitness heterogeneity within the population. The second mode is degenerate, with all individuals adopting similar gene-expression levels and obtaining uniform fitness. The first mode is employed when a prevalent stress is encountered, and the second under a rare one. This hints at a role for historical encounters in phenotypic plasticity.

In this study, we suggest that detrended fluctuation analysis may reveal alternations in long-range temporal correlations associated with epileptic status. We consider two groups of subjects: patients with confirmed focal epilepsy, and healthy controls. Both groups were exposed to intermittent photic stimulation. Analysis based on event-related spectral perturbations revealed that photic driving in epileptic patients is higher in the $\alpha$ band and lower in other bands. This result is related to altered excitability in nonphotosensitive epilepsy. To prove this, we tested two hypotheses. First, we assess long-range temporal correlations using detrended fluctuation analysis, with the objective of evaluating whether this metric differs between epileptic patients and healthy controls through the application of between-subject statistics. Second, we investigate whether detrended fluctuation analysis provides more valuable insights into the aforementioned differences in comparison to traditional spectral analysis of brain signals. More precisely, we test if a machine learning algorithm trained on detrended fluctuation analysis-based features outperforms one trained on the spectral-based features in classifying between epileptic patients and controls. Furthermore, we study whether the features differ between classifiers by implementing feature importance assessment. Our findings demonstrate that the classifier based on detrended fluctuation analysis exhibits higher efficiency, and its features are notably distinct from those of the classifier based on spectral analysis. We postulate that the long-range temporal correlations captured via detrended fluctuation analysis reveal novel aspects of the epileptic brain response to intermittent photic stimulation, and they are more pronounced than the features captured via spectral analysis.

A large number of magnetic sensors, like superconducting quantum interference devices, optical pumping, and nitrogen vacancy magnetometers, were shown to satisfy the energy resolution limit. This limit states that the magnetic sensitivity of the sensor, when translated into a product of energy with time, is bounded below by Planck's constant, $\hslash$. This bound implies a fundamental limitation as to what can be achieved in magnetic sensing. Here we explore biological magnetometers, in particular three magnetoreception mechanisms thought to underly animals' geomagnetic field sensing: the radical-pair, the magnetite, and the MagR mechanism. We address the question of how close these mechanisms approach the energy resolution limit. At the quantitative level, the utility of the energy resolution limit is that it informs the workings of magnetic sensing in model-independent ways and thus can provide subtle consistency checks for theoretical models and estimated or measured parameter values, particularly needed in complex biological systems. At the qualitative level, the closer the energy resolution is to $\hslash$, the more “quantum” is the sensor. This offers an alternative route towards understanding the quantum biology of magnetoreception. It also quantifies the room for improvement, illuminating what nature has achieved, and stimulating the engineering of biomimetic sensors exceeding nature's magnetic sensing performance.

Quantum computers can accurately compute ground state energies using phase estimation, but this requires a guiding state that has significant overlap with the true ground state. For large molecules and extended materials, it becomes difficult to find guiding states with good ground state overlap for growing molecule sizes. Additionally, the required number of qubits and quantum gates may become prohibitively large. One approach for dealing with these challenges is to use a quantum embedding method, which allows a reduction to one or multiple smaller quantum cores embedded in a larger quantum region. In such situations, it is unclear how the embedding method affects the hardness of constructing good guiding states. In this work, we therefore investigate the preparation of guiding states in the context of quantum embedding methods. We extend previous work on quantum impurity problems, a framework in which we can rigorously analyze the embedding of a subset of orbitals. While there exist results for optimal active orbital space selection in terms of energy minimization, we rigorously demonstrate how the same principles can be used to define selected orbital spaces for state preparation in terms of the overlap with the ground state. Moreover, we perform numerical studies of molecular systems relevant to biochemistry, one field in which quantum embedding methods are required due to the large size of biomacromolecules such as proteins and nucleic acids. We investigate two different embedding strategies which can exhibit qualitatively different orbital entanglement. In all cases, we demonstrate that the easy-to-obtain mean-field state will have a sufficiently high overlap with the target state to perform quantum phase estimation.

The feedback between mechanical and chemical signals plays a key role in controlling many biological processes and collective cell behavior. Here we focus on the emergence of spatiotemporal density waves in a one-dimensional “cell train.” Combining a minimal theoretical model with in vitro experiments on MDCK epithelial cells confined to a linear pattern, we examine the spontaneous oscillations driven by feedback between myosin activation and mechanical deformations, as well as their effect on the response of the tissue to externally applied deformations. We show that the nature and frequency of spontaneous oscillations is controlled by the size of the cell train, with a transition from size-dependent standing waves to intrinsic spontaneous waves at the natural frequency of the tissue. The response to external boundary perturbations exhibits a resonance at this natural frequency, providing a possible venue for inferring the mechanochemical couplings that control the tissue behavior from rheological experiments.

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.

The preferred conditions for evolutionary innovation are a fundamental question, but little is known, in part because the question involves rare events. We focus on the potential role of polyploidy in the evolution of novel traits. There are two hypotheses regarding the effects of polyploidy on evolution: Polyploidy reduces the effect of a single mutation and slows evolution. In contrast, the gene redundancy introduced by polyploidy will promote neofunctionalization and accelerate evolution. Does polyploidy speed up or slow down evolution? In this study, we propose a simple model of polyploid cells and show that the evolutionary rate of polyploids is similar to or much slower than that of haploids under neutral selection or during gradual evolution. However, on a fitness landscape where cells should jump over a lethal valley to increase their fitness, the probability of evolution in polyploidy could be drastically increased, and the optimal number of chromosomes is identified. We theoretically discuss the existence of this optimal chromosome number from the large-deviation theory. Furthermore, we propose that the optimization for achieving evolutionary innovation could determine the range of chromosome number in polyploid bacteria.

The motility of eukaryotic cells is strongly influenced by their environment, with confined cells often developing qualitatively different motility patterns from those migrating on simple two-dimensional substrates. Recent experiments, coupled with data-driven methods to extract a cell's equation of motion, showed that cancerous MDA-MB-231 cells persistently hop in a limit cycle when placed on two-state adhesive micropatterns (two large squares connected by a narrow bridge), while they remain stationary on average in rectangular confinements. In contrast, healthy MCF10A cells migrating on the two-state micropattern are bistable, i.e., they settle into either basin on average with only noise-induced hops between the two states. We can capture all these behaviors with a single computational phase field model of a crawling cell, under the assumption that contact with nonadhesive substrate inhibits the cell front. Our model predicts that larger and softer cells are more likely to persistently hop, while smaller and stiffer cells are more likely to be bistable. Other key factors controlling cell migration are the frequency of protrusions and their magnitude of noise. Our results show that relatively simple assumptions about how cells sense their geometry can explain a wide variety of different cell behaviors, and show the power of data-driven approaches to characterize both experiment and simulation.

Biological sensors rely on the temporal dynamics of ligand concentration for signaling. The sensory performance is bounded by the distinguishability between the sensory state transition dynamics under different environmental protocols. This work presents a comprehensive theory to characterize arbitrary transient sensory dynamics of biological sensors. Here the sensory performance is quantified by the Kullback-Leibler (KL) divergence between the probability distributions of the sensor's stochastic paths. We introduce a novel benchmark to assess a sensor's transient sensory performance arbitrarily far from equilibrium. We identify a counterintuitive phenomenon in multistate sensors: while an initial exposure to high ligand concentration may hinder a sensor's sensitivity towards a future concentration up-shift, certain sensors may show a boost in sensitivity if the initial high concentration exposure is followed by a transient resetting at a low concentration environment. The boosted performance exceeds that of a sensor starting from an initially low concentration environment. This effect, reminiscent of a drug withdrawal effect, can be explained by the Markovian dynamics of the multistate sensor, similar to the Markovian Mpemba effect. Moreover, an exhaustive machine learning study of four-state sensors reveals a tight connection between the sensor's performance and the structure of the Markovian graph of its states.