Journal of the Royal Society Interface

The environments inhabited by flying insects demand a balance between flight efficiency and flight manoeuvrability. In structural oscillators such as the insect indirect flight motor, efficiency (arising from resonance) and manoeuvrability (arising from kinematic modulation) are typically quid pro quo, with modulation incurring penalties to efficiency. Band-type resonance is a phenomenon that offers, in theory, a strategy to lessen these penalties via careful navigation through a band of efficient kinematic states. However, identifying this band is challenging: no methods exist to identify the complete band in realistic motor models, involving elasticity distributed across thorax and wing. Nor are the effects of elasticity distribution on the band known. In this work, we address both open topics. We present a suite of numerical methods for identifying the complete resonance band in general systems. Applying them to models of the insect flight motor with distributed elasticity--thoracic and wing flexion--reveals that distributed elasticity is moderate-risk but high-reward morphological feature. Well-tuned distributions expand the resonance band over fourfold whereas poorly-tuned distributions completely extinguish the resonance band. These results indicate that distributing elasticity across the insect flight motor can have adaptive value, and motivate broader work identifying distributions across species.

Successful development of multicellular organisms requires cells to transition between states with precise timing. Distinct cell states are often understood as being maintained by stabilizing regulatory networks, such that a complete cell-state transition requires network rewiring through partial dismantling of the current state and concurrent reconfiguration into a new one. Empirically, these transitions are often investigated by quantifying the gain or loss of expression of a small number of state-specific markers, frequently a single proxy. A general quantitative framework for comparing the kinetics of such transitions across experimental conditions is lacking. Here, we show that the delayed Weibull distribution provides a natural description of cell-state transition kinetics when transition is viewed as the cumulative consequence of many molecular events, whose timing may vary between cells and conditions, analogous to system failure in reliability theory. This formulation yields a compact model with interpretable parameters describing the delay before transition onset, the characteristic timescale of transition, the temporal form of the transition hazard, and the fraction of cells competent to respond. Together, this framework provides a practical and interpretable approach for quantifying the kinetics of cell-state transitions and how they are altered by perturbation.

Change point detection is critical for identifying structural transitions in time series data. While most existing methods focus on changes in statistical properties of the data such as the mean or variance, many real-world systems are governed by dynamical models in which changes occur in model parameters. We introduce MICA, an algorithm that detects change points by minimizing the discrepancy between model simulations with a given dynamical model and observed data. The method integrates binary segmentation with a genetic algorithm to identify both the timing and nature of model parameter changes. MICA simultaneously estimates segment-specific and global parameters alongside change points, offering enhanced flexibility and interpretability. We demonstrate its utility on synthetic datasets and real-world scenarios, including COVID-19 epidemiological modeling, under policy interventions, and the analysis of generator cooling systems in wind turbines to monitor operational status. While illustrated using differential and difference equation models, MICA is model-agnostic and applicable to any simulatable system, making it broadly useful for applications requiring accurate tracking of structural dynamics.

With the development of microfluidics, it has now become possible to assess the susceptibility of bacteria to antibiotics at the single-cell level instead of relying on population measurements. Such studies are particularly relevant when the growth of bacterial population in the presence of antibiotics is heterogeneous. Here, we build a model to describe such a case, and apply it to experimental measurements on a small population of E. Coli exposed to ciprofloxacin, a drug which is well known for triggering a bistable response.

Time-resolved measurements are central to calibrating mechanistic dynamical models, but current inference frameworks typically assume that reported measurement times are exact. In practice, actual sampling times may deviate from reported times because of sample-handling delays, imper-fect synchronization, or reporting errors. Here, we present a Bayesian framework for parameter inference in ordinary differential equation models that explicitly accounts for uncertainty in measurement times. We formulate latent measurement times as random variables and derive a joint and marginalized posterior. To compute the marginal likelihood efficiently, we augment the original dynamical system with additional state variables that evaluate the required integrals during numerical simulation. This reduces the dimensionality of the estimation problems and allows for efficient and reliable Markov chain Monte Carlo sampling. Across synthetic examples and a published model of carotenoid cleavage in Arabidopsis thaliana, neglecting time uncertainty led to biased estimates and overconfident uncertainty quantification, whereas the proposed marginalized formulation recovered reliable parameter estimates while substantially improving sampling efficiency and scalability. These results identify measurement time uncertainty as an important source of variability in dynamic modeling and establish posterior marginalization as a practical strategy for robust mechanistic inference.

Bacterial communities grow as dynamic populations that respond to their environments. A clinically relevant example is the inactivation of beta-lactam antibiotics by intracellular beta-lactamase in E. faecalis resistant strains. In these populations, resistant bacteria act as antibiotic sinks, detoxifying the environment and allowing sensitive bacteria to survive treatment through a cooperative interaction. In this work, we study strongly coupled planktonic and biofilm populations of mixed sensitive-resistant E. faecalis bacteria under antibiotic stress using fluorescent microscopy. The presence of resistant bacteria in the system benefits both resistant and sensitive cells, leading to mixed planktonic and biofilm populations at super-inhibitory drug concentrations. We show that a beta-lactam antibiotic with or without the addition of a beta-lactam inhibitor can lead to a population inversion effect, characterized by a non-monotonic relation between initial and final fractions of resistant bacteria. The effect is observed in both the planktonic and biofilm populations and is modulated by the total initial cell density. A well-mixed model with competition mediated by resource sharing and cooperation from global degradation of toxins predicts the experimentally observed behavior. These observations suggest underlying population-level mechanisms that are largely independent of biofilm spatial structure.

Nucleus shape is a sensitive indicator of cell state, influenced by numerous bio-chemical and physiological factors. While prior work has cataloged how perturbations alter nucleus morphology, we address the inverse: inferring underlying molecular changes from nucleus shape alone. We previously developed a mechanical model yielding two nondimensional parameters: flatness index and scale factor, which are surrogate measures for cortical actin tension and nuclear envelope compliance respectively. In this study, we apply these parameters to investigate the dynamics in cellular mechanics during confined migration. We fabricated polydimethylsiloxane (PDMS) microchannels with widths of 3 {micro}m (high confinement) and 10 {micro}m (low confinement) and tracked cells migrating through them. We captured high-frequency 3D nucleus shapes via double fluorescence exclusion microscopy and custom image analysis. Fitting the model and estimating flatness index and scale factor to time-resolved shapes revealed dynamic regulation in 3 {micro}m channels: actin tension decreased and nucleus compliance increased immediately before nucleus entry into the constriction, with rapid restoration to baseline upon exit. No such changes occurred in 10 {micro}m channels, indicating active, confinement-dependent cytoskeletal adaptation. Immunostaining for YAP and lamin-A,C confirmed these model inferences. Our results uncover mechanostasis, active mechanical homeostasis, during confined migration and establish the combination of double fluorescence exclusion microscopy and nondimensional nucleus shape parameters as a powerful, non-invasive tool for single-cell mechanobiology studies.

The basic reproduction number R0 is central to malaria epidemiology, yet it is typically treated as a static quantity derived under memoryless assumptions for mosquito demography. In natural systems, however, mosquito populations are shaped by delayed processes such as larval development and density-dependent feedback, introducing biological memory into vector dynamics. We develop a minimal delay-based framework that incorporates this memory into the Ross-Macdonald model by describing adult mosquito abundance with a retarded differential equation. This formulation induces a time-dependent transmission potential R0(t). Using complex analysis and the argument principle, we derive an explicit stability threshold [Formula], which separates stable from oscillatory transmission regimes. Near this threshold, delayed feedback produces slow relaxation times and sustained transient oscillations, implying that transmission potential may vary intrinsically even in the absence of external forcing. To account for ecological variability, we extend this deterministic condition into a probabilistic framework and define the stability probability as [Formula]. Numerical simulations and global sensitivity analysis show that recruitment and developmental delays are the primary drivers of instability, while adult mortality has a weaker stabilizing effect. These results indicate that malaria interventions may influence not only the magnitude of malaria transmission but also its dynamical stability. By linking delay dynamics, transmission theory, and uncertainty quantification, this framework provides a basis for stability-aware modeling and interpretation of malaria transmission under ecological variability. Author summaryMalaria transmission is often summarized by a single number, R0, treated as a fixed indicator of whether transmission will increase or decline. This assumes mosquito populations respond instantly to environmental conditions. In reality, mosquitoes develop through stages where larval conditions, such as crowding, nutrition, or temperature, affect adult populations only after a delay. This creates biological memory: todays mosquitoes reflect past environments. We show that this memory can fundamentally reshape transmission dynamics. When developmental delays are included, transmission potential is no longer constant but can fluctuate over time, even in stable environments. These fluctuations can persist or amplify depending on the balance between mosquito growth, mortality, and delay. As a result, variability in mosquito abundance or malaria transmission may arise from intrinsic dynamics rather than external drivers alone. Under ecological variability, stability becomes probabilistic, allowing estimation of how likely transmission is to remain stable. Interventions that reduce larval productivity or increase adult mortality may therefore both lower transmission and make it more predictable, improving interpretation and control strategies.

Understanding how cells migrate through confined environments is crucial for elucidating fundamental biological processes, including cancer invasion, immune surveillance, and tissue morphogenesis. The nucleus, as the largest and stiffest cellular organelle, often limits cellular deformability, making it a key factor in migration through narrow pores or highly constrained spaces. In this work, we introduce a geometric surface partial differential equation (GS-PDE) model in which the cell plasma membrane and nuclear envelope are described as evolving energetic closed surfaces governed by force-balance equations. We replicate the results of a biophysical experiment, in which a microfluidic device is used to impose compressive stresses on cells by driving them through narrow microchannels under a controlled pressure gradient. The model is validated by reproducing cell entry into the microchannels. A parametric sensitivity analysis highlights the dominant influence of specific parameters, whose accurate estimation is essential to faithfully capture the experimental setup. We found that surface tension and confinement geometry emerge as key determinants of translocation efficiency. Although tailored to this specific setup for validation purposes, the framework is sufficiently general to be applied to a broad range of cell mechanics scenarios, providing a robust and flexible tool for investigating the interplay between cell mechanics and confinement. It also offers a solid foundation for future extensions integrating more complex biochemical processes such as active confined migration. Author summaryCells often migrate through very narrow spaces in tissues, a process critical for cancer invasion, immune surveillance, and tissue development. In particular, the stiffness of the nucleus, the largest and most rigid organelle, can limit migration through tight pores. In this study, we present a mathematical model describing the motion of a cell and its nucleus through a microchannel during cell translocation, using a geometric formulation based on surface partial differential equations. The model is general and applicable to a variety of scenarios involving confined cell transport. The model is validated by reproducing key experiments on cell translocation through narrow microchannels. The framework incorporates essential surface features, including mechanical responses, bending rigidity, and surface tension. Sensitivity analysis highlights surface tension and channel geometry as the parameters that most strongly influence translocation. Overall, the model provides new insights into the mechanics of confined cell transport, grants access to cellular quantities that are difficult to measure experimentally, such as cell and nucleus areas, perimeters, and stresses, and establishes a foundation for future extensions incorporating more complex biochemical processes.

AO_SCPLOWBSTRACTC_SCPLOWOncolytic viruses provide cancer therapy using replication-competent viruses that selectively infect and lyse tumour cells. Their tumour-specific replication also enables the delivery of targeted, virus-encoded gene products, such as enzymes that activate prodrugs. This dual functionality offers the potential for synergistic effects by combining direct oncolysis with localised drug activation. The interplay between infection, replication, lysis, and gene product delivery remains poorly understood. Here, we introduce a spatially structured, multi-patch model of cancer cells infected by an oncolytic virus engineered to deliver a prodrug-activating enzyme. The spatial system is first represented as a microscopic model and subsequently reduced via spectral dimension reduction techniques. This reduction yields an ordinary differential equation model for a set of coarse-grained variables, which we analyze both without the transgene (OV model) and with the transgene (TOV model). For each case, we compute the basic reproduction number, R0, which determines the conditions for viral spread. Both models exhibit three regimes via transcritical bifurcations: (i) R0 < 0, extinction of both cancer and infected cells; (ii) 0 < R0 [&le;] 1, persistence of cancer cells only; and (iii) R0 > 1, coexistence as a stable node or as a focus. The TOV model, as a difference form the OV model, can undergo periodic oscillations arising from a Hopf-Andronov bifurcation. Notably, oscillation amplitudes can be controlled such that cancer cells largely decrease when drug-induced death is stronger in non-infected cells than in infected ones, enabling effective cancer cells killing while maintaining viral replication and prodrug activation. The qualitative behaviour of the coarse-grained model is shown to be preserved in both the microscopic and spatially explicit models.

Mitochondria often form branching membrane networks distributed throughout the cell interior. In many, though not all, cell types, these networks are observed to consist of one large connected component together with many smaller fragments. Why does this pattern arise? Does it reflect a specific biological function, an external biophysical constraint, or something simpler? Using results from extremal graph theory, we prove a new theorem which suggests that, under a sufficiently broad sampling of the space of mitochondria-like graphs, the predominance of three-way junctions makes the appearance of a large component likely. This suggests that, in some settings, a large component may serve as a useful null model for mitochondrial network structure rather than requiring a dedicated explanation. More broadly, our result points towards testable predictions, since systematic deviations from this baseline may help reveal additional constraints or mechanisms shaping mitochondrial morphology.

Oscillatory behaviour is important in multiple biological contexts. However, the inherent nonlinearity and high dimensionality of mathematical models in biology makes proving the existence and the localization of limit cycle oscillations challenging. Here, we provided an elementary proof for the existence and a method for rigorously localizing the oscillatory solutions in a class of benchmark biomolecular oscillators. To construct the proof, we used a geometric approach based on Brouwers Fixed Point theorem. We constructed a toroidal-like manifold within a positively invariant set by removing the hypervolume containing the fixed point and the trajectories converging to it along its stable manifold. We showed that the vector field describing the system dynamics maps a cross section of the toroidal-like manifold onto itself. The existence of a limit cycle solution in this manifold was guaranteed by Brouwers Fixed Point theorem. For different sets of initial conditions in these cross-sections, we used an interval-based Reachability Analysis to localize the oscillatory behaviour that complements the Brouwers Fixed Point theorem approach. These results add a simple and elegant approach to demonstrating the existence of limit cycles in biomolecular systems as well as a method for rigorous localization of the region of existence.

Achieving complex multi-species control objectives is essential for engineering advanced autoregulated biomolecular devices. This paper addresses the problem of robust steady-state tracking for outputs defined as multiplicative combinations of biomolecular species concentrations. We first introduce a control architecture realized via chemical reaction networks that steers the product of two target species concentrations in the controlled network to a prescribed value. A robust stability analysis is provided for closed-loop system families with distinct structural characteristics. The proposed framework is also extended to a more general formulation capable of regulating arbitrary monomial outputs involving multiple species. Numerical simulations of representative examples corroborate the theoretical results and illustrate the effectiveness of our approach.

Recurrence Quantification Analysis (RQA) is a powerful phenomenological method for characterizing dynamical systems from sequential empirical data, but it is fundamentally limited to continuous signals. Symbolic RQA (sRQA) extends this framework to discrete state sequences, enabling the analysis of both inherently discrete systems and continuous systems where state-based dynamics and motifs are of interest. Despite its promise, accessible and unified software support for sRQA has remained limited. Here we introduce the sRQA package, an open-source R library that consolidates discretization and symbolization, data visualization, and computation of recurrence and cross-recurrence metrics into a single accessible toolset. We validated the method using simulated data with known dynamical properties, confirming that sRQA metrics behaved as theoretically expected. We then demonstrated the utility of sRQA across three real-world applications. First, we applied sRQA to ECG recordings, showing that symbolic recurrence metrics reliably distinguished atrial fibrillation from normal sinus rhythm, with an XGBoost classifier achieving 92% accuracy and an AUC of 0.97. Second, we applied sRQA to fMRI BOLD time series from the dorsal attention network, finding that symbolic and cross-recurrence metrics differentiated movie-viewing from resting-state conditions, revealing greater regularity and inter-subnetwork coordination during task engagement. Third, we applied sRQA to intrinsically symbolized sequences of pauses in speech, identifying valence-specific differences in pause dynamics between truthful and deceptive statements, as well as sex differences in pause structure during negatively-valenced speech. Together, these results demonstrate that sRQA provides a flexible and sensitive framework for characterizing discrete and discretized dynamical systems across biological and behavioral domains. AUTHOR SUMMARYMany biological and behavioral systems are best understood as sequences of discrete states rather than smooth, continuous processes. For example, a heartbeat that shifts between rhythms, a brain that transitions between activity patterns, or a speaker who pauses and resumes in ways that carry meaning. Standard methods for analyzing the dynamics of such systems were not designed with this kind of data in mind. Here, we introduce the sRQA package, an open-source software library that makes it straightforward to apply symbolic recurrence analysis to both discrete and continuous data. We demonstrate the library across four examples: simulated data with known properties, cardiac recordings distinguishing atrial fibrillation from normal heart rhythm, brain imaging data capturing differences between rest and task engagement, and speech recordings where pause patterns differ between truthful and deceptive statements. In each case, sRQA revealed meaningful structure in the data that would be difficult to detect with conventional tools. We hope this library will make symbolic recurrence analysis more accessible to researchers across the biological and behavioral sciences.

Phenotypic selection can cause the transient, selective upregulation of fitness-conferring genes in isogenic cell populations under stress, producing selective enrichment of the fitness gene relative to a neutral reference gene. While computational models have shown that such enrichment requires noisy gene expression and a cellular memory linking growth rate to gene expression (Ciechonska et al., 2022), the precise mechanistic requirements and the analytical principles governing enrichment have remained unclear. Here, we present an exact analytical framework that unifies enrichment mechanisms across both growth-driven and death-driven selection regimes. By analysing a stochastic model of explicit mRNA and protein dynamics, we prove that when selection acts via cell division, the fitness advantage of faster growth is exactly cancelled by the penalty of faster protein dilution. We show this symmetry is broken by translational feedback but not by transcriptional feedback alone; for genes with regulated (switching) promoters, the promoter-state memory provides an independent route to enrichment without translational feedback. Conversely, when selection acts via cell death, this exact cancellation is bypassed, allowing selective enrichment to emerge from baseline gene expression noise without any assumptions about growth-related feedback loops or regulated vs constitutive expression. We derive an exact fluctuation-response relation demonstrating that, in all cases, enrichment scales with the super-Poissonian component of unperturbed protein noise times the relevant memory timescale. All analytical predictions are corroborated by stochastic simulations of a finitepopulation Moran model. These results have implications for the emergence of drug resistance: by transiently enriching survival-conferring phenotypes, phenotypic selection can extend the window during which cell division occurs under stress, increasing the opportunity for permanent genetic mutations to arise.

Precise regulation of gene expression in batch bacterial cultures is challenging because the underlying dynamics vary with cellular physiological state over time. Although cell-silicon systems enable rapid, real-time optogenetic control, disturbance rejection remains difficult in batch culture because the plant dynamics shift across growth phases, limiting the effectiveness of fixed-gain controllers designed under constant-growth assumptions. Here, we present a multiscale model-guided feedback control framework for disturbance rejection in batch E. coli cultures. Frequency-response analysis shows that the input-output dynamics of gene expression depend strongly on growth phase, revealing operating-point-dependent limits on the disturbance rejection performance of a fixed-gain PID controller. To address this limitation, we develop two growth-aware control strategies: a gain-scheduled PID (PID-GS) controller that adapts to cellular physiological state, and a gain-scheduled feedback-feedforward controller (PID-GS-FF) that further compensates for growth perturbations. We also introduce a controller evaluation framework that identifies three distinct operating regimes for targeted experimental validation. Together, these results show that accounting for growth-state-dependent dynamics is necessary for robust disturbance rejection in batch culture and provide a control-oriented framework for regulating living systems with shifting operating conditions.

Human walking is intrinsically variable. For example, there is considerable stride to stride variability even when walking speed is constant. This variability is due to uncertainty in the sensorimotor system and the environment, and is shaped by both musculoskeletal dynamics (e.g. joint stiffness and damping originating from muscles) and the control strategy used to mitigate the effects of uncertainty. Yet, insight into how sensorimotor noise shapes walking variability is limited due to a lack of experimental methods to assess sensorimotor noise and control strategies during walking. Simulations that account for uncertainty can elucidate how sensorimotor noise affects movement variability but due to numerical challenges, accounting for sensorimotor noise is not common in simulations of walking. Existing simulations have hugely simplified musculoskeletal dynamics (e.g. no muscles), the control policy (e.g. pre-defined feedback loops), or sensorimotor noise sources (e.g. only motor noise). Here, we performed stochastic optimal control simulations of walking based on a model with 9 degrees of freedom and 18 muscles to study how the level of sensory and motor noise influences walking. We solved for feedforward muscle excitations and full-state time-varying feedback gains that minimised expected effort while generating periodic, and hence stable, gait patterns. To enable these simulations, we approximated the state distribution with a Gaussian and used an unscented transform to propagate the state covariance. Resulting optimisation problems were solved with direct collocation. Sensorimotor noise level had a small effect on the mean kinematics but shaped kinematic and muscle activity variability as well as expected effort. Although simulations underestimated the magnitude of experimental positional variability, they captured its structure. In agreement with experimental results, the control policy prioritised limiting variability of centre of mass kinematics and minimal swing foot clearance over limiting joint angle variability. Hence, our simulations suggest that effort minimisation underlies these observations. Author summaryWhen performing a movement multiple times, each repetition will be slightly different due to random disturbances in the neural signals used to control movement, i.e. sensorimotor noise. Because it is difficult to measure inside the nervous system of a moving person, computer simulations are used to study movement control. They found that both sensorimotor noise and musculoskeletal mechanics determine how people control arm movements and standing. However, there are no simulations of walking that systematically evaluated how sensorimotor noise level influences walking kinematics because they pose computational challenges. Here, we proposed and used an approach for minimal effort simulations of walking in the presence of uncertainty. We imposed forward speed and stability but not kinematics. We found that the level of sensorimotor noise had little effect on the mean movement but a strong effect on the variability and the expected effort. The control strategy prioritised reducing the variability of the centre of mass position and swing foot clearance over reducing the variability of individual joint angles, which is also observed in experiments. Interestingly, strict control of centre of mass position and foot clearance in our simulations emerged from minimising effort.

Understanding how biological shape and movement interact with surrounding fluids represents a fundamental challenge at the intersection of biology, physics, and engineering. Fish locomotion exemplifies this challenge: body morphology and swimming kinematics together determine the hydrodynamic forces and flow structures that enable efficient propulsion and maneuverability. Whereas biologists have long sought to connect morphological variation to swimming performance, traditional morphometric approaches provide limited insight into the fluid mechanical consequences of shape differences. Similarly, although computational fluid dynamics can reveal detailed flow physics, simulating hydrodynamics across diverse and dynamic morphologies remains prohibitively expensive for systematic investigation. To bridge this gap, we introduce a data-driven framework that connects fish body shape dynamics to hydro-dynamic performance through compact morphospace parameterization and reduced-order modeling. Using CFD simulations of 15 fish species from the Digital Life Project database (www.digitallife3d.org/3d-model), we generate hydrodynamic datasets capturing the shape-flow relationship. Principal Component Analysis (PCA) extracts four dominant shape parameters from dorsal body profiles, which are then integrated into an Inverse-Design with Dynamic Mode Decomposition (ID-DMD) framework to model the resulting fluid dynamics. The resulting modal analysis suggests that locomotion strategies emerge from specific shape-flow interactions. We further demonstrate the frameworks utility through single- and multi-objective shape optimization, showing how it enables efficient exploration of the morphology-hydrodynamics relationship. This approach offers a novel analysis and design tool for understanding how biological form and motion interact with fluid mechanics, with applications ranging from bio-inspired vehicle development to evolutionary biomechanics.

The influence of arbitrary randomness in cell division times on the variability of protein copy numbers within a lineage ensemble has been recently studied, going beyond the contributions of noisy gene expression and partitioning error. However, variability of protein concentrations need separate study, since cell size growth between cell divisions dilute protein concentrations at the same rate as size growth, which also determines mean division times. Here for a model of bursty protein production, we present exact moments (of all orders) of protein concentrations in the cyclo-stationary state, comparing: (i) population and lineage cell ensembles, and (ii) statistics at different cell ages. Two interesting results emerge. While the variance of protein concentration changes with the degree of division time heterogeneity at any cell age, the age-averaged variance is independent of it within lineage ensemble but stays dependent within population ensemble. The skewness within population ensemble is higher in younger cells than within lineage ensemble, and this behavior reverses at older ages. Such a feature vanishes for the age-averaged distribution, with population based skewness always dominating over that of lineage. We also show that mother-daughter correlations in generation times, do not add any significant difference to the results.

Collective foraging is widespread across the animal kingdom, allowing animals to more effectively discover resources. However, collective foragers need to balance a key trade off between private exploration and using social information. Social information can come in very distinct forms, ranging from simple positional cues to complex payoff information. However, how the types of available social cues and environmental volatility shape collective foraging behavior is not well understood. We address this using a spatially-explicit model in which agents track a mobile resource via multi-agent reinforcement learning. Agents choose between random exploration, private tracking, and social attraction. We systematically varied resource volatility and the type of available social cues to analyze their effect on individual and collective behavior. Our results show that the quality of social information dictates the emerging collective behavior. Low-quality social cues (e.g., positions, actions) result in a fragile strategy that is effective in stable environments but fails as volatility increases. Conversely, high-quality social information (e.g., payoffs) enables behavioral diversity: Agents selectively copy others and flexibly change between individual tracking or exploration depending on the environmental volatility. Our findings identify the interplay between information quality and ecological context as a fundamental mechanism governing the emergence of distinct forms of collective behavior from individual decision rules.