Parallel Interaction Detection Algorithms for a Particle-based Live Controlled Real-time Microtubule Gliding Simulation System Accelerated by GPGPU

Gutmann, Gregory; Inoue, Daisuke; Kakugo, Akira; Konagaya, Akihiko

doi:10.1007/s00354-017-0011-5

Cited by 6 publications

(11 citation statements)

References 16 publications

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“…The simulation was done on an updated version of our real-time simulation system implementing the Vicsek model. 1 In our simulation, microtubules are represented by a chain of particles connected by a spring force for longitudinal tension and compression, along with a bending restoring force. Then, all microtubule segment particles within the system are under the effect of a Lennard-Jones like potential for repulsive particleparticle interactions.…”

Section: Supplementary Discussionmentioning

confidence: 99%

Adaptation of Patterns of Motile Filaments under Dynamic Boundary Conditions

et al. 2019

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Boundary conditions are important for pattern formation in active matter. However, it is still not well-understood how alterations in the boundary conditions (dynamic boundary conditions) impact pattern formation. To elucidate the effect of dynamic boundary conditions on the pattern formation by active matter, we investigate an in vitro gliding assay of microtubules on a deformable soft substrate. The dynamic boundary conditions were realized by applying mechanical stress through stretching and compression of the substrate during the gliding assay. A single cycle of stretch-and-compression (relaxation) of the substrate induces perpendicular alignment of microtubules relative to the stretch axis, whereas repeated cycles resulted in zigzag patterns of microtubules. Our model shows that the orientation angles of microtubules correspond to the direction to attain smooth movement without buckling, which is further amplified by the collective migration of the microtubules. Our results provide an insight into understanding the rich dynamics in self-organization arising in active matter subjected to time-dependent boundary conditions.

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Section: Supplementary Discussionmentioning

confidence: 99%

Adaptation of Patterns of Motile Filaments under Dynamic Boundary Conditions

et al. 2019

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“…Software frameworks that only use the CPU to process the simulation usually struggle to meet the ideal requirements [7]. Gutmann et al [9][10][11] has implemented a microtubule gliding assay simulation based on a particle simulation system using a low-level DirectX 12 graphics library with general purpose processing computing units (GPGPU) to utilize GPU performance, allowing it to simulate up to millions of particles when using multiple GPU cards. Combining with VR and haptic systems, the particle simulation system becomes a system suitable for molecular modeling with haptic VR rendering [8].…”

Section: Virtual Reality Simulationmentioning

confidence: 99%

“…Particle simulation system presented in this study is based on particle-based real-time microtubule gliding assay simulation system accelerated by GPGPU [9][10][11] and haptic rendering system for virtual reality molecular modelling [7,8]. This system simulates the movements of thousands of particles using the Euler integration for each time step.…”

Section: Particle Simulation System Overviewmentioning

confidence: 99%

“…Among several alternatives, particle simulation system using multiple Graphics Processing Units (GPUs) resources is the best candidate [7,8]. Such 3D particle simulation system has been implemented in microtubule gliding assay simulation that utilize multiple GPUs to provide performance power to simulate up to millions of simulated particles [9][10][11]. The 3D simulation system in this research is derived from the particle simulation system reported in those studies.…”

Section: Introductionmentioning

confidence: 99%

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Tensegrity representation of microtubule objects using unified particle objects and springs

Pramudwiatmoko

Gutmann

Ueno

et al. 2020

CBIJ

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There are limitations in interactions with molecular objects in laboratory experiments due to the very small size of the objects. Common media to show the experimental results of molecular objects is still lack of observer interaction to understand it intuitively. In order to overcome this lack of interaction, this research takes tensegrity representation of molecular objects reproducing experimental results and creates interactive 3D objects to be presented in a virtual reality (VR) environment. The tensegrity representation enables us to enhance the interaction experience with the natural user interface with haptic technology and hand tracking controller. A particle simulation system that utilizes multiple GPUs resources is used to fulfill haptic VR requirements. We developed a unified particle object model using springs and particles which we call anchors which act as tensegrity structure of the object to support conformation of filament-type objects such as microtubules. Some object parameters can be set to match the flexural rigidity of the object with some experimental results. The bending shape of the object is evaluated using the classic bending equation and the results show high compatibility. Viscoelastic behavior also shows similarities with the viscosity reported in other studies. The object's flexural rigidity can be adjusted to match the target value with the direction of the prediction equation. The object model provides a better insight about molecular objects with natural and real-time interactions to provide a more intuitive understanding with the molecular objects presented. The results show that this model can also be applied to any filament-type or rod-like molecular object.

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“…The current study is among a few in the literature to use a swarm engineering approach to simulating MT self-assembly process in forming 2D sub-structures. The framework draws inspiration from the work of Gutmann et al [15] in simulating gliding behavior in MTs in a real-time 3D environment, where gliding is driven by motor proteins and forms dynamic rings and bundle structures. However, it involves millions of computations, posing a challenge to real-time simulations.…”

Section: Introductionmentioning

confidence: 99%

Mimicking Sub-Structures Self-Organization in Microtubules

Palaparthi

Pidaparti

2019

Biomimetics

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Microtubules (MTs) are highly dynamic polymers distributed in the cytoplasm of a biological cell. Alpha and beta globular proteins constituting the heterodimer building blocks combine to form these tubules through polymerization, controlled by the concentration of Guanosine-triphosphate (GTPs) and other Microtubule Associated Proteins (MAPs). MTs play a crucial role in many intracellular processes, predominantly in mitosis, organelle transport and cell locomotion. Current research in this area is focused on understanding the exclusive behaviors of self-organization and their association with different MAPs through organized laboratory experiments. However, the intriguing intelligence behind these tiny machines resulting in complex self-organizing structures is mostly unexplored. In this study, we propose a novel swarm engineering framework in modeling rules for these systems, by combining the principles of design with swarm intelligence. The proposed framework was simulated on a game engine and these simulations demonstrated self-organization of rings and protofilaments in MTs. Analytics from these simulations assisted in understanding the influence of GTPs on protofilament formation. Also, results showed that the population density of GTPs rather than their bonding probabilities played a crucial role in polymerization in forming microtubule substructures.

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Parallel Interaction Detection Algorithms for a Particle-based Live Controlled Real-time Microtubule Gliding Simulation System Accelerated by GPGPU

Cited by 6 publications

References 16 publications

Adaptation of Patterns of Motile Filaments under Dynamic Boundary Conditions

Adaptation of Patterns of Motile Filaments under Dynamic Boundary Conditions

Tensegrity representation of microtubule objects using unified particle objects and springs

Mimicking Sub-Structures Self-Organization in Microtubules

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