Faculty Profiles - KURATA Hiroyuki

Papers - KURATA Hiroyuki

Division display21 - 40 of about 251／ All the affair displays >>

Prednts: Improved and robust prediction of nitrotyrosine sites by integrating multiple sequence features Reviewed

Nilamyani A.N., Auliah F.N., Moni M.A., Shoombuatong W., Hasan M.M., Kurata H.

International Journal of Molecular Sciences 22 ( 5 ) 1 - 11 2021.03

IRC-Fuse: improved and robust prediction of redox-sensitive cysteine by fusing of multiple feature representations Reviewed

Hasan M.M., Alam M.A., Shoombuatong W., Kurata H.

Journal of Computer-Aided Molecular Design 35 ( 3 ) 315 - 323 2021.03

Virtual metabolic human dynamic model for pathological analysis and therapy design for diabetes Reviewed

Kurata H.

iScience 24 ( 2 ) 2021.02

Pup-fuse: Prediction of protein pupylation sites by integrating multiple sequence representations Reviewed

Auliah F.N., Nilamyani A.N., Shoombuatong W., Alam M.A., Hasan M.M., Kurata H.

International Journal of Molecular Sciences 22 ( 4 ) 1 - 12 2021.02

iLBE for Computational Identification of Linear B-cell Epitopes by Integrating Sequence and Evolutionary Features Reviewed

Hasan M.M., Khatun M.S., Kurata H.

Genomics, Proteomics and Bioinformatics 2021.01

RCGAToolbox: A Real-coded Genetic Algorithm Software for Parameter Estimation of Kinetic Models Reviewed

Maeda K., Boogerd F.C., Kurata H.

IPSJ Transactions on Bioinformatics 14 30 - 35 2021.01

Language：English Publishing type：Research paper (scientific journal)

Kinetic modeling is essential for understanding the dynamic behavior of biochemical networks, such as metabolic and signal transduction pathways. However, parameter estimation remains a major bottleneck in kinetic modeling. Although several software tools have been developed to address this issue, they are meant to be used by experts, and their lack of user-friendliness hampers their general usage by capable yet inexperienced scientists. One of the difficulties is the lack of graphical user interfaces (GUIs), which means that users must learn how to write scripts for parameter estimation. In this study, we present RCGAToolbox, a user-friendly parameter estimation software that provides real-coded genetic algorithms (RCGAs). The RCGAToolbox has two RCGAs: the unimodal normal distribution crossover with minimal generation gap (UNDX/MGG) and the real-coded ensemble crossover star with just generation gap (REXstar/JGG). To facilitate parameter estimation, RCGAToolbox offers straightforward access to powerful RCGAs, such as GUIs, an easy-to-use installer, and a comprehensive user guide. Moreover, the RCGAToolbox supports systems biology standards for better usability and interoperability. The RCGAToolbox is available at https://github.com/kmaeda16/RCGAToolbox under GNU GPLv3, along with the user guide and application examples. The RCGAToolbox runs on MATLAB (R2016a or later) in Windows, Linux, and Mac.

DOI： 10.2197/IPSJTBIO.14.30

Scopus

Other Link： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85122021287&origin=inward

Robust in-phase synchronization in repressor-based coupled gene oscillators Reviewed

Shamim Ul Hasan A.B.M., Dey S., Kurata H., Singh A.

IFAC-PapersOnLine 54 ( 15 ) 574 - 579 2021.01

RCGAToolbox: A Real-coded Genetic Algorithm Software for Parameter Estimation of Kinetic Models Reviewed

Maeda Kazuhiro, Boogerd Fred C., Kurata Hiroyuki

IPSJ Transactions on Bioinformatics 14 ( 0 ) 30 - 35 2021.01

Coupling protocol of interlocked feedback oscillators in circadian clocks Reviewed

Rashid M.M., Kurata H.

Journal of the Royal Society, Interface 17 ( 167 ) 2020.06

Computer-Aided Rational Design of Efficient NADPH Production System by Escherichia coli pgi Mutant Using a Mixture of Glucose and Xylose Reviewed

Matsuoka Y., Kurata H.

Frontiers in Bioengineering and Biotechnology 8 2020.04

Recent development of machine learning methods in microbial phos-phorylation sites Reviewed

Rashid M.M., Shatabda S., Hasan M.M., Kurata H.

Current Genomics 21 ( 3 ) 194 - 203 2020.01

i4mC-Mouse: Improved identification of DNA N4-methylcytosine sites in the mouse genome using multiple encoding schemes Reviewed

Hasan M.M., Manavalan B., Shoombuatong W., Khatun M.S., Kurata H.

Computational and Structural Biotechnology Journal 18 906 - 912 2020.01

A prediction model of functional outcome at 6 months using clinical findings of a person with traumatic spinal cord injury at 1 month after injury Reviewed

Ariji Y., Hayashi T., Ideta R., Koga R., Murai S., Towatari F., Terashi Y., Sakai H., Kurata H., Maeda T.

Spinal Cord 2020.01

ヒト全身代謝システムの数理モデル Invited Reviewed

倉田博之

細胞 52 376 - 379 2020.01

i6mA-Fuse: improved and robust prediction of DNA 6 mA sites in the Rosaceae genome by fusing multiple feature representation Reviewed

Hasan M.M., Manavalan B., Shoombuatong W., Khatun M.S., Kurata H.

Plant Molecular Biology 2020.01

ProIn-Fuse: improved and robust prediction of proinflammatory peptides by fusing of multiple feature representations Reviewed

Khatun M.S., Hasan M.M., Shoombuatong W., Kurata H.

Journal of Computer-Aided Molecular Design 2020.01

Evolution of sequence-based bioinformatics tools for protein-protein interaction prediction Reviewed

Khatun M.S., Shoombuatong W., Hasan M.M., Kurata H.

Current Genomics 21 ( 6 ) 454 - 463 2020.01

Authorship：Corresponding author Language：English Publishing type：Article, review, commentary, editorial, etc. (scientific journal)

© 2020 Bentham Science Publishers. Protein-protein interactions (PPIs) are the physical connections between two or more proteins via electrostatic forces or hydrophobic effects. Identification of the PPIs is pivotal, which contributes to many biological processes including protein function, disease incidence, and therapy design. The experimental identification of PPIs via high-throughput technology is time-consuming and expensive. Bioinformatics approaches are expected to solve such restrictions. In this review, our main goal is to provide an inclusive view of the existing sequence-based computational prediction of PPIs. Initially, we briefly introduce the currently available PPI databases and then review the state-of-the-art bioinformatics approaches, working principles, and their performances. Finally, we discuss the caveats and future perspective of the next generation algorithms for the prediction of PPIs.

DOI： 10.2174/1389202921999200625103936

Scopus

Other Link： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85089875758&origin=inward

Improvement of the memory function of a mutual repression network in a stochastic environment by negative autoregulation Reviewed International journal

Hasan A., Kurata H., Pechmann S.

BMC Bioinformatics 20 ( 1 ) 2019.12

Authorship：Corresponding author Language：English Publishing type：Research paper (scientific journal)

© 2019 The Author(s). Background: Cellular memory is a ubiquitous function of biological systems. By generating a sustained response to a transient inductive stimulus, often due to bistability, memory is central to the robust control of many important biological processes. However, our understanding of the origins of cellular memory remains incomplete. Stochastic fluctuations that are inherent to most biological systems have been shown to hamper memory function. Yet, how stochasticity changes the behavior of genetic circuits is generally not clear from a deterministic analysis of the network alone. Here, we apply deterministic rate equations, stochastic simulations, and theoretical analyses of Fokker-Planck equations to investigate how intrinsic noise affects the memory function in a mutual repression network. Results: We find that the addition of negative autoregulation improves the persistence of memory in a small gene regulatory network by reducing stochastic fluctuations. Our theoretical analyses reveal that this improved memory function stems from an increased stability of the steady states of the system. Moreover, we show how the tuning of critical network parameters can further enhance memory. Conclusions: Our work illuminates the power of stochastic and theoretical approaches to understanding biological circuits, and the importance of considering stochasticity when designing synthetic circuits with memory function.

DOI： 10.1186/s12859-019-3315-2

Kyutacar

Scopus

Other Link： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85077279648&origin=inward

Computational identification of microbial phosphorylation sites by the enhanced characteristics of sequence information Reviewed International journal

Hasan M., Rashid M., Khatun M., Kurata H.

Scientific Reports 9 ( 1 ) 2019.12

Authorship：Corresponding author Language：English Publishing type：Research paper (scientific journal)

© 2019, The Author(s). Protein phosphorylation on serine (S) and threonine (T) has emerged as a key device in the control of many biological processes. Recently phosphorylation in microbial organisms has attracted much attention for its critical roles in various cellular processes such as cell growth and cell division. Here a novel machine learning predictor, MPSite (Microbial Phosphorylation Site predictor), was developed to identify microbial phosphorylation sites using the enhanced characteristics of sequence features. The final feature vectors optimized via a Wilcoxon rank sum test. A random forest classifier was then trained using the optimum features to build the predictor. Benchmarking investigation using the 5-fold cross-validation and independent datasets test showed that the MPSite is able to achieve robust performance on the S- and T-phosphorylation site prediction. It also outperformed other existing methods on the comprehensive independent datasets. We anticipate that the MPSite is a powerful tool for proteome-wide prediction of microbial phosphorylation sites and facilitates hypothesis-driven functional interrogation of phosphorylation proteins. A web application with the curated datasets is freely available at http://kurata14.bio.kyutech.ac.jp/MPSite/.

DOI： 10.1038/s41598-019-44548-x

Scopus

Other Link： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85066627531&origin=inward

Ranking network mechanisms by how they fit diverse experiments and deciding on E. coli's ammonium transport and assimilation network Reviewed International journal

Maeda K., Westerhoff H., Kurata H., Boogerd F.

npj Systems Biology and Applications 5 ( 1 ) 2019.12

Authorship：Corresponding author Language：English Publishing type：Research paper (scientific journal)

© 2019, The Author(s). The complex ammonium transport and assimilation network of E. coli involves the ammonium transporter AmtB, the regulatory proteins GlnK and GlnB, and the central N-assimilating enzymes together with their highly complex interactions. The engineering and modelling of such a complex network seem impossible because functioning depends critically on a gamut of data known at patchy accuracy. We developed a way out of this predicament, which employs: (i) a constrained optimization-based technology for the simultaneous fitting of models to heterogeneous experimental data sets gathered through diverse experimental set-ups, (ii) a ‘rubber band method’ to deal with different degrees of uncertainty, both in experimentally determined or estimated parameter values and in measured transient or steady-state variables (training data sets), (iii) integration of human expertise to decide on accuracies of both parameters and variables, (iv) massive computation employing a fast algorithm and a supercomputer, (v) an objective way of quantifying the plausibility of models, which makes it possible to decide which model is the best and how much better that model is than the others. We applied the new technology to the ammonium transport and assimilation network, integrating recent and older data of various accuracies, from different expert laboratories. The kinetic model objectively ranked best, has E. coli's AmtB as an active transporter of ammonia to be assimilated with GlnK minimizing the futile cycling that is an inevitable consequence of intracellular ammonium accumulation. It is 130 times better than a model with facilitated passive transport of ammonia.

DOI： 10.1038/s41540-019-0091-6

Kyutacar

Scopus

Other Link： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85064343332&origin=inward

　 PREV - NEXT 　

<KURATA Hiroyuki>

Papers - KURATA Hiroyuki