Biological Shape Research Articles

A Computational Approach to 3-Dimensional Reconstruction Image of Diabetic Foot using the Near-Infrared Spectroscopy (NIRS) after Magnetic Field Intensity Stimulation: Preliminary Study

Diabetes Mellitus (DM) or Diabetes is a disease characterized by a high sugar level in the blood. The foot is considered as a distinct entity where most of the complication arises that often leads to lower extremity amputation. Recently, the Pulse Magnetic Field (PMF) devices that use magnetic field intensity as stimulants are being used to improve blood circulation among patients with peripheral vascular diseases (PVD). This study promotes the use of Near-Infrared Spectroscopy (NIRS) devices in diagnosing and monitoring DM after the use of PMF devices by providing a piece of biomedical evidence through a simulation approach on how to reconstruct a 3-dimensional biological tissue shape models. The result showed that this simulation approach could be of use in the actual reconstruction of a diabetic foot showing the absorption coefficient. NIRS device and the biomedical images could be of use in places where radiographic imaging modalities are not accessible.

Dance and Science: an analysis of the creative process behind "Transitions", a show by Contemporary Dance Company of UFRJ

This study proposes an esthetic reflection on the influence of the biological sciences on dance as a variety of contemporary art. The relationship between science and art in this study is grounded on François Dagognet’s hyperphenomenology, particularly his representation of the major role played by the image-symbol as essential base of present-day scientific activity [1]. Contemporary dance creators and companies such as Wayne Mac Gregor, Phylobolus Dance Company and Liz Lerman Dance Exchange are analyzed, in order to highlight how some biosciences themes are explored as creative potentials in their dance performances [2,3,4]. We aim at relating biological shapes and patterns existing in the cellular and molecular universe with the fundamentals of dance described by Helenita Sá Earp. Earp’s conception posits dance as wide-scoped knowledge, including a “science of dance” where intuition works as basic foundation and its epistemology is made of an open type of knowledge named “dance parameters”, namely motion, space, shape, dynamics and time. These parameters allow establishing access points for multiple connections within the realm of dance. This approach tends to generate a kind of access to body actions stimulating interdisciplinarity between several areas of knowledge [4]. As study-case, we focus methodologically on the processes of esthetic creation involved in the production of a multimedia show entitled “Transitions” by appealing to biochemical notions, especially some features of cytology and cellular morphology, cell proliferation and processes of cellular transportation [5].

Constructing a multiple‐part morphospace using a multiblock method

Abstract Popular current methods for quantifying variation in biological shape are well‐suited to analyses of isolated parts (e.g. the same bone from the skeletons of many individuals). An analytical challenge exists for quantifying variation between the shapes of multiple‐part objects where each part has a different position, rotation or scale (e.g. partial or whole articulated skeletons). We investigated regularised consensus principal component analysis (RCPCA) as a multiblock method for quantifying variation in the shape of multiple‐part objects. Multiblock methods are routinely used in other big data research fields such as bioinformatics/medicine, marketing and food research, but have not been widely embraced for evolutionary biology research. We have created the new package morphoBlocks for the r programming language to make RCPCA more accessible for shape evolution research. morphoBlocks provides a complete workflow for formatting, analysing and visualising the variation between multiple‐part objects by integrating functions from a diverse range of other packages. In particular, global components produced by RCPCA provide a consensus space that we present here as a morphospace for multiple‐part objects. morphoBlocks is demonstrated with a case study of manually placed landmarks and automatically placed pseudolandmarks from the partial wing skeletons of 15 extant penguin species and five fossil penguin species. Our case study provides quantitative support for a historical hypothesis about the magnitude and mode of morphological change across the evolutionary history of penguins. RCPCA can be used to analyse two‐ or three‐dimensional datasets with 10s of landmarks, or 100s to 1,000s of semilandmarks or pseudolandmarks, from 10s to 100s of specimens comprised of two or more parts. We use morphoBlocks on a small three‐bone case study and provide a framework for applying this method to much larger studies investigating the ecological or evolutionary significance of multiple‐part objects.

Functional random forests for curve response

The rapid advancement of functional data in various application fields has increased the demand for advanced statistical approaches that can incorporate complex structures and nonlinear associations. In this article, we propose a novel functional random forests (FunFor) approach to model the functional data response that is densely and regularly measured, as an extension of the landmark work of Breiman, who introduced traditional random forests for a univariate response. The FunFor approach is able to predict curve responses for new observations and selects important variables from a large set of scalar predictors. The FunFor approach inherits the efficiency of the traditional random forest approach in detecting complex relationships, including nonlinear and high-order interactions. Additionally, it is a non-parametric approach without the imposition of parametric and distributional assumptions. Eight simulation settings and one real-data analysis consistently demonstrate the excellent performance of the FunFor approach in various scenarios. In particular, FunFor successfully ranks the true predictors as the most important variables, while achieving the most robust variable sections and the smallest prediction errors when comparing it with three other relevant approaches. Although motivated by a biological leaf shape data analysis, the proposed FunFor approach has great potential to be widely applied in various fields due to its minimal requirement on tuning parameters and its distribution-free and model-free nature. An R package named ’FunFor’, implementing the FunFor approach, is available at GitHub.

Glucoregulation and coping behavior after chronic stress in rats: Sex differences across the lifespan

The purpose of the current study was to determine how biological sex shapes behavioral coping and metabolic health across the lifespan after chronic stress. We hypothesized that examining chronic stress-induced behavioral and endocrine outcomes would reveal sex differences in the biological basis of susceptibility. During late adolescence, male and female Sprague-Dawley rats experienced chronic variable stress (CVS). Following completion of CVS, all rats experienced a forced swim test (FST) followed 3 days later by a fasted glucose tolerance test (GTT). The FST was used to determine coping in response to a stressor. Endocrine metabolic function was evaluated in the GTT by measuring glucose and corticosterone, the primary rodent glucocorticoid. Rats then aged to 15 months when the FST and GTT were repeated. In young rats, chronically stressed females exhibited more passive coping and corticosterone release in the FST. Additionally, chronically stressed females had elevated corticosterone and impaired glucose clearance in the GTT. Aging affected all measurements as behavioral and endocrine outcomes were sex specific. Furthermore, regression analysis between hormonal and behavioral responses identified associations depending on sex and stress. Collectively, these data indicate increased female susceptibility to the effects of chronic stress during adolescence. Further, translational investigation of coping style and glucose homeostasis may identify biomarkers for stress-related disorders.

Morphogenesis – “The Riddles of Form” in Twenty-First Century Science

Morphogenesis – “The Riddles of Form” in Twenty-First Century Science

Protocol for Retrieving Three-Dimensional Biological Shapes for a Few XFEL Single-Particle Diffraction Patterns.

X-ray free-electron laser (XFEL) scattering promises to probe single biomolecular complexes without crystallization, enabling the study of biomolecular structures under near-physiological conditions at room temperature. However, such structural determination of biomolecules is extremely challenging thus far. In addition to the large numbers of diffraction patterns required, the orientation of each diffraction pattern needs to be accurately estimated and the missing phase information needs to be recovered for three-dimensional (3D) structure reconstruction. Given the current limitations to the amount and resolution of the data available from single-particle XFEL scattering experiments, we propose an alternative approach to find plausible 3D biological shapes from a limited number of diffraction patterns to serve as a starting point for further analyses. In our proposed strategy, small sets of input (e.g., five) XFEL diffraction patterns were matched against a library of diffraction patterns simulated from 1628 electron microscopy (EM) models to find potential matching 3D models that are consistent with the input diffraction patterns. This approach was tested for three example cases: EMD-3457 (Thermoplasma acidophilum 20S proteasome), EMD-5141 (Escherichia coli 70S ribosome complex), and EMD-5152 (budding yeast Nup84 complex). We observed that choosing the best strategy to define matching regions on the diffraction patterns is critical for identifying correctly matching diffraction patterns. While increasing the number of input diffraction patterns improved the matches in some cases, we found that the resulting matches are more dependent on the uniqueness or complexity of the shape as captured in the individual input diffraction patterns and the availability of a similar 3D biological shape in the search library. The protocol could be useful for finding candidate models for a limited amount of low-resolution data, even when insufficient for reconstruction, performing a quick exploration of new data upon collection, and the analysis of the conformational heterogeneity of the particle of interest as captured within the diffraction patterns.

SlicerMorph: An open and extensible platform to retrieve, visualize and analyse 3D morphology

Abstract Large‐scale digitization projects such as #ScanAllFishes and oVert are generating high‐resolution microCT scans of vertebrates by the thousands. Data from these projects are shared with the community using aggregate 3D specimen repositories like MorphoSource through various open licenses. We anticipate an explosion of quantitative research in organismal biology with the convergence of available data and the methodologies to analyse them. Though the data are available, the road from a series of images to analysis is fraught with challenges for most biologists. It involves tedious tasks of data format conversions, preserving spatial scale of the data accurately, 3D visualization and segmentations, and acquiring measurements and annotations. When scientists use commercial software with proprietary formats, a roadblock for data exchange, collaboration and reproducibility is erected that hurts the efforts of the scientific community to broaden participation in research. We developed SlicerMorph as an extension of 3D Slicer, a biomedical visualization and analysis ecosystem with extensive visualization and segmentation capabilities built on proven python‐scriptable open‐source libraries such as Visualization Toolkit and Insight Toolkit. In addition to the core functionalities of Slicer, SlicerMorph provides users with modules to conveniently retrieve open‐access 3D models or import users own 3D volumes, to annotate 3D curve and patch‐based landmarks, generate landmark templates, conduct geometric morphometric analyses of 3D organismal form using both landmark‐driven and landmark‐free approaches, and create 3D animations from their results. We highlight how these individual modules can be tied together to establish complete workflow(s) from image sequence to morphospace. Our software development efforts were supplemented with short courses and workshops that cover the fundamentals of 3D imaging and morphometric analyses as it applies to study of organismal form and shape in evolutionary biology. Our goal is to establish a community of organismal biologists centred around Slicer and SlicerMorph to facilitate easy exchange of data and results and collaborations using 3D specimens. Our proposition to our colleagues is that using a common open platform supported by a large user and developer community ensures the longevity and sustainability of the tools beyond the initial development effort.

Salt to conserve: a review on the ecology and preservation of hypersaline ecosystems.

When it comes to the investigation of key ecosystems in the world, we often omit salt from the ecological recipe. In fact, despite occupying almost half of the volume of inland waters and providing crucial services to humanity and nature, inland saline ecosystems are often overlooked in discussions regarding the preservation of global aquatic resources of our planet. As a result, our knowledge of the biological and geochemical dynamics shaping these environments remains incomplete and we are hesitant in framing effective protective strategies against the increasing natural and anthropogenic threats faced by such habitats. Hypersaline lakes, water bodies where the concentration of salt exceeds 35g/l, occur mainly in arid and semiarid areas resulting from hydrological imbalances triggering the accumulation of salts over time. Often considered the 'exotic siblings' within the family of inland waters, these ecosystems host some of the most extremophile communities worldwide and provide essential habitats for waterbirds and many other organisms in already water-stressed regions. These systems are often highlighted as natural laboratories, ideal for addressing central ecological questions due to their relatively low complexity and simple food web structures. However, recent studies on the biogeochemical mechanisms framing hypersaline communities have challenged this archetype, arguing that newly discovered highly diverse communities are characterised by specific trophic interactions shaped by high levels of specialisation. The main goal of this review is to explore our current understanding of the ecological dynamics of hypersaline ecosystems by addressing four main research questions: (i) why are hypersaline lakes unique from a biological and geochemical perspective; (ii) which biota inhabit these ecosystems and how have they adapted to the high salt conditions; (iii) how do we protect biodiversity from increasing natural and anthropogenic threats; and (iv) which scientific tools will help us preserve hypersaline ecosystems in the future? First, we focus on the ecological characterisation of hypersaline ecosystems, illustrate hydrogeochemical dynamics regulating such environments, and outline key ecoregions supporting hypersaline systems across the globe. Second, we depict the diversity and functional aspects of key taxa found in hypersaline lakes, from microorganisms to plants, invertebrates, waterbirds and upper trophic levels. Next, we describe ecosystem services and discuss possible conservation guidelines. Finally, we outline how cutting-edge technologies can provide new insights into the study of hypersaline ecology. Overall, this review sheds further light onto these understudied ecosystems, largely unrecognised as important sources of unique biological and functional diversity. We provide perspectives for key future research avenues, and advocate that the conservation of hypersaline lakes should not be taken with 'a grain of salt'.

Rigid motion invariant statistical shape modeling based on discrete fundamental forms: Data from the osteoarthritis initiative and the Alzheimer’s disease neuroimaging initiative

We present a novel approach for nonlinear statistical shape modeling that is invariant under Euclidean motion and thus alignment-free. By analyzing metric distortion and curvature of shapes as elements of Lie groups in a consistent Riemannian setting, we construct a framework that reliably handles large deformations. Due to the explicit character of Lie group operations, our non-Euclidean method is very efficient allowing for fast and numerically robust processing. This facilitates Riemannian analysis of large shape populations accessible through longitudinal and multi-site imaging studies providing increased statistical power. Additionally, as planar configurations form a submanifold in shape space, our representation allows for effective estimation of quasi-isometric surfaces flattenings. We evaluate the performance of our model w.r.t. shape-based classification of hippocampus and femur malformations due to Alzheimer’s disease and osteoarthritis, respectively. In particular, we outperform state-of-the-art classifiers based on geometric deep learning as well as statistical shape modeling especially in presence of sparse training data. To provide insight into the model’s ability of capturing biological shape variability, we carry out an analysis of specificity and generalization ability.

The Expanding Role of Geometric Morphometrics in Craniofacial Surgery.

Geometric morphometrics (GM) is an advanced landmark-based quantitative method used to study biological shape and form. Historically, GM has been limited to non-biomedical fields such as comparative biology; however, this technique confers advantages over traditional cephalometric methods, warranting a review of current applications of GM to human craniofacial disorders. The RISmed package was used to extract metadata associated with PubMed publications referencing GM analysis techniques in craniofacial and reconstructive surgery. PubMed search terms included "geometric AND morphometric AND craniofacial;" and "geometric AND morphometric AND reconstructive surgery." Duplicate search results were eliminated. Search yielded 139 studies between 2005 and 2020, of which 27 met inclusion criteria. Human craniofacial studies constituted 2% of all queried GM studies. Among these, cleft lip and palate were the most commonly studied craniofacial conditions (7 studies, 26%), followed by sagittal craniosynostosis (4 studies, 15%). Seventeen studies (63%) used GM to assess skeletal structures, seven studies (26%) examined both skeletal and soft tissues, and three studies (11%) analyzed soft tissues only. Eleven studies (40.1%) employed a GM approach to evaluate postoperative changes in craniofacial morphology. Two studies (7%) systematically compared GM analysis with conventional shape measurements. The ability to study shape while controlling for variability in structure size and imaging technique make GM a promising tool for understanding growth patterns in complex craniofacial diseases. Furthermore, GM overcomes many limitations of traditional cephalometric techniques, and hence may claim an expanded role in the study of human craniofacial disorders in clinical and research settings.

Principles of dengue virus evolvability derived from genotype-fitness maps in human and mosquito cells.

Dengue virus (DENV) cycles between mosquito and mammalian hosts. To examine how DENV populations adapt to these different host environments, we used serial passage in human and mosquito cell lines and estimated fitness effects for all single-nucleotide variants in these populations using ultra-deep sequencing. This allowed us to determine the contributions of beneficial and deleterious mutations to the collective fitness of the population. Our analysis revealed that the continuous influx of a large burden of deleterious mutations counterbalances the effect of rare, host-specific beneficial mutations to shape the path of adaptation. Beneficial mutations preferentially map to intrinsically disordered domains in the viral proteome and cluster to defined regions in the genome. These phenotypically redundant adaptive alleles may facilitate host-specific DENV adaptation. Importantly, the evolutionary constraints described in our simple system mirror trends observed across DENV and Zika strains, indicating it recapitulates key biophysical and biological constraints shaping long-term viral evolution.

Wetland hydroperiod predicts community structure, but not the magnitude of cross-community congruence

A major focus in community ecology is understanding how biological interactions and environmental conditions shape horizontal communities. However, few studies have explored whether cross-community interactions are consistent or non-stationary across environmental gradients. Using the relative abundance of birds, aquatic macroinvertebrates and plants, we examined how cross-community congruence varied between short and long-hydroperiod prairie pothole wetlands in southern Alberta. These wetlands are structured by their hydroperiod: the length of time that ponded water is present in the wetland. We compared the strength of cross-community congruence and the strength of congruence between each horizontal community and wetland hydroperiod in wetlands that typically contain ponded water throughout the year to wetlands that dry up every summer. The strength of cross-community relationships was similar between more permanent and more ephemeral wetland classes, suggesting that biological interactions have a near equivalent role in shaping community composition, regardless of hydroperiod. However, because cross-community congruence, measured as the Procrustes pseudo-R value, was, on average, 77% ± SE 12% greater than that between each horizontal community and measures of wetland hydroperiod, we concluded that community structure is not shaped by hydroperiod alone. We attribute the observed cross-community congruence to (1) plants and aquatic macroinvertebrates influence birds through habitat and food provisioning, and (2) birds influence plants and aquatic macroinvertebrates by dispersing their propagules.

How do large river zooplankton communities respond to abiotic and biotic drivers over time? A complex and spatially dependent example

Abstract Recent evidence shows that in lotic, physically‐dominated ecosystems like large rivers, zooplankton can develop spatially structured assemblages and fulfill functionally important roles. While refuting the long‐standing notion that riverine zooplankton communities are numerically depauperate and spatially homogeneous, this also shows that the importance of abiotic and biotic drivers and the consistency of ecological patterns are still poorly understood for these communities. We collected zooplankton along 300 km of the Illinois River for 5 years to test the influence of a suite of biotic and abiotic variables on zooplankton density, biomass, and diversity. We hypothesized abiotic variables such as temperature, turbidity, and velocity would be predominant predictors, with biotic variables like planktivory becoming important when physical factors do not constrain them. Results showed basin‐wide declines in zooplankton taxonomic richness as planktivory from the invading silver carp (Hypophthalmichthys molitrix) increased. In contrast, density and biomass were explained by abiotic factors at the basin scale, with velocity, turbidity, and pH driving biomass and velocity driving density. We also found that both the driving factors and the plankton responses varied among the upper, middle, and lower sections of the Illinois River. We conclude that while zooplankton communities of the Illinois River are highly structured in space, the driving forces behind their distribution patterns are not simple. Instead, there is a complex spatio‐temporal template of biological, chemical, and hydraulic factors shaping these communities in the Illinois River. Along with emphasizing the importance of spatial heterogeneity in large river ecosystems, these results also support a growing view that invasive species interactions can supersede the well‐established role of abiotic factors in explaining global freshwater diversity losses. This also highlights the importance of including long‐term zooplankton sampling in ecological monitoring programs in diverse and productive large river systems like the Illinois.

Comparative design, hydrodynamic analysis, and physical performance of fish-like robots

Biological aquatic animals are turned to for design inspiration of marine drones because of their efficient long-range performance and high agility in the water. Investigations of body caudal fin biological shapes from the Carangiform, Subcarangiform, and Thunniform categories are compared to widely used symmetric airfoils to determine shape performance. Three-dimensional hydrodynamic results using four solution methods are examined to analyze these hydrofoils at decreasing aspect ratios with a constant wing chord length. This numerical investigation results in the selection of the Subcarangiform hydrofoil to be used in the physical design of a fish-like bio-inspired drone. These hydrodynamic results are utilized to adapt a fish-like aquatic unmanned vehicle from conceptual design to working prototype that is tested in a mission environment. Parameters for buoyancy and stability are then defined to give guidance during the physical design phase. Quantities for weight and volume could be extrapolated and used to determine the need for ballast in the system. The final prototype system is tested in its mission environment. It accomplishes turns at a body length to turn radius ratio of 1:1, at a swim velocity of 1.5 body lengths per second. This system's real-world performance is compared to that of other systems.

Automated Superpixel-borders-guided Deformation Image Registration for Adaptive Radiotherapy

Registration between the baseline and follow-up lung computed tomography (CT) volumes plays an important role in computer-aided diagnosis and following-up care during adaptive radiotherapy. Diffeomorphic log-Demons as state of the art in Demons implementations is restricted to relatively small deformations, low accuracy, and ignoring some prior structural features. In this paper, an automated superpixel-borders-guided deformation image registration (SBG-DIR) algorithm is proposed. The proposed SBG-DIR method uses the simple linear iterative clustering (SLIC) algorithm to automated superpixels generation. Incorporation of superpixel borders into registration algorithm is implemented by a new similarity criterion based on the binary volume representation of superpixel borders. The binary volume representation enables accurate preserving motion boundaries, contributes to a faster convergence of the objective function and eliminates errors caused by manual interaction. In addition, a subtraction volume is produced by the intensity difference between the first time point CT volume and its warped follow-up CT volume. The subtraction volume can be used for detection of tumor tissue growth or shrinkage, which is an essential part of a CT-based diagnosis. Moreover, to ensure the topology preservation of biological objects, our proposed SBG-DIR method is implemented in the space of diffeomorphisms, in which meaningful biological shapes can be found. Compared with the state-of-the-art Demons, the proposed SBG-DIR method doesn’t require any additional optimization, yields a faster convergence and is more accurate and efficient in recovering large deformations. Experimental results indicate that the proposed SBG-DIR method performed better than the state-of-the-art Demons algorithms.

A Curvature and Density‐based Generative Representation of Shapes

AbstractThis paper introduces a generative model for 3D surfaces based on a representation of shapes with mean curvature and metric, which are invariant under rigid transformation. Hence, compared with existing 3D machine learning frameworks, our model substantially reduces the influence of translation and rotation. In addition, the local structure of shapes will be more precisely captured, since the curvature is explicitly encoded in our model. Specifically, every surface is first conformally mapped to a canonical domain, such as a unit disk or a unit sphere. Then, it is represented by two functions: the mean curvature half‐density and the vertex density, over this canonical domain. Assuming that input shapes follow a certain distribution in a latent space, we use the variational autoencoder to learn the latent space representation. After the learning, we can generate variations of shapes by randomly sampling the distribution in the latent space. Surfaces with triangular meshes can be reconstructed from the generated data by applying isotropic remeshing and spin transformation, which is given by Dirac equation. We demonstrate the effectiveness of our model on datasets of man‐made and biological shapes and compare the results with other methods.

Place and Environment in the Ongoing Evolution of Cognitive Neuroscience.

Cognitive science today increasingly is coming under the influence of embodied, embedded, extended, and enactive perspectives, superimposed on the more traditional cybernetic, computational assumptions of classical cognitive research. Neuroscience has contributed to a greatly enhanced understanding of brain function within the constraints of the traditional cognitive science approach, but interpretations of many of its findings can be enriched by the newer alternative perspectives. Here, we note in particular how these frameworks highlight the cognitive requirements of an animal situated within its particular environment, how the coevolution of an organism's biology and ecology shape its cognitive characteristics, and how the cognitive realm extends beyond the brain of the perceiving animal. We argue that these insights of the embodied cognition paradigm reveal the central role that "place" plays in the cognitive landscape and that cognitive scientists and philosophers alike can gain from paying heed to the importance of a concept of place. We conclude with a discussion of how this concept can be applied with respect to cognitive function, species comparisons, ecologically relevant experimental designs, and how the "hard problem" of consciousness might be approached, among its other implications.

Walking on Kendall\u2019s Shape Space: Understanding Shape Spaces and Their Coordinate Systems

More and more analyses of biological shapes are using the techniques of geometric morphometrics based on configurations of landmarks in two or three dimensions. A fundamental concept at the core of these analyses is Kendall’s shape space and local approximations to it by shape tangent spaces. Kendall’s shape space is complex because it is a curved surface and, for configurations with more than three landmarks, multidimensional. This paper uses the shape space for triangles, which is the surface of a sphere, to explore and visualize some properties of shape spaces and the respective tangent spaces. Considerations about the dimensionality of shape spaces are an important step in understanding them, and can offer a coordinate system that can translate between positions in the shape space and the corresponding landmark configurations and vice versa. By simulation studies “walking” along that are great circles around the shape space, each of them corresponding to the repeated application of a particular shape change, it is possible to grasp intuitively why shape spaces are curved and closed surfaces. From these considerations and the available information on shape spaces for configurations with more than three landmarks, the conclusion emerges that the approach using a tangent space approximation in general is valid for biological datasets. The quality of approximation depends on the scale of variation in the data, but existing analyses suggest this should be satisfactory to excellent in most empirical datasets.

Tensegrity Modelling and the High Toughness of Spider Dragline Silk.

This work establishes a tensegrity model of spider dragline silk. Tensegrity systems are ubiquitous in nature, being able to capture the mechanics of biological shapes through simple and effective modes of deformation via extension and contraction. Guided by quantitative microstructural characterization via air plasma etching and low voltage scanning electron microscopy, we report that this model is able to capture experimentally observed phenomena such as the Poisson effect, tensile stress-strain response, and fibre toughness. This is achieved by accounting for spider silks’ hierarchical organization into microfibrils with radially variable properties. Each fibril is described as a chain of polypeptide tensegrity units formed by crystalline granules operating under compression, which are connected to each other by amorphous links acting under tension. Our results demonstrate, for the first time, that a radial variability in the ductility of tensegrity chains is responsible for high fibre toughness, a defining and desirable feature of spider silk. Based on this model, a discussion about the use of graded tensegrity structures for the optimal design of next-generation biomimetic fibres is presented.