Although bacterial chemotaxis behavior in a single gradient has been well studied, chemotaxis of bacterial population in complex environments is not well understood. Here, we discovered four distinct behaviors of Escherichia coli populations in microfluidic experiments with different opposing gradients of MeAsp and serine. By using a population chemotaxis model based on the dynamics of intracellular signaling pathways, we found that the nongenetic variability of the relevant chemoreceptors (Tar and Tsr) within the population is responsible for the diverse population behaviors. Through analyses combining the phenotype-to-performance mapping and Tar/Tsr ratio distribution, we predicted the phase diagram of population chemotaxis behaviors under varying chemical gradients and the effect of growth period on population behaviors, which were verified by additional experiments. Our study suggests that phenotypic heterogeneity in chemoreceptors enables diverse chemotactic strategies, which cells may adopt to improve their population fitness in complex environments.

Biomolecular condensates are membraneless organelles formed via phase separation of macromolecules, typically consisting of bond-forming “stickers” connected by flexible “linkers.” Linkers have diverse roles, such as occupying space and facilitating interactions. To understand how linker length relative to other lengths affects condensation, we focus on the pyrenoid, which enhances photosynthesis in green algae. Specifically, we apply coarse-grained simulations and analytical theory to the pyrenoid proteins of Chlamydomonas reinhardtii: the rigid holoenzyme Rubisco and its flexible partner EPYC1. Remarkably, halving EPYC1 linker lengths decreases critical concentrations by tenfold. We attribute this difference to the molecular “fit,” i.e., the number of stickers of EPYC1 that can bind to a single Rubisco given the constraint of EPYC1 linker length. We find an inverse relationship between molecular fit and the tendency of EPYC1 and Rubisco to phase separate. Moreover, by computationally varying Rubisco sticker locations we discover that the naturally occurring sticker locations yield the poorest fit for all EPYC1 linker lengths; thus the natural locations optimize phase separation. Surprisingly, shorter linkers mediate a transition to a gas of rods as Rubisco stickers approach the poles. These findings illustrate how intrinsically disordered proteins affect phase separation through the interplay of molecular length scales.

Many biological systems dynamically rearrange their components through a sequence of configurations in order to perform their functions. Such dynamic processes have been studied using network models that sequentially retrieve a set of stored patterns. Previous models of sequential retrieval belong to a general class in which the components of the system are controlled by feedback (input modulation). In contrast, we introduce a new class of models in which the feedback modifies the interactions among the components (interaction modulation). We show that interaction modulation models not only are capable of retrieving dynamic sequences, but they do so more robustly than input modulation models. In particular, we find that modulation of symmetric interactions allows retrieval of patterns with different activity levels and has a much larger dynamic capacity. Our results suggest that interaction modulation may be a common principle underlying biological systems that show complex collective dynamics.

The genotype-to-phenotype problem (G2P) for multicellular development asks how genetic inputs control collective phenotypic outputs. However, this is a challenging problem due to gene redundancy and stochasticity, causing mutations to have subtle phenotypic effects and replicates to display significant variation. We approach this problem using the model organism Myxococcus xanthus, a motile self-organizing bacterium that forms three-dimensional cell aggregates that mature into spore-filled fruiting bodies when under starvation stress. We develop a high-throughput imaging method using three-dimensional-printed microscopes to efficiently collect large phenotypic datasets. Our automated methods for analysis and visualization produce a map of phenotypic variation in M. xanthus development. We demonstrate that even subtle effects on developmental dynamics caused by mutation can be identified, discriminated, characterized, and given statistical significance, with implications for future gene annotation studies and the effect of environmental factors on G2P.

Membraneless organelles, liquidlike condensates composed of protein and nucleic acids, alter the biochemical and physical landscape of the cell. While specific membraneless organelles are found in stereotypical locations, little is known about the physical mechanisms that guide their positioning. Here, we investigate how stress granules, a type of cytoplasmic membraneless organelle, establish their stereotypical perinuclear positioning. We find that actin and microtubules play complementary roles. Lamellar actin confines stress granules, and its retrograde flow drives them toward the cell center. Microtubules, in turn, adhere to stress granules through capillary interactions, which tend to concentrate stress granules in microtubule-rich regions near the nucleus. Similar physical mechanisms are likely to play a role in the positioning of other membraneless organelles.

Complex living systems are thought to exist at the “edge of chaos” separating the ordered dynamics of robust function from the disordered dynamics of rapid environmental adaptation. Here, a deeper inspection of 72 experimentally supported discrete dynamical models of cell processes reveals previously unobserved order on long time scales, suggesting greater rigidity in these systems than was previously conjectured. We find that propagation of internal perturbations is transient in most cases, and that even when large perturbation cascades persist, their phenotypic effects are often minimal. Moreover, we find evidence that stochasticity and desynchronization can lead to increased recovery from regulatory perturbation cascades. Our analysis relies on new measures that quantify the tendency of perturbations to spread through a discrete dynamical system. Computing these measures was not feasible using current methodology; thus, we developed a multipurpose CUDA-based simulation tool, which we have made available as the open-source Python library cubewalkers. Based on novel measures and simulations, our results suggest that—contrary to current theory—cell processes are ordered and far from the edge of chaos.

We use numerical simulations and linear stability analysis to study active nematic droplets in the regime where the passive phase is isotropic. We show that activity leads to the emergence of nematic order and of spontaneous rotation in both two and three dimensions. In two dimensions the rotation is caused by the formation of a chiral $+1$ defect at the center of the drop. With increasing activity, the droplet deforms to an ellipse and then to a rotating annulus. Growing droplets form extended active arms which loop around to produce holes. In three dimensions the rotation is due to a disclination which loops away from and back to the surface, defining the rotation axis. In the bulk the disclination loop ends at a skyrmion. Active extensile flows deform the droplet to an oblate ellipsoid and contractile flows elongate it along the rotation axis. We compare our results on rotation in two-dimensional droplets with experiments on microtubule and motor protein suspensions and find a critical radius approximately equal to $700\mu \text{m}$, above which the spontaneous rotation gives way to active turbulence. Comparing the simulation parameters with experiments on epithelial cell colonies shows that the crossover radius for cell colonies could be as large as $2\text{mm}$, in agreement with experiments.

The initiation of directional cell motion requires symmetry breaking that can happen with or without external stimuli. During cell crawling, forces generated by the cytoskeleton and their transmission through mechanosensitive adhesions to the extracellular substrate play a crucial role. In a recently proposed one-dimensional model [P. Sens, Proc. Natl. Acad. Sci. USA 117, 24670 (2020)], a mechanical feedback loop between force-sensitive adhesions and cell tension was shown to be sufficient to explain spontaneous symmetry breaking and multiple motility patterns through stick-slip dynamics, without the need to account for signaling networks or active polar gels. We extend this model to two dimensions to study the interplay between cell shape and mechanics during crawling. Through a local force balance along a deformable boundary, we show that the membrane tension coupled with shape change can regulate the spatiotemporal evolution of the stochastic binding of mechanosensitive adhesions. Linear stability analysis identifies the unstable parameter regimes where spontaneous symmetry breaking can take place. Using simulations to solve the fully coupled nonlinear system of equations, we show that, starting from a randomly perturbed circular shape, this instability can lead to keratocyte-like shapes. Simulations predict that different adhesion kinetics and membrane tension can result in different cell motility modes including gliding, zigzag, rotating, and sometimes chaotic movements. Thus, using a minimal model of cell motility, we identify that the interplay between adhesions and tension can select emergent motility modes.

Cell neighbor exchanges play a critical role in regulating tissue fluidity during epithelial morphogenesis and repair. In vivo, these neighbor exchanges are often hindered by the formation of transiently stable fourfold vertices, which can develop into complex multicellular rosettes where five or more cell junctions meet. Despite their importance, the mechanical origins of multicellular rosettes have remained elusive, and current cellular models lack the ability to explain their formation and maintenance. Here we present a dynamic vertex model of epithelial tissues with strain-dependent tension remodeling and mechanical memory dissipation. We show that an increase in cell junction tension upon contraction and reduction in tension upon extension can stabilize higher-order vertices, temporarily stalling cell rearrangements. On the other hand, inducing mechanical memory dissipation via relaxation of junction strain and stress promotes the resolution of higher-order vertices, facilitating cell neighbor exchanges. We demonstrate that by tuning the rates of tension remodeling and mechanical memory dissipation, we can control topological transitions and tissue material properties, recapitulating complex cellular topologies seen in developing organisms.

Information of relevance to the organism often is represented by the concentrations of particular molecules inside a cell. As outside observers we can now measure these concentrations precisely, but the cell's own mechanisms must be noisier. We show that, in certain limits, there is a universal tradeoff between the information capacity of these concentration measurements and the amount of relevant information that is captured, a version of the information bottleneck problem. This universal tradeoff is confirmed, quantitatively, in an analysis of the positional information encoded by the “gap genes” in the developing fly embryo.

Several physical mechanisms have been proposed to explain allostery in proteins. They differ by the number of internal states that they assume a protein to occupy, leaving open the question of what controls the emergence of these distinct physical forms of allostery. Here, we analyze a simplified model of protein allostery under a range of physical and evolutionary constraints. We find that a continuum of mechanisms between two archetypes emerges through evolution. In one limit, a single-state mechanism exists where ligand binding induces a displacement along a single normal mode, and in the other limit, a multi-state mechanism exists where ligand binding induces a switch across an energy barrier to a different stable state. Importantly, whenever the two mechanisms are possible, the multi-state mechanism confers a stronger allosteric effect and thus a selective advantage. This work defines the essential constraints that distinguish single- and multi-state allostery and sets the stage for a physical theory of its evolutionary origins.

We explore the application of a theory of semantic information to the well-motivated problem of resource foraging. Semantic information is defined as the subset of correlations, which is here measured via the transfer entropy, between agent $A$ and environment $E$ that is necessary for the agent to maintain its viability $V$. Viability, in turn, is endogenously defined as opposed to the use of exogenous quantities like utility functions. In our model, the forager's movements are determined by its ability to measure, via a sensor, the presence of an individual unit of resource, while the viability function is its expected lifetime. Through “interventions”—scrambling the correlations between agent and environment by noising the sensor—we demonstrate the presence of a critical value of the noise parameter, ${\eta }_c$, above which the forager's expected lifetime is dramatically reduced. On the other hand, for $\eta <{\eta }_c$ there is little to no effect on its ability to survive. We refer to this boundary as the semantic threshold, quantifying the subset of agent-environment correlations that the agent actually needs to maintain its desired state of staying alive. Each bit of information affects the agent's ability to persist both above and below the semantic threshold. Modeling the viability curve and its semantic threshold via forager and/or environment parameters, we show how the correlations are instantiated. Our work demonstrates the successful application of semantic information to a well-known agent-based model of biological and ecological interest. Additionally, we demonstrate that the concept of semantic thresholds may prove useful for understanding the role information plays in allowing systems to become autonomous agents.

Motility is a fundamental feature of living matter, encompassing single cells and collective behavior. Such living systems are characterized by nonconservativity of energy and a large diversity of spatiotemporal patterns. Thus, fundamental physical principles to formulate their behavior are not yet fully understood. This study explores a violation of Newton's third law in motile active agents, by considering non-reciprocal mechanical interactions known as odd elasticity. By extending the description of odd elasticity to a nonlinear regime, we present a general framework for the swimming dynamics of active elastic materials in low-Reynolds-number fluids, such as wavelike patterns observed in eukaryotic cilia and flagella. We investigate the nonlocal interactions within a swimmer using generalized material elasticity and apply these concepts to biological flagellar motion. Through simple solvable models and the analysis of Chlamydomonas flagella waveforms and experimental data for human sperm, we demonstrate the wide applicability of a nonlocal and non-reciprocal description of internal interactions within living materials in viscous fluids, offering a unified framework for active and living matter physics.

Paratrechina longicornis ants are known for their ability to cooperatively transport large food items. Previous studies have focused on the behavioral rules of individual ants and explained the efficient coordination using the coupled-carrier model. In contrast to this microscopic description, we instead treat the transported object as a single self-propelled particle characterized by its velocity magnitude and angle. We experimentally observe P. longicornis ants cooperatively transporting loads of varying radii. By analyzing the statistical features of the load's movement, we show that its salient properties are well captured by a set of Langevin equations describing a self-propelled particle. We relate the parameters of our macroscopic model to microscopic properties of the system. While the autocorrelation time of the velocity direction increases with group size, the autocorrelation time of the speed has a maximum at an intermediate group size. This corresponds to the critical slowdown close to the phase transition identified in the coupled-carrier model. Our findings illustrate that a self-propelled particle model can effectively characterize a system of interacting individuals.

Many motile bacteria, such as Escherichia coli, swim by rotating multiple flagella. These semiflexible helical filaments are independently actuated by flagellar motors randomly distributed on the surface of the cell body. When all the motors rotate in the same direction, within a fraction of a second, this complex elastohydrodynamic system transforms into a straight swimmer in which all the flagella form a tight bundle propelling the cell forward. Underlying this bundling phenomenon, there are several physical factors, most of which have been analyzed in isolation using theoretical or macroscopic models. Here we report a direct study of bundling dynamics in bacterial cells whose flagellar motors can be switched on and off by light, while fluorescently labeled flagella are observed. Using optical tweezers and microfabrication to constrain cell body kinematics, we found that although translations are not essential for bundling, wobbling plays an important role in achieving a stable configuration of the bundle and body complex. We find that the curved shape of the hook, a flexible joint that transmits motor torque to flagella, strongly affects the vectorial nature of the exchanged torques and must be taken into account to correctly reproduce experimental observations. Finally, we show that once the flagella are aligned, further tightening of the bundle does not lead to an increase in the swimming speed.

Safe experimentation addressing the spread of epidemic disease requires extreme care. Risks can be avoided through the development of model systems where analogs of epidemic processes can be manipulated in the laboratory and compared with predictions from mathematical models. Here we describe an integrated mathematical and experimental analysis of a vector-borne disease. The experimental system we developed, “malaria in a Petri dish,” requires alternating host bacteria to propagate a dimorphic virus. Unlike typical vector-borne diseases, this model system uses only nonhazardous and inexpensive materials. We quantified the growth properties of the hosts and viral forms and examined the spread of the virus between independent bacterial microcolonies in an immobilized suspension. The combined model and experiment found the spatial constraints imposed by alternating microcolony infections strongly inhibited the spread of the disease. Our mathematical model allowed us to mimic the propagation of the infections and predicted how propagation increased when the spatial heterogeneity was decreased. The model also correctly predicted the efficiency of herd immunity when immunizing a fraction of the bacterial population.

Cell lineage statistics is a powerful tool for inferring cellular parameters, such as division rate, death rate, or population growth rate. Yet, in practice such an analysis suffers from a basic problem: how should we treat incomplete lineages that do not survive until the end of the experiment? Here, we develop a model-independent theoretical framework to address this issue. We show how to quantify fitness landscape, survivor bias, and selection for arbitrary cell traits from cell lineage statistics in the presence of death, and we test this method using an experimental data set in which a cell population is exposed to a drug that kills a large fraction of the population. This analysis reveals that failing to properly account for dead lineages can lead to misleading fitness estimations. For simple trait dynamics, we prove and illustrate numerically that the fitness landscape and the survivor bias can in addition be used for the nonparametric estimation of the division and death rates, using only lineage histories. Our framework provides universal bounds on the population growth rate, and a fluctuation-response relation that quantifies the change in population growth rate due to the variability in death rate. Further, in the context of cell size control, we obtain generalizations of Powell's relation that link the distributions of generation times with the population growth rate, and we show that the survivor bias can sometimes conceal the adder property, namely the constant increment of volume between birth and division.

Nucleosomes and their modifications often facilitate gene regulation in eukaryotes. Certain genomic regions may obtain alternate epigenetic states through enzymatic reactions forming positive feedback between nucleosome states. This opens the possibility of a runaway process where the whole genome becomes uniformly modified. How a system of nucleosome states maintains confinement is an open question. Here we explore a family of stochastic dynamic models with combinations of read-write enzymes. We find that a larger number of intermediate nucleosome states increases the robustness of linear spreading in models with only local positive feedback reactions and the degree of bistability under conditions with at least one nonlocal feedback reaction. Further, supplementing the positive feedback with one negative feedback acting over long distances along the genome enables confinement of epigenetic, bistable regions. Our study emphasizes the importance of determining whether each particular read-write enzyme acts only locally or between distant nucleosomes.

Microtubules are key structural elements of living cells that are crucial for cell division, intracellular transport, and motility. Recent experiments have shown that microtubule-severing proteins and molecular motors stimulate the direct and localized incorporation of free tubulin into the shaft. However, a mechanistic picture of how microtubule-associated proteins affect the lattice is completely missing. Here we theoretically explore a potential mechanism of lattice turnover stimulated by processive molecular motors in which a weak transient destabilization of the lattice by the motor stepping promotes the formation of mobile vacancies. In the absence of free tubulin the defect rapidly propagates, leading to a complete fracture. In the presence of free tubulin, the motor walk induces a vacancy drift in the direction opposite of the motor walk. The drift is accompanied by the direct and localized incorporation of free tubulin along the trajectory of the vacancy. Our results are consistent with experiments and strongly suggest that a weak lattice-motor interaction is responsible for an augmented microtubule shaft plasticity.