Particle simulation approach for subcellular dynamics and interactions of biological molecules

Azuma, Ryuzo; Kitagawa, T.; Kobayashi, Hiroshi; Konagaya, Akihiko

doi:10.1186/1471-2105-7-s4-s20

Cited by 13 publications

(11 citation statements)

References 30 publications

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“…In our method using Monte Carlo simulation of the dynamics and interaction of biological molecules [22], many kinds of molecules and many of each kind can exist at every site in the simulation lattice. The behaviors of these molecules is simulated by repeating two phases: migration and reaction.…”

Section: Molecular Behaviormentioning

confidence: 99%

“…We have done this in the development of our high-speed mesoscopic-level simulation method. Protein molecules are expressed as particles undergoing continuous Brownian motion [22].…”

Section: Preliminary Experimentationmentioning

confidence: 99%

“…We have developed a high-speed mesoscopiclevel method for clarifying cell dynamics that uses Monte Carlo simulation of biological molecule interactions [22,23]. The molecules are described as particles that take a random walk in 3-dimensional discrete space.…”

Section: Introductionmentioning

confidence: 99%

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Mesoscopic-level Simulation of Dynamics and Interactions of Biological Molecules Using Monte Carlo Simulation

Yamaguchi

Matsumoto

Azuma

et al. 2007

J VLSI Sign Process Syst Sign Im

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A mesoscopic-level method for clarifying living cell dynamics is described that uses Monte Carlo simulation of biological molecule interactions. The molecules are described as particles that take a random walk in 3-dimensional discrete space. Many kinds of molecules (including complex forms) are supported, so complex reactions with enzymes can be simulated. Also described is an field programmable gate array system with reconfigurable hardware that that will support complete modeling of an entire cell. Two-phase processing (migration and reaction) is used to simulate the complex reactions, so the method can be implemented in a limited amount of hardware. The migration and reaction circuits are deeply pipelined, resulting in high performance. Estimated performance is 30 times faster than with a 3.2-GHz Pentium 4 computer. This approach should make it possible to eventually simulate cell interactions involving one billion particles.

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Section: Molecular Behaviormentioning

confidence: 99%

“…We have done this in the development of our high-speed mesoscopic-level simulation method. Protein molecules are expressed as particles undergoing continuous Brownian motion [22].…”

Section: Preliminary Experimentationmentioning

confidence: 99%

See 1 more Smart Citation

Mesoscopic-level Simulation of Dynamics and Interactions of Biological Molecules Using Monte Carlo Simulation

Yamaguchi

Matsumoto

Azuma

et al. 2007

J VLSI Sign Process Syst Sign Im

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“…In this domain people utilize parameterized computation in the MCS study. Huang et al [27] derived a new lower bound for the exact algorithms of the maximum common induced subgraph. The authors also investigated the upper bound effects.…”

Section: Biological Function Analysismentioning

confidence: 99%

“…Azuma et al [27] focused on molecular-level dynamics to affect molecular properties at the cellular level. Based on a particle model, they designed an algorithm to simulate the chemical reaction-diffusion dynamics of molecules.…”

Section: Molecular Interaction and System Biologymentioning

confidence: 99%

Development of computations in bioscience and bioinformatics and its application: review of the Symposium of Computations in Bioinformatics and Bioscience (SCBB06)

Deng

Zhang

2006

BMC Bioinformatics

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The first symposium of computations in bioinformatics and bioscience (SCBB06) was held in Hangzhou, China on June 21–22, 2006. Twenty-six peer-reviewed papers were selected for publication in this special issue of BMC Bioinformatics. These papers cover a broad range of topics including bioinformatics theories, algorithms, applications and tool development. The main technical topics contain gene expression analysis, sequence analysis, genome analysis, phylogenetic analysis, gene function prediction, molecular interaction and system biology, genetics and population study, immune strategy, protein structure prediction and proteomics.

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Protein charge and mass contribute to the spatio‐temporal dynamics of protein–protein interactions in a minimal proteome

Wang

Nussinov³

et al. 2013

Proteomics

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We constructed and simulated a ‘minimal proteome’ model using Langevin dynamics. It contains 206 essential protein types which were compiled from the literature. For comparison, we generated six proteomes with randomized concentrations. We found that the net charges and molecular weights of the proteins in the minimal genome are not random. The net charge of a protein decreases linearly with molecular weight, with small proteins being mostly positively charged and large proteins negatively charged. The protein copy numbers in the minimal genome have the tendency to maximize the number of protein-protein interactions in the network. Negatively charged proteins which tend to have larger sizes can provide large collision cross-section allowing them to interact with other proteins; on the other hand, the smaller positively charged proteins could have higher diffusion speed and are more likely to collide with other proteins. Proteomes with random charge/mass populations form less stable clusters than those with experimental protein copy numbers. Our study suggests that ‘proper’ populations of negatively and positively charged proteins are important for maintaining a protein-protein interaction network in a proteome. It is interesting to note that the minimal genome model based on the charge and mass of E. Coli may have a larger protein-protein interaction network than that based on the lower organism M. pneumoniae.

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Particle simulation approach for subcellular dynamics and interactions of biological molecules

Cited by 13 publications

References 30 publications

Mesoscopic-level Simulation of Dynamics and Interactions of Biological Molecules Using Monte Carlo Simulation

Mesoscopic-level Simulation of Dynamics and Interactions of Biological Molecules Using Monte Carlo Simulation

Development of computations in bioscience and bioinformatics and its application: review of the Symposium of Computations in Bioinformatics and Bioscience (SCBB06)

Protein charge and mass contribute to the spatio‐temporal dynamics of protein–protein interactions in a minimal proteome

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