43 results found
Ding SS, Muhle LS, Brown AEX, et al., 2019, Comparison of solitary and collective foraging strategies of Caenorhabditis elegans in patchy food distributions
<jats:title>Abstract</jats:title><jats:p>The benefits of social behaviour in insects and vertebrates are well-documented in terms of mating success and predator avoidance. Social foraging has also been shown to benefit organisms in environments where food is patchily distributed, but whether this is true in the case where organisms do not rely on long-range communications to coordinate their social behaviour has been understudied. To address this question, we use the tractable laboratory model organism <jats:italic>Caenorhabditis elegans</jats:italic>, where a social strain (<jats:italic>npr-1</jats:italic> mutant) and a solitary strain (N2) are available for direct comparison of foraging strategies. We first develop an on-lattice minimal model for comparing social and solitary feeding strategies, finding that social agents benefit from feeding faster and more efficiently simply due to group formation. To compare these simulation results with real experimental data, we modify our minimal model to incorporate the specific feeding behaviours of the <jats:italic>npr-1</jats:italic> and N2 strains. Surprisingly, the resultant strain-specific model predicts that the solitary strain performs better than the social one in all food distribution environments that we tested, which we confirm with lab experiments. Additional computational experiments identify the N2 strain’s higher feeding rate to be the key factor underlying its advantage over <jats:italic>npr-1</jats:italic> worms. Our work highlights the difficulties in addressing questions of optimal behaviour, and the valuable role of modelling as a guiding principle.</jats:p>
Ding SS, Schumacher L, Javer A, et al., 2019, Shared behavioral mechanisms underlie C. elegans aggregation and swarming, eLife, Vol: 8, ISSN: 2050-084X
In complex biological systems, simple individual-level behavioral rules can give rise to emergent group-level behavior. While collective behavior has been well studied in cells and larger organisms, the mesoscopic scale is less understood, as it is unclear which sensory inputs and physical processes matter a priori. Here, we investigate collective feeding in the roundworm C. elegans at this intermediate scale, using quantitative phenotyping and agent-based modeling to identify behavioral rules underlying both aggregation and swarming—a dynamic phenotype only observed at longer timescales. Using fluorescence multi-worm tracking, we quantify aggregation in terms of individual dynamics and population-level statistics. Then we use agent-based simulations and approximate Bayesian inference to identify three key behavioral rules for aggregation: cluster-edge reversals, a density-dependent switch between crawling speeds, and taxis towards neighboring worms. Our simulations suggest that swarming is simply driven by local food depletion but otherwise employs the same behavioral mechanisms as the initial aggregation.
Javer A, Ripoll-Sanchez L, Brown AE, 2018, Powerful and interpretable behavioural features for quantitative phenotyping of C. elegans, Philosophical Transactions B: Biological Sciences, Vol: 373, ISSN: 0962-8436
Behaviour is a sensitive and integrative readout of nervous system function and therefore an attractive measure for assessing the effects of mutation or drug treatment on animals. Video data provide a rich but high-dimensional representation of behaviour, and so the first step of analysis is often some form of tracking and feature extraction to reduce dimensionality while maintaining relevant information. Modern machine-learning methods are powerful but notoriously difficult to interpret, while handcrafted features are interpretable but do not always perform as well. Here, we report a new set of handcrafted features to compactly quantify Caenorhabditis elegans behaviour. The features are designed to be interpretable but to capture as much of the phenotypic differences between worms as possible. We show that the full feature set is more powerful than a previously defined feature set in classifying mutant strains. We then use a combination of automated and manual feature selection to define a core set of interpretable features that still provides sufficient power to detect behavioural differences between mutant strains and the wild-type. Finally, we apply the new features to detect time-resolved behavioural differences in a series of optogenetic experiments targeting different neural subsets.
Larson SD, Gleeson P, Brown AEX, 2018, Connectome to behaviour: modelling Caenorhabditis elegans at cellular resolution, Philosophical Transactions of the Royal Society of London: Biological Sciences, Vol: 373, ISSN: 0962-8436
It has been 30 years since the ‘mind of the worm’ was published in Philosophical Transactions B (White et al. 1986 Phil. Trans. R. Soc. Lond. B 314, 1–340). Predicting Caenorhabditis elegans' behaviour from its wiring diagram has been an enduring challenge since then. This special theme issue of Philosophical Transactions B combines research from neuroscientists, physicists, mathematicians and engineers to discuss advances in neural activity imaging, behaviour quantification and multiscale simulations, and how they are bringing the goal of whole-animal modelling at cellular resolution within reach.
Chew YL, Grundy LJ, Brown AEX, et al., 2018, Neuropeptides encoded by nlp-49 modulate locomotion, arousal and egg-laying behaviours in Caenorhabditis elegans via the receptor SEB-3, Philosophical Transactions of the Royal Society of London: Biological Sciences, Vol: 373, ISSN: 0962-8436
Neuropeptide signalling has been implicated in a wide variety of biological processes in diverse organisms, from invertebrates to humans. The Caenorhabditis elegans genome has at least 154 neuropeptide precursor genes, encoding over 300 bioactive peptides. These neuromodulators are thought to largely signal beyond ‘wired’ chemical/electrical synapse connections, therefore creating a ‘wireless’ network for neuronal communication. Here, we investigated how behavioural states are affected by neuropeptide signalling through the G protein-coupled receptor SEB-3, which belongs to a bilaterian family of orphan secretin receptors. Using reverse pharmacology, we identified the neuropeptide NLP-49 as a ligand of this evolutionarily conserved neuropeptide receptor. Our findings demonstrate novel roles for NLP-49 and SEB-3 in locomotion, arousal and egg-laying. Specifically, high-content analysis of locomotor behaviour indicates that seb-3 and nlp-49 deletion mutants cause remarkably similar abnormalities in movement dynamics, which are reversed by overexpression of wild-type transgenes. Overexpression of NLP-49 in AVK interneurons leads to heightened locomotor arousal, an effect that is dependent on seb-3. Finally, seb-3 and nlp-49 mutants also show constitutive egg-laying in liquid medium and alter the temporal pattern of egg-laying in similar ways. Together, these results provide in vivo evidence that NLP-49 peptides act through SEB-3 to modulate behaviour, and highlight the importance of neuropeptide signalling in the control of behavioural states.This article is part of a discussion meeting issue ‘Connectome to behaviour: modelling C. elegans at cellular resolution’.
Javer A, Currie M, Lee CW, et al., 2018, An open source platform for analyzing and sharing worm behavior data, Nature Methods, Vol: 15, Pages: 645-646, ISSN: 1548-7091
Ding SS, Schumacher LJ, Javer AE, et al., 2018, Shared behavioral mechanisms underlie C. elegans aggregation and swarming, Publisher: Cold Spring Harbor Laboratory
<jats:title>Abstract</jats:title><jats:p>In complex biological systems, simple individual-level behavioral rules can give rise to emergent group-level behavior. While such collective behavior has been well studied in cells and larger organisms, the mesoscopic scale is less understood, as it is unclear which sensory inputs and physical processes matter <jats:italic>a priori</jats:italic>. Here, we investigate collective feeding in the roundworm <jats:italic>C. elegans</jats:italic> at this intermediate scale, using quantitative phenotyping and agent-based modeling to identify behavioral rules underlying both aggregation and swarming—a dynamic phenotype only observed at longer timescales. Using fluorescent multi-worm tracking, we quantify aggregation behavior in terms of individual dynamics and population-level statistics. Based on our quantification, we use agent-based simulations and approximate Bayesian inference to identify three key behavioral rules that give rise to aggregation: cluster-edge reversals, a density-dependent switch between crawling speeds, and taxis towards neighboring worms. Our simulations suggest that swarming is simply driven by local food depletion but otherwise employs the same behavioral mechanisms as the initial aggregation. Hence, mesoscopic <jats:italic>C. elegans</jats:italic> uses mechanisms familiar from microscopic systems for aggregation, but implemented via more complex behaviors characteristic of macroscopic organisms.</jats:p>
Chakraborty K, Vijayan K, Brown AEX, et al., 2018, Glassy worm-like micelles in solvent and shear mediated shape transitions., Soft Matter, Vol: 2018, ISSN: 1744-683X
The glassiness of polymer melts is generally considered to be suppressed by small dimensions, added solvent, and heat. Here, we suggest that glassiness persists at the nanoscale in worm-like micelles composed of amphiphilic diblock copolymers of poly(ethylene oxide)-polystyrene (PS). The glassiness of these worms is indicated by a lack of fluorescence recovery after photobleaching as well as micron-length rigid segments separated by hinges. The coarse-grained molecular dynamics studies probe the dynamics of the PS in these glassy worms. Addition of an organic solvent promotes a transition from hinged to fully flexible worms and to spheres or vesicles. Simulation demonstrates two populations of organic solvent in the core of the micelle-a solvent 'pool' in the micelle core and a second population that accumulates at the interface between the core and the corona. The stable heterogeneity of the residual solvent could explain the unusual hinged rigidity, but solvent removal during shear-extension could be more effective and yield - as observed - nearly straight worms without hinges.
Brown AEX, de Bivort B, 2018, Ethology as a physical science, Nature Physics, Vol: 14, Pages: 653-657, ISSN: 1745-2473
The study of animal behaviour, ethology, is becoming more quantitative. New theory is emerging, driven by better imaging and novel representations of animal posture dynamics that span the vast range of relevant behavioural timescales.
Brown AEX, de Bivort B, 2018, Ethology as a physical science, Publisher: bioRxiv
Behaviour is the ultimate output of an animal's nervous system and choosing the right action at the right time can be critical for survival. The study of the organisation of behaviour in its natural context, ethology, has historically been a primarily qualitative science. A quantitative theory of behaviour would advance research in neuroscience as well as ecology and evolution. However, animal posture typically has many degrees of freedom and behavioural dynamics vary on timescales ranging from milliseconds to years, presenting both technical and conceptual challenges. Here we review 1) advances in imaging and computer vision that are making it possible to capture increasingly complete records of animal motion and 2) new approaches to understanding the resulting behavioural data sets. With the right analytical approaches, these data are allowing researchers to revisit longstanding questions about the structure and organisation of animal behaviour and to put unifying principles on a quantitative footing. Contributions from both experimentalists and theorists are leading to the emergence of a physics of behaviour and the prospect of discovering laws and developing theories with broad applicability. We believe that there now exists an opportunity to develop theories of behaviour which can be tested using these data sets leading to a deeper understanding of how and why animals behave.
Li K, Javer A, Keaveny E, et al., Recurrent Neural Networks with Interpretable Cells Predict and Classify Worm Behaviour, Twenty-ninth Annual Conference on Neural Information Processing Systems (NIPS)
An important goal in behaviour analytics is to connect disease state or genomevariation with observable differences in behaviour. Despite advances in sensortechnology and imaging, informative behaviour quantification remains challenging.The nematode worm C. elegans provides a unique opportunity to test analysisapproaches because of its small size, compact nervous system, and the availabilityof large databases of videos of freely behaving animals with known genetic differences.Despite its relative simplicity, there are still no reports of generative modelsthat can capture essential differences between even well-described mutant strains.Here we show that a multilayer recurrent neural network (RNN) can produce diversebehaviours that are difficult to distinguish from real worms’ behaviour andthat some of the artificial neurons in the RNN are interpretable and correlate withobservable features such as body curvature, speed, and reversals. Although theRNN is not trained to perform classification, we find that artificial neuron responsesprovide features that perform well in worm strain classification.
Keaveny E, Brown AE, 2017, Predicting path from undulations for C. elegans using linear and nonlinear resistive force theory, Physical Biology, Vol: 14, ISSN: 1478-3975
A basic issuein the physics of behaviouris the mechanical relationship between an animal and its surroundings. The nematode and model organism C. elegans provides an excellent platform to explore this relationship due to its anatomical simplicity. Nonetheless,the physics of nematode crawling, in which the worm undulates its body to move on a wet surface, is not completely understoodand the mathematical models often used to describe this phenomenon are empirical. We confirm that linear resistive force theory, one such empirical model,is effective at predicting a worm’s path from its sequence of body postures for forward crawling, reversing, and turning and for a broad range of different behavioural phenotypes observedin mutant worms. However, agreement between the predicted and observed path is lost when using this model with recently measured valuesof the drag anisotropy. A recently proposed nonlinear extensionof the resistive force theory model also provides accurate predictions, but does not resolve the discrepancy between the parameters required to achieve good path prediction and the experimentally measured parameters. This meansthat while we have good effective models of worm crawling that can be used in applications such as whole-animal simulations and advanced tracking algorithms, there are still unanswered questions about the precise nature of the physical interaction between worms and their most commonly studied laboratory substrate.
Gomez-Marin A, Stephens GJ, Brown AE, 2016, Hierarchical compression of C. elegans locomotion reveals phenotypic differences in the organisation of behaviour, Journal of the Royal Society Interface, Vol: 13, ISSN: 1742-5689
Regularities in animal behaviour offer insight into the underlying organisational and functional principles of nervous systems and automated tracking provides the opportunity to extract featuresof behaviour directly from large-scale video data. Yet how to effectively analyse such behavioural data remains an open question. Here we explore whether a minimum description length principle can beexploited to identify meaningful behaviours and phenotypes. We apply a dictionary compression algorithm to behavioural sequences from the nematode worm Caenorhabditis elegans freely crawling on an agar plate both with and without food and during chemotaxis. We find that the motifs identified by the compression algorithm are rare but relevant for comparisons between worms in different environments, suggesting that hierarchical compression can be a useful step in behaviour analysis. We also use compressibility as a new quantitative phenotype and find that the behaviour of wild-isolated strains of C. elegans is more compressible than that of the laboratory strain N2 as well as the majority of mutant strains examined. Importantly, in distinction to more conventional phenotypes such as overall motor activity or aggregation behaviour, the increased compressibility of wild isolates is not explained by the loss of function of the gene npr-1, which suggests that erratic locomotion is a laboratory-derived trait with a novel genetic basis. Because hierarchical compression can be applied to any sequence, we anticipate that compressibility can offer insight into the organisation of behaviour in other animals including humans.
Brown AE, Gyenes B, 2016, Deriving shape-based features for C. elegans locomotion using dimensionality reduction methods, Frontiers in Behavioral Neuroscience, Vol: 10, ISSN: 1662-5153
High-throughput analysis of animal behavior is increasingly common following the advances of recording technology, leading to large high-dimensional data sets. This dimensionality can sometimes be reduced while still retaining relevant information. In the case of the nematode worm Caenorhabditis elegans, more than 90% of the shape variance can be captured using just four principal components. However, it remains unclear if other methods can achieve a more compact representation or contribute further biological insight to worm locomotion. Here we take a data-driven approach to worm shape analysis using independent component analysis (ICA), non-negative matrix factorization (NMF), a cosine series, and jPCA (a dynamic variant of principal component analysis [PCA]) and confirm that the dimensionality of worm shape space is close to four. Projecting worm shapes onto the bases derived using each method gives interpretable features ranging from head movements to tail oscillation. We use these as a comparison method to find differences between the wild type N2 worms and various mutants. For example, we find that the neuropeptide mutant nlp-1(ok1469) has an exaggerated head movement suggesting a mode of action for the previously described increased turning rate. The different bases provide complementary views of worm behavior and we expect that closer examination of the time series of projected amplitudes will lead to new results in the future.
Schwarz RF, Branicky R, Grundy LJ, et al., 2015, Changes in postural syntax characterize sensory modulation and natural variation of C. elegans locomotion, PLOS Computational Biology, Vol: 11, ISSN: 1553-734X
Locomotion is driven by shape changes coordinated by the nervous system through time;thus, enumerating an animal's complete repertoire of shape transitions would provide abasis for a comprehensive understanding of locomotor behaviour. Here we introduce a discreterepresentation of behaviour in the nematode C. elegans. At each point in time, theworm’s posture is approximated by its closest matching template from a set of 90 posturesand locomotion is represented as sequences of postures. The frequency distribution of posturalsequences is heavy-tailed with a core of frequent behaviours and a much larger set ofrarely used behaviours. Responses to optogenetic and environmental stimuli can be quantifiedas changes in postural syntax: worms show different preferences for differentsequences of postures drawn from the same set of templates. A discrete representation of behaviour will enable the use of methods developed for other kinds of discrete data in bioinformatics and language processing to be harnessed for the study of behaviour.
Koren Y, Sznitman R, Arratia PE, et al., 2015, Model-independent phenotyping of C. elegans locomotion using scale-invariant feature transform, PLOS One, Vol: 10, ISSN: 1932-6203
To uncover the genetic basis of behavioral traits in the model organism C. elegans, a common strategy is to study locomotion defects in mutants. Despite efforts to introduce (semi-)automated phenotyping strategies, current methods overwhelmingly depend on worm-specific features that must be hand-crafted and as such are not generalizable for phenotyping motility in other animal models. Hence, there is an ongoing need for robust algorithms that can automatically analyze and classify motility phenotypes quantitatively. To this end, we have developed a fully-automated approach to characterize C. elegans’ phenotypes that does not require the definition of nematode-specific features. Rather, we make use of the popular computer vision Scale-Invariant Feature Transform (SIFT) from which we construct histograms of commonly-observed SIFT features to represent nematode motility. We first evaluated our method on a synthetic dataset simulating a range of nematode crawling gaits. Next, we evaluated our algorithm on two distinct datasets of crawling C. elegans with mutants affecting neuromuscular structure and function. Not only is our algorithm able to detect differences between strains, results capture similarities in locomotory phenotypes that lead to clustering that is consistent with expectations based on genetic relationships. Our proposed approach generalizes directly and should be applicable to other animal models. Such applicability holds promise for computational ethology as more groups collect high-resolution image data of animal behavior.
Gulli S, Maddalena L, McKelvey C, et al., 2015, Characterization of complex porous structures for reusable thermal protection systems: Porosity measurements, Journal of Spacecraft and Rockets, Vol: 52, Pages: 166-176, ISSN: 0022-4650
Copyright © 2014 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved. This work is focused on the nonintrusive characterization of the local and average porosity of a prototype carbon-carbon nose, representative of a reusable thermal protection system based on transpiration cooling. A study based on the x-ray computed tomography scan of the specimen has been carried out with the purpose of defining the most important guidelines for the permeability tests, which are the minimum areatobeprobed with a hot-film anemometer and the correct distance of the mass flux sensor from the wall. The former has been calculated from the average porosity calculation, whereas the latter has been retrieved from the statistical analysis of the dimensions, and the distribution of the void structures inside the porous network coupled to the theory of fluid flow through perforated plates. Several longitudinal and transversal sectioning planes with respect to the symmetry axis of the carbon mask have been analyzed to calculate the internal porosity from the two-dimensional images, whereas the three-dimensional reconstruction of the sample has been used to retrieve the average volumetric porosity. Both the nominal values of the two-dimensional porosity and volumetric porosity have provided the same dimension of the characteristic area to be probed with a hot-film sensor for the permeability measurements. Preliminary permeability tests, performed within the predicted dimension of the control surface, have confirmed the uniformity of the mean velocity field and allowed verifying the range of variation of the correct distance of a hot-film sensor from the wall obtained from the statistical analysis of the computed tomography images.
Brown AEX, Schafer WR, 2015, Automated behavioural fingerprinting of caenorhabditis elegans mutants, Systems Genetics: Linking Genotypes and Phenotypes, Pages: 234-256, ISBN: 9781107013841
© Cambridge University Press 2015. Rapid advances in genetics, genomics and imaging have given insight into the molecular and cellular basis of behaviour in a variety of model organisms with unprecedented detail and scope. It is increasingly becoming routine to isolate behavioural mutants, clone and characterise mutant genes and discern the molecular and neural basis for a behavioural phenotype. Conversely, reverse genetic approaches have made it possible to straightforwardly identify genes of interest in whole-genome sequences and generate mutants that can be subjected to phenotypic analysis. In this latter approach, it is the phenol typing that presents the major bottleneck; when it comes to connecting phenotype to genotype in freely behaving animals, analysis of behaviour itself remains superficial and time-consuming. However, many proof-of-principle studies of automated behavioural analysis over the last decade have poised the field on the verge of exciting developments that promise to begin closing this gap. In the broadest sense, our goal in this chapter is to explore what we can learn about the genes involved in neural function by carefully observing behaviour. This approach is rooted in model organism genetics but shares ideas with ethology and neuroscience, as well as computer vision and bioinformatics. After introducing Caenorhabditis elegans as a model, we will survey the research that has led to the current state of the art in worm behavioural phenol typing and present current research that is transforming our approach to behavioural genetics. The worm as a model organism Caenorhabditis elegans is a nematode worm that lives in bacteria-rich environments such as rotting fruit and has also been isolated from insects and snails which it is thought to use for longer-range transportation (Barriere and Felix 2005, Lee et al. 2011). In the laboratory, it is commonly cultured on the surface of agar plates seeded with a lawn of the bacterium Escherichia coli
Yemini EI, Brown AEX, 2015, Tracking Single C. elegans Using a USB Microscope on a Motorized Stage., Pages: 181-197
Locomotion and gross morphology have been important phenotypes for C. elegans genetics since the inception of the field and remain relevant. In parallel with developments in genome sequencing and editing, phenotyping methods have become more automated and quantitative, making it possible to detect subtle differences between mutants and wild-type animals. In this chapter, we describe how to calibrate a single-worm tracker consisting of a USB microscope mounted on a motorized stage and how to record and analyze movies of worms crawling on food. The resulting quantitative phenotypic fingerprint can sensitively identify differences between mutant and wild-type worms.
Gulli S, Maddalena L, McKelvey C, et al., 2014, Permeability measurements of complex porous structures for reusable thermal protection systems (invited paper)
Reusable thermal protection systems are one of the key technologies that have to be improved in order to afford long-duration hypersonic flights. Transpiration cooling has been demonstrated to be one of the most promising active cooling techniques in terms of temperature decreasing and coolant mass saving. The coupling of the boundary layer with the thermal response of selected porous materials plays a crucial role in enabling the practical use of the transpiration cooling technique for reusable thermal protection systems. In this work, a novel test-rig for the non-intrusive characterization in terms of local permeability of a customized porous Carbon-Carbon nose-tip is proposed. The new concept of effective permeability, conceived as the local blowing capability of a porous structure with respect to a selected coolant fluid, is also introduced. The coolant (air) mass-fluxes blown from the porous surface of the specimen, and measured by a hot-film probe, are related to the average pressure gradient across the local material thickness by using the Darcy's law on prescribed locations. A parallel work, based on the X-Ray computed tomography scan of the prototype specimen, has been carried out with the purpose of defining the most important guidelines for the effective-permeability tests. Specifically, the calculation of the average porosity is used to define the minimum area to be probed with the hot-wire. The analysis of the statistical distribution of the void structures inside the C/C cone, coupled to the use of the theory of fluid-flow through perforated plates, is performed to determine the correct distance of the hot-wire from the wall. The results show permeability variation among the surveyed locations ranging from 6% to 172%. The effective-permeability map obtained allows classifying the prototype C/C mask as a semi-pervious structure. In particular, the higher effective permeability is located near the stagnation point region where two delaminations are locat
Yemini E, Jucikas T, Grundy LJ, et al., 2013, A database of Caenorhabditis elegans behavioral phenotypes, NATURE METHODS, Vol: 10, Pages: 877-+, ISSN: 1548-7091
Brown AEX, Yemini EI, Grundy LJ, et al., 2013, A dictionary of behavioral motifs reveals clusters of genes affecting Caenorhabditis elegans locomotion, PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, Vol: 110, Pages: 791-796, ISSN: 0027-8424
Russell CA, Fonville JM, Brown AEX, et al., 2012, The Potential for Respiratory Droplet-Transmissible A/H5N1 Influenza Virus to Evolve in a Mammalian Host, SCIENCE, Vol: 336, Pages: 1541-1547, ISSN: 0036-8075
Rehfeldt F, Brown AEX, Raab M, et al., 2012, Hyaluronic acid matrices show matrix stiffness in 2D and 3D dictates cytoskeletal order and myosin-II phosphorylation within stem cells, INTEGRATIVE BIOLOGY, Vol: 4, Pages: 422-430, ISSN: 1757-9694
Purohit PK, Litvinov RI, Brown AEX, et al., 2011, Protein unfolding accounts for the unusual mechanical behavior of fibrin networks, ACTA BIOMATERIALIA, Vol: 7, Pages: 2374-2383, ISSN: 1742-7061
Brown AEX, Schafer WR, 2011, Unrestrained worms bridled by the light, NATURE METHODS, Vol: 8, Pages: 129-U43, ISSN: 1548-7091
Rajagopal K, Mahmud A, Christian DA, et al., 2010, Curvature-Coupled Hydration of Semicrystalline Polymer Amphiphiles Yields flexible Worm Micelles but Favors Rigid Vesicles: Polycaprolactone-Based Block Copolymers, MACROMOLECULES, Vol: 43, Pages: 9736-9746, ISSN: 0024-9297
Krieger CC, Bhasin N, Tewari M, et al., 2010, Exon-Skipped Dystrophins for Treatment of Duchenne Muscular Dystrophy: Mass Spectrometry Mapping of Most Exons and Cooperative Domain Designs Based on Single Molecule Mechanics, CYTOSKELETON, Vol: 67, Pages: 796-807, ISSN: 1949-3584
Zemel A, Rehfeldt F, Brown AEX, et al., 2010, Optimal matrix rigidity for stress-fibre polarization in stem cells, NATURE PHYSICS, Vol: 6, Pages: 468-473, ISSN: 1745-2473
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