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Dive into the research topics where Kazuyuki Nakamura is active.

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Featured researches published by Kazuyuki Nakamura.


pacific symposium on biocomputing | 2008

PARAMETER ESTIMATION OF IN SILICO BIOLOGICAL PATHWAYS WITH PARTICLE FILTERING TOWARDS A PETASCALE COMPUTING

Kazuyuki Nakamura; Ryo Yoshida; Masao Nagasaki; Satoru Miyano; Tomoyuki Higuchi

The aim of this paper is to demonstrate the potential power of large-scale particle filtering for the parameter estimations of in silico biological pathways where time course measurements of biochemical reactions are observable. The method of particle filtering has been a popular technique in the field of statistical science, which approximates posterior distributions of model parameters of dynamic system by using sequentially-generated Monte Carlo samples. In order to apply the particle filtering to system identifications of biological pathways, it is often needed to explore the posterior distributions which are defined over an exceedingly high-dimensional parameter space. It is then essential to use a fairly large amount of Monte Carlo samples to obtain an approximation with a high-degree of accuracy. In this paper, we address some implementation issues on large-scale particle filtering, and then, indicate the importance of large-scale computing for parameter learning of in silico biological pathways. We have tested the ability of the particle filtering with 10(8) Monte Carlo samples on the transcription circuit of circadian clock that contains 45 unknown kinetic parameters. The proposed approach could reveal clearly the shape of the posterior distributions over the 45 dimensional parameter space.


PLOS ONE | 2012

A high-resolution shape fitting and simulation demonstrated equatorial cell surface softening during cytokinesis and its promotive role in cytokinesis.

Hiroshi Koyama; Tamiki Umeda; Kazuyuki Nakamura; Tomoyuki Higuchi; Akatsuki Kimura

Different models for animal cell cytokinesis posit that the stiffness of the equatorial cortex is either increased or decreased relative to the stiffness of the polar cortex. A recent work has suggested that the critical cytokinesis signaling complex centralspindlin may reduce the stiffness of the equatorial cortex by inactivating the small GTPase Rac. To determine if such a reduction occurs and if it depends on centralspindlin, we devised a method to estimate cortical bending stiffness with high spatio-temporal resolution from in vivo cell shapes. Using the early Caenorhabditis elegans embryo as a model, we show that the stiffness of the equatorial cell surface is reduced during cytokinesis, whereas the stiffness of the polar cell surface remains stiff. The equatorial reduction of stiffness was compromised in cells with a mutation in the gene encoding the ZEN-4/kinesin-6 subunit of centralspindlin. Theoretical modeling showed that the absence of the equatorial reduction of stiffness could explain the arrest of furrow ingression in the mutant. By contrast, the equatorial reduction of stiffness was sufficient to generate a cleavage furrow even without the constriction force of the contractile ring. In this regime, the contractile ring had a supportive contribution to furrow ingression. We conclude that stiffness is reduced around the equator in a centralspindlin-dependent manner. In addition, computational modeling suggests that proper regulation of stiffness could be sufficient for cleavage furrow ingression.


Frontiers in Physiology | 2015

Estimating cellular parameters through optimization procedures: elementary principles and applications

Akatsuki Kimura; Antonio Celani; Hiromichi Nagao; Timothy J. Stasevich; Kazuyuki Nakamura

Construction of quantitative models is a primary goal of quantitative biology, which aims to understand cellular and organismal phenomena in a quantitative manner. In this article, we introduce optimization procedures to search for parameters in a quantitative model that can reproduce experimental data. The aim of optimization is to minimize the sum of squared errors (SSE) in a prediction or to maximize likelihood. A (local) maximum of likelihood or (local) minimum of the SSE can efficiently be identified using gradient approaches. Addition of a stochastic process enables us to identify the global maximum/minimum without becoming trapped in local maxima/minima. Sampling approaches take advantage of increasing computational power to test numerous sets of parameters in order to determine the optimum set. By combining Bayesian inference with gradient or sampling approaches, we can estimate both the optimum parameters and the form of the likelihood function related to the parameters. Finally, we introduce four examples of research that utilize parameter optimization to obtain biological insights from quantified data: transcriptional regulation, bacterial chemotaxis, morphogenesis, and cell cycle regulation. With practical knowledge of parameter optimization, cell and developmental biologists can develop realistic models that reproduce their observations and thus, obtain mechanistic insights into phenomena of interest.


Journal of Statistical Computation and Simulation | 2014

Rapid detection of the switching point in a financial market structure using the particle filter

Yoshihiro Yura; Hideki Takayasu; Kazuyuki Nakamura; Misako Takayasu

We apply the particle filter for the quick and accurate estimation of a switching point in a financial market based on a recently developed theoretical model, the potentials of unbalanced complex kinetics (PUCK) model, which fulfils all empirically stylized facts such as fat-tailed distribution of price changes and the anomalous diffusion in a short-time scale. We show the efficiency of an optimized driving force in particle filtering for the estimation of the parameters of the PUCK model, using a simulation study. As an example, we apply the method to the dollar–yen exchange market before and after the biggest earthquake in Japan in March 2011. With this fast and efficient estimation method, we can clearly confirm that the statistics of the time series of exchange rate changed drastically at the time of the arrival of the quake in Tokyo area, implying that the earthquake worked as a trigger for the markets switching point.


Archive | 2009

Particle Filtering in Data Assimilation and Its Application to Estimation of Boundary Condition of Tsunami Simulation Model

Kazuyuki Nakamura; Naoki Hirose; Byung Ho Choi; Tomoyuki Higuchi

We discuss the merit of application of the particle filter compared with the ensemble Kalman filter in data assimilation, as well as its application to tsunami simulation model. The particle filter is one of the ensemble-based methods and is similar to the ensemble Kalman filter that is widely used in sequential data assimilation. We discuss the pros and cons through numerical experiments when the particle filter is used in data assimilation. In next, we review the framework of bottom topography correction based on the tide gauge data. In this procedure, the particle filter was employed to assimilate the tide gauge data, and special localization was used for parameterization. We previously showed the validity of the methods in terms of both attenuation of degeneracy problem and the effectiveness of estimation. We also showed the analysis result of the depth of Yamato Rises in that work. However, the analysis result itself was not sufficiently validated. To validate the analyzed result, we show the result of twin experiment based on artificial bottom topography in this paper. The result fortifies effectiveness of the introduced method for correcting the depth of rise. It also supplements the result of the previous analysis in the Japan Sea.


soft computing | 2012

Extraction of groove feelings from drum data using non-negative matrix factorization

Yoshito Ohya; Kazuyuki Nakamura; Terumasa Tokunaga

In this paper, we propose the algorithm to extract the groove feeling from drum data. In the previous researches, extraction of the groove feeling requires pre-separated acoustic sources. We employed non-negative matrix factorization (NMF) to make separated information on hitting time from monaural wave data in which multiple acoustic sources are mixed. We applied our algorithm to a drum data and obtained a difference of time fluctuations among instruments, which relates to groove feeling and impression. The result implies that the proposed algorithm can extract some sensitive differences of nuance of drumming.


IEEE Transactions on Signal Processing | 2007

A Recursive Recomputation Approach for Smoothing in Nonlinear State–Space Modeling: An Attempt for Reducing Space Complexity

Kazuyuki Nakamura; Takashi Tsuchiya

In this paper, we develop a new generic implementation scheme for numerical smoothing in nonlinear and Bayesian state-space modeling. Our new generic implementation scheme, which we call recursive recomputation scheme, reduces the space complexity from O(MT) to O(M log T), at the cost of O(log T) times computation of filtering distributions in time complexity. This reduction is accomplished by employing carefully designed recursive recomputation. The Japanese stock market price time-series data with T = 956 is taken up as an instance to demonstrate advantage of the proposed scheme. The path-sampling particle smoother is implemented with the scheme to smooth the whole interval estimating the change of volatility. The number of particles is 3 000 000, and the whole interval is smoothed with 5.3-GB storage, accomplishing saving of storage by a factor of 1/20. The computed smoothing distribution is compared with the ones computed with the existing two other well-known smoothers, the forward-backward smoother and the smoother based on two-filter formula. It turns out that, among the three, ours is the only method which succeeded in computing a reliable and plausible smoothing distribution in the situation.


Chaos | 2016

Local noise sensitivity: Insight into the noise effect on chaotic dynamics

Nina Sviridova; Kazuyuki Nakamura

Noise contamination in experimental data with underlying chaotic dynamics is one of the significant problems limiting the application of many nonlinear time series analysis methods. Although numerous studies have been devoted to the investigation of different aspects of noise-nonlinear dynamics interactions, the effects produced by noise on chaotic dynamics are not fully understood. This study sought to analyze the local effects produced by noise on chaotic dynamics with a smooth attractor. Local Wayland test translation errors were calculated for noise-induced Lorenz and Rössler chaotic models, and for experimental green light photoplethysmogram data. Results demonstrated that under noise induction, local regions on the chaotic attractor with high values of local translation error can be observed. This phenomenon was defined as the local noise sensitivity. It was found that for both models, local noise-sensitive regions were located close to the systems equilibrium points. Additionally, it was found that the reconstructed dynamics represent well the local noise sensitivity of the original dynamics. The concept of local noise sensitivity is expected to contribute to various applied studies, as it reveals regions of chaotic attractors that are sensitive to the presence of noise.


Soils and Foundations | 2012

Parameter identification for Cam-clay model in partial loading model tests using the particle filter

Takayuki Shuku; Akira Murakami; S. Nishimura; Kazunori Fujisawa; Kazuyuki Nakamura


Journal of Universal Computer Science | 2006

Sequential Data Assimilation : Information Fusion of a Numerical Simulation and Large Scale Observation Data

Kazuyuki Nakamura; Tomoyuki Higuchi; Naoki Hirose

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Akatsuki Kimura

National Institute of Genetics

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