Journal of The Royal Society Interface最新文献

By linking genetic sequences to phenotypic traits, genotype-phenotype maps represent a key layer in biological organization. Their structure modulates the effects of genetic mutations which can contribute to shaping evolutionary outcomes. Recent work based on algorithmic information theory introduced an upper bound on the likelihood of a random genetic mutation causing a transition between two phenotypes, using only the conditional complexity between them. Here we evaluate how well this bound works for a range of genotype-phenotype maps, including a differential equation model for circadian rhythm, a matrix-multiplication model of gene regulatory networks, a developmental model of tooth morphologies for ringed seals, a polyomino-tile shape model of biological self-assembly, and the hydrophobic/polar (HP) lattice protein model. By assessing three levels of predictive performance, we find that the bound provides meaningful estimates of phenotype transition probabilities across these complex systems. These results suggest that transition probabilities can be predicted to some degree directly from the phenotypes themselves, without needing detailed knowledge of the underlying genotype-phenotype map.

Living systems have evolved cognitive complexity to reduce environmental uncertainty, enabling them to predict and prepare for future conditions. Anticipation, distinct from simple prediction, involves active adaptation before an event occurs and is a key feature of both neural and aneural biological agents. Building on the moving average convergence-divergence principle from financial trend analysis, we propose an implementation of anticipation through synthetic biology by designing and evaluating experimentally testable minimal genetic circuits capable of anticipating environmental trends. Through deterministic and stochastic analyses, we demonstrate that these motifs achieve robust anticipatory responses under a wide range of conditions. Our findings suggest that simple genetic circuits could be naturally exploited by cells to prepare for future events, providing a foundation for engineering predictive biological systems.

Fractures of the distal limb in Thoroughbred racehorses primarily occur because of accumulation of bone microdamage from high-intensity training. Mathematical models of subchondral bone adaptation of the third metacarpal lateral condyles are capable of approximating existing data for Thoroughbred racehorses in training or at rest. To improve upon previous models, we added a dynamic resorption rate and microdamage accumulation and repair processes. Our ordinary differential equation model simulates the coupled processes of bone adaptation and microdamage accumulation, and is calibrated to data on racehorses in training and rest. Sensitivity analyses of our model suggest that joint loads and distances covered per day are among the most significant parameters for predicting microdamage accumulated during training. We also use the model to compare the impact of incrementally increasing training programmes as horses enter training from a period of rest, and maintenance workloads of horses that are race fit on bone adaptation. We find that high-speed training accounts for the majority of damage to the bone. Furthermore, for horses in race training, the estimated rates of bone repair are unable to offset the rate of damage accumulation under a typical Australian racing campaign, highlighting the need for regular rest from training.

The Beckman Institute for Advanced Science of Technology at the University of Illinois Urbana-Champaign was established in 1989 with the generous support of the Arnold and Mabel Beckman Foundation. It was built to break through disciplinary boundaries and produce scientific discoveries that could only be made by teams using interdisciplinary approaches. After 36 years, I reflect on the transformative legacy of the Beckman Institute at Illinois and how it informs my perspective on future of interdisciplinary research.

Metagenomic data has significantly advanced microbiome research by employing ecological models, particularly in personalized medicine. The generalized Lotka-Volterra (gLV) model is commonly used to understand microbial interactions and predict ecosystem dynamics. However, gLV models often fail to capture complex interactions, especially when data are limited or noisy. This study critically assesses the effectiveness of gLV and similar models using Bayesian inference and a model reduction method based on information theory. We found that ecological data often leads to non-interpretability and overfitting due to limited information, noisy data and parameter sloppiness. Our results highlight the need for simpler models that align with the available data and propose a distribution-based approach to better capture ecosystem diversity, stability and competition. These findings challenge current bottom-up ecological modelling practices and aim to shift the focus towards a statistical mechanics view of ecology based on distributions of parameters.

Radar technology has become a powerful tool for studying animal aeroecology in the lower atmosphere, particularly bird migration across large spatio-temporal scales. Quantifying bird density from radar data requires radar cross-section (RCS) estimates. Although RCS data exist for small bird species, large birds, especially soaring and flocking species, remain poorly characterized, partly due to technical challenges in measuring and modelling their complex morphology. This study introduces a practical and accessible modelling framework for estimating species-specific RCS in large birds, using the T-matrix electromagnetic scattering method, based on geometrically simplified representations of bird morphology. We computed spherical RCS values for 11 large bird species across C- and S-band radar wavelengths and compared them with both spherical and prolate spheroidal RCS estimates obtained using the WIPL-D software, a method previously validated for biological targets. As expected, the spherical simplification led to a systematic overestimation of RCS relative to a more anatomically representative model. However, the bias was consistent and can be corrected using regression-derived scaling factors. This approach addresses the critical lack of empirical RCS data for large birds, offering an alternative that can be implemented in open-source platforms. It can be integrated with automated detection tools to enhance our understanding of migration patterns in understudied bird groups and to support efforts to mitigate bird-aircraft collisions.

The ribosome flow model (RFM) is a phenomenological model for the unidirectional flow of particles along a one-dimensional chain of [Formula: see text] sites. The RFM has been extensively used to study the dynamics of ribosome flow along a single-mRNA molecule during translation. In this case, the particles model ribosomes and each site corresponds to a consecutive group of codons. Networks of interconnected RFMs have been used to model and analyse large-scale translation in the cell and, in particular, the effects of competition for shared resources. Here, we analyse the RFM with a negative feedback connection from the protein production rate to the initiation rate. This model is based on, for example, the production of proteins that inhibit the translation of their own mRNA. The RFM with negative feedback is a 2-cooperative dynamic system, i.e. its flow maps the set of vectors with up to one-sign variation to itself. Using tools from the theory of 2-cooperative dynamical systems, we provide a simple condition guaranteeing that the closed-loop system admits at least one non-trivial periodic solution. When this condition holds, we also explicitly characterize a large set of initial conditions such that any solution emanating from this set converges to a non-trivial periodic solution. Such a solution corresponds to a periodic pattern of ribosome densities along the mRNA and to a periodic pattern of protein production.

Rays and skates tend to have different fin kinematics depending on their proximity to a ground plane such as the sea-floor. Near the ground, rays tend to be more undulatory (high wavenumber), while far from the ground, rays tend to be more oscillatory (low wavenumber). It is unknown whether these differences are driven by hydrodynamics or other biological pressures. Here, we show that near the ground, the time-averaged lift on a ray-like fin is highly dependent on wavenumber. We support our claims using a ray-inspired robotic rig that can produce oscillatory and undulatory motions on the same fin. Potential flow simulations reveal that lift is always negative because quasi-steady forces overcome wake-induced forces. Three-dimensional flow measurements demonstrate that oscillatory wakes are more disrupted by the ground than undulatory wakes. All these effects lead to a suction force towards the ground that is stronger and more destabilizing for oscillatory fins than undulatory fins. Our results suggest that wavenumber plays a role in the near-ground dynamics of ray-like fins, particularly in terms of dorsoventral accelerations. The fact that lower wavenumber is linked with stronger suction forces offers a new way to interpret the depth-dependent kinematics of rays and ray-inspired robots.

We maintain balance during gait using both proactive and reactive control strategies. Damage to the brain from a stroke impairs reactive balance, but little is known about how a stroke impacts proactive control during walking. Stroke-related impairments to proactive control could become targets for interventions designed to improve responses to predictable disturbances and reduce fall risk. Therefore, we determined whether proactive strategies during predictable treadmill accelerations differed between people post-stroke (n = 14) and people without stroke (n = 14). Both groups walked with accelerations at random (every one to five strides) and regular (every three strides) intervals. We quantified the effects of the perturbations as changes to the centre of mass (COM) speed and used mechanical leg work to quantify the proactive strategies to slow the COM. Participants without stroke reduced peak COM speed better than those with stroke when perturbations were regular (-0.016 m s^-1 versus +0.004 m s^-1; p = 0.007). They also reduced positive leg work more during the perturbation step than the group post-stroke (-5.7% versus +2.5%; p = 0.003). One implication of these findings is that people post-stroke may be more susceptible to falls during predictable gait disturbances, and future work should identify the underlying impairments that cause these deficits.

Chaos is omnipresent in nature, and its understanding provides enormous social and economic benefits. However, the unpredictability of chaotic systems is a textbook concept due to their sensitivity to initial conditions, aperiodic behaviour, fractal dimensions, nonlinearity and strange attractors. In this work, we introduce, for the first time, chaotic learning, a novel multiscale topological paradigm that enables accurate predictions from chaotic systems. We show that seemingly random and unpredictable chaotic dynamics counterintuitively offer unprecedented quantitative predictions. Specifically, we devise multiscale topological Laplacians to embed real-world data into a family of interactive chaotic dynamical systems, modulate their dynamical behaviours and enable the accurate prediction of the input data. As a proof of concept, we consider 28 datasets from four categories of realistic problems: 10 brain waves, four benchmark protein datasets, 13 single-cell RNA sequencing datasets and an image dataset, as well as two distinct chaotic dynamical systems, namely the Lorenz and Rossler attractors. We demonstrate chaotic learning predictions of the physical properties from chaos. Our new chaotic learning paradigm profoundly changes the textbook perception of chaos and bridges topology, chaos and learning for the first time.