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

DOI： 10.1080/10790268.2022.2047548

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DOI： 10.3390/ijms24087296

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DOI： 10.1371/journal.pone.0276609

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

DOI： 10.2174/0929867328666210804090224

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DOI： 10.1016/j.csbj.2022.10.012

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Neuropeptides (NPs) are the most versatile neurotransmitters in the immune systems that regulate various central anxious hormones. An efficient and effective bioinformatics tool for rapid and accurate large-scale identification of NPs is critical in immunoinformatics, which is indispensable for basic research and drug development. Although a few NP prediction tools have been developed, it is mandatory to improve their NPs' prediction performances. In this study, we have developed a machine learning-based meta-predictor called NeuroPred-FRL by employing the feature representation learning approach. First, we generated 66 optimal baseline models by employing 11 different encodings, six different classifiers and a two-step feature selection approach. The predicted probability scores of NPs based on the 66 baseline models were combined to be deemed as the input feature vector. Second, in order to enhance the feature representation ability, we applied the two-step feature selection approach to optimize the 66-D probability feature vector and then inputted the optimal one into a random forest classifier for the final meta-model (NeuroPred-FRL) construction. Benchmarking experiments based on both cross-validation and independent tests indicate that the NeuroPred-FRL achieves a superior prediction performance of NPs compared with the other state-of-the-art predictors. We believe that the proposed NeuroPred-FRL can serve as a powerful tool for large-scale identification of NPs, facilitating the characterization of their functional mechanisms and expediting their applications in clinical therapy. Moreover, we interpreted some model mechanisms of NeuroPred-FRL by leveraging the robust SHapley Additive exPlanation algorithm.

DOI： 10.1093/bib/bbab167

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

DOI： 10.1093/bib/bbab228

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DOI： 10.1093/bfgp/elaa028

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DOI： 10.1093/bib/bbaa202

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

DOI： 10.3390/ijms22052704

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

DOI： 10.1007/s10822-020-00368-0

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DOI： 10.1016/j.isci.2021.102101

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DOI： 10.3390/ijms22042120

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DOI： 10.1016/j.gpb.2019.04.004

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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

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

DOI： 10.1016/j.ifacol.2021.10.318

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

DOI： 10.2197/ipsjtbio.14.30

CiNii Article

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

DOI： 10.1098/rsif.2020.0287

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DOI： 10.3389/fbioe.2020.00277

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

DOI： 10.2174/1389202921666200427210833

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

DOI： 10.1016/j.csbj.2020.04.001

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

DOI： 10.1038/s41393-020-0488-5

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

CiNii Research

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

DOI： 10.1007/s11103-020-00988-y

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© 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

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DOI： 10.1007/s10822-020-00343-9

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© 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

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© 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

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© 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

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© 2019, The Author(s). Many metabolic cycles, including the tricarboxylic acid cycle, glyoxylate cycle, Calvin cycle, urea cycle, coenzyme recycling, and substrate cycles, are well known to catabolize and anabolize different metabolites for efficient energy and mass conversion. In terms of stoichiometric structure, this study explicitly identifies two types of metabolic cycles. One is the well-known, elementary cycle that converts multiple substrates into different products and recycles one of the products as a substrate, where the recycled substrate is supplied from the outside to run the cycle. The other is the self-replenishment cycle that merges multiple substrates into two or multiple identical products and reuses one of the products as a substrate. The substrates are autonomously supplied within the cycle. This study first defines the self-replenishment cycles that many scientists have overlooked despite its functional importance. Theoretical analysis has revealed the design principle of the self-replenishment cycle that presents a threshold response without any bistability nor cooperativity. To verify the principle, three detailed kinetic models of self-replenishment cycles embedded in an E. coli metabolic system were simulated. They presented the threshold response or digital switch-like function that steeply shift metabolic status.

DOI： 10.1038/s41598-019-53589-1

Kyutacar

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© 2019 Federation of European Biochemical Societies Tuberculosis (TB) is a leading killer caused by Mycobacterium tuberculosis. Recently, anti-TB peptides have provided an alternative approach to combat antibiotic tolerance. We have developed an effective computational predictor, identification of antitubercular peptides (iAntiTB), by the integration of multiple feature vectors deriving from the amino acid sequences via random forest (RF) and support vector machine (SVM) classifiers. The iAntiTB combines the RF and SVM scores via linear regression to enhance the prediction accuracy. To make a robust and accurate predictor, we prepared the two datasets with different types of negative samples. The iAntiTB achieved area under the ROC curve values of 0.896 and 0.946 on the training datasets of the first and second datasets, respectively. The iAntiTB outperformed the other existing predictors.

DOI： 10.1002/1873-3468.13536

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DOI： 10.3390/cells8020095

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© 2019 The Royal Society of Chemistry. Cysteine S-nitrosylation is a type of reversible post-translational modification of proteins, which controls diverse biological processes. It is associated with redox-based cellular signaling to protect against oxidative stress. The identification of S-nitrosylation sites is an important step to reveal the function of proteins; however, experimental identification of S-nitrosylation is expensive and time-consuming work. Hence, sequence-based computational prediction of potential S-nitrosylation sites is highly sought before experimentation. Herein, a novel predictor PreSNO has been developed that integrates multiple encoding schemes by the support vector machine and random forest algorithms. The PreSNO achieved an accuracy and Matthews correlation coefficient value of 0.752 and 0.252 respectively in classifying between SNO and non-SNO sites when evaluated on the independent dataset, outperforming the existing methods. The web application of the PreSNO and its associated datasets are freely available at http://kurata14.bio.kyutech.ac.jp/PreSNO/.

DOI： 10.1039/c9mo00098d

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© 2018 Elsevier B.V. One of the most important epigenetic modifications is N4-methylcytosine, which regulates many biological processes including DNA replication and chromosome stability. Identification of N4-methylcytosine sites is pivotal to understand specific biological functions. Herein, we developed the first bioinformatics tool called i4mC-ROSE for identifying N4-methylcytosine sites in the genomes of Fragaria vesca and Rosa chinensis in the Rosaceae, which utilizes a random forest classifier with six encoding methods that cover various aspects of DNA sequence information. The i4mC-ROSE predictor achieves area under the curve scores of 0.883 and 0.889 for the two genomes during cross-validation. Moreover, the i4mC-ROSE outperforms other classifiers tested in this study when objectively evaluated on the independent datasets. The proposed i4mC-ROSE tool can serve users' demand for the prediction of 4mC sites in the Rosaceae genome. The i4mC-ROSE predictor and utilized datasets are publicly accessible at http://kurata14.bio.kyutech.ac.jp/i4mC-ROSE/.

DOI： 10.1016/j.ijbiomac.2019.12.009

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© 2019 Khatun, Hasan and Kurata. Numerous inflammatory diseases and autoimmune disorders by therapeutic peptides have received substantial consideration; however, the exploration of anti-inflammatory peptides via biological experiments is often a time-consuming and expensive task. The development of novel in silico predictors is desired to classify potential anti-inflammatory peptides prior to in vitro investigation. Herein, an accurate predictor, called PreAIP (Predictor of Anti-Inflammatory Peptides) was developed by integrating multiple complementary features. We systematically investigated different types of features including primary sequence, evolutionary and structural information through a random forest classifier. The final PreAIP model achieved an AUC value of 0.833 in the training dataset via 10-fold cross-validation test, which was better than that of existing models. Moreover, we assessed the performance of the PreAIP with an AUC value of 0.840 on a test dataset to demonstrate that the proposed method outperformed the two existing methods. These results indicated that the PreAIP is an accurate predictor for identifying AIPs and contributes to the development of AIPs therapeutics and biomedical research. The curated datasets and the PreAIP are freely available at http://kurata14.bio.kyutech.ac.jp/PreAIP/.

DOI： 10.3389/fgene.2019.00129

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© 2018 IEEE. Zea mays (maize) is one of the most vital crops which are grown widely in the world. To understand the molecular structures and functions of maize, the identification of protein-protein interaction (PPI) is very important. PPI identification by wet lab experiments is time-consuming, expensive and laborious. These days in silico methods that accurately predict potential PPIs based on protein sequence information are highly demanded. Research on PPI prediction in maize is currently very limited, and no dedicated bioinformatics schemes are available. In this work, we proposed a novel approach, termed SIPMA (Systematic Identification of PPI in Maize using Autocorrelation). A machine learning random forest classifier was trained with autocorrelation features to build the prediction model. The SIPMA, which was tested by the experimentally verified PPI dataset of maize, yielded a prediction accuracy of 0.899 when the specificity was 0.969 on the training set. The SIPMA achieved promising performances on the test datasets. Compared with different sequence-based encoding and statistical learning methods, the SIPMA was a powerful computational resource for identifying PPIs in maize.

DOI： 10.1109/BIBE.2018.00030

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© 2018 IEEE. Lysine malonylation is a newly discovered post-translational modification of proteins, which plays an important role in regulating many cellular fonctions. Several approaches are available to identify malonylation proteins and its malonylation sites, however; experimental identification of malonylation sites is often laborious and costly. Therefore, computational schemes are needed to identify potential malonylation sites prior to in vitro experimentation. In this paper, a novel computational scheme iLMS (Identification of Lysine-Malonylation Sites) has been developed by combining primary sequences and evolutionary features via a support vector machine classifier. The final iLMS scheme achieved a robust performance in cross-validation test in both human and mouse datasets. For the mouse data, the iLMS predictor outperformed other existing implementations. The iLMS is a promising computational scheme for the prediction of malonylation sites.

DOI： 10.1109/BIBE.2018.00077

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© 2018 Hasan, Kurata. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Lysine succinylation is one of the dominant post-translational modification of the protein that contributes to many biological processes including cell cycle, growth and signal transduction pathways. Identification of succinylation sites is an important step for understanding the function of proteins. The complicated sequence patterns of protein succinylation revealed by proteomic studies highlight the necessity of developing effective species-specific in silico strategies for global prediction succinylation sites. Here we have developed the generic and nine speciesspecific succinylation site classifiers through aggregating multiple complementary features. We optimized the consecutive features using the Wilcoxon-rank feature selection scheme. The final feature vectors were trained by a random forest (RF) classifier. With an integration of RF scores via logistic regression, the resulting predictor termed GPSuc achieved better performance than other existing generic and species-specific succinylation site predictors. To reveal the mechanism of succinylation and assist hypothesis-driven experimental design, our predictor serves as a valuable resource. To provide a promising performance in large-scale datasets, a web application was developed at http://kurata14.bio.kyutech.ac.jp/GPSuc/.

DOI： 10.1371/journal.pone.0200283

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

DOI： 10.4172/2332-0737.1000137

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

DOI： 10.18632/oncoscience.411

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

© 2017 Elsevier Ltd Oscillatory phenomena play a major role in organisms. In some biological oscillations such as cell cycles and heartbeats, the period can be tuned without significant changes in the amplitude. This property is called (period) tunability, one of the prominent features of biological oscillations. However, how biological oscillators produce tunable oscillations remains largely unexplored. We tackle this question using computational experiments. It has been reported that positive-plus-negative feedback oscillators produce tunable oscillations through the hysteresis-based mechanism. First, in this study, we confirmed that positive-plus-negative feedback oscillators generate tunable oscillations. Second, we found that tunability is positively correlated with the dynamic range of oscillations. Third, we showed that long negative feedback oscillators without any additional positive feedback loops can produce tunable oscillations. Finally, we computationally demonstrated that by lengthening the negative feedback loop, the Repressilator, known as a non-tunable synthetic gene oscillator, can be converted into a tunable oscillator. This work provides synthetic biologists with clues to design tunable gene oscillators.

DOI： 10.1016/j.jtbi.2017.12.014

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© 2017 The Society for Biotechnology, Japan Many kinetic models of Escherichia coli central metabolism have been built, but few models accurately reproduced the dynamic behaviors of wild type and multiple genetic mutants. In 2016, our latest kinetic model improved problems of existing models to reproduce the cell growth and glucose uptake of wild type, ΔpykA:pykF and Δpgi in a batch culture, while it overestimated the glucose uptake and cell growth rates of Δppc and hardly captured the typical characteristics of the glyoxylate and TCA cycle fluxes for Δpgi and Δppc. Such discrepancies between the simulated and experimental data suggested biological complexity. In this study, we overcame these problems by assuming critical mechanisms regarding the OAA-regulated isocitrate dehydrogenase activity, aceBAK gene regulation and growth suppression. The present model accurately predicts the extracellular and intracellular dynamics of wild type and many gene knockout mutants in batch and continuous cultures. It is now the most accurate, detailed kinetic model of E. coli central carbon metabolism and will contribute to advances in mathematical modeling of cell factories.

DOI： 10.1016/j.jbiosc.2017.09.005

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DOI： 10.2197/ipsjtbio.11.31

CiNii Article

Other Link： https://ci.nii.ac.jp/naid/130007483197

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

© 2018 Bentham Science Publishers. Background: Cysteine S-sulfenylation is a major type of dynamic post-translational modification of the protein that plays an important role in regulating many biological processes in both of prokaryotic and eukaryotic species. To understand the function of S-sulfenylated proteins, identification of S-sulfenylation sites is an essential step. Due to numerous restrictions of experimental methods, computational prediction of the potential S-sulfenylation sites becomes popular. In this review, we discuss the recent development and challenges in protein S-sulfenylation site prediction from the available datasets, algorithms and accessible services. We also demonstrate the encountered limitation and future perspective of the computational prediction tools. Conclusion: The development of S-sulfenylation site prediction and their application is an emerging field of protein bioinformatics research. Accurate predictors are expected to identify general and species-specific S-sulfenylation sites when more experimental annotation data are available. Combining experimental and computational technologies will definitely accelerate an understanding of protein S-sulfenylation, discovering regulatory networks in living organisms.

DOI： 10.2174/0929866525666180905110619

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

DOI： 10.1186/s13068-017-0867-0

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© 2017 Elsevier LtdBiological memory is a ubiquitous function that can generate a sustained response to a transient inductive stimulus. To better understand this function, we must consider the mechanisms by which different structures of genetic networks achieve memory. Here, we investigated two competitive gene regulatory network models: the regulated mutual activation network (MAN) and the regulated mutual repression network (MRN). Stochasticity deteriorated the persistence of memory of both the MAN and the MRN. Mathematical comparison by simulation and theoretical analysis identified functional differences in the stochastic memory between the competitive models: specifically, the MAN provided much more robust, persistent memory than the MRN. The stochastic persistent memory pattern of the MAN can be adjusted by changing the binding strength of the activators, whereas the MRN required highly cooperative and strong binding repressors for robust memory.

DOI： 10.1016/j.jtbi.2017.05.036

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© 2017 Masunaga et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.The ErbB receptor signaling pathway plays an important role in the regulation of cellular proliferation, survival and differentiation, and dysregulation of the pathway is linked to various types of human cancer. Mathematical models have been developed as a practical complementary approach to deciphering the complexity of ErbB receptor signaling and elucidating how the pathways discriminate between ligands to induce different cell fates. In this study, we developed a simulator to accurately calculate the dynamic sensitivity of extracellular-signal-regulated kinase (ERK) activity (ERK∗) and Akt activity (Akt∗), downstream of the ErbB receptors stimulated with epidermal growth factor (EGF) and heregulin (HRG). To demonstrate the feasibility of this simulator, we estimated how the reactions critically responsible for ERK∗ and Akt∗ change with time and in response to different doses of EGF and HRG, and predicted that only a small number of reactions determine ERK∗ and Akt∗. ERK∗ increased steeply with increasing HRG dose until saturation, while showing a gently rising response to EGF. Akt∗ had a gradual wide-range response to HRG and a blunt response to EGF. Akt∗ was sensitive to perturbations of intracellular kinetics, while ERK∗ was more robust due to multiple, negative feedback loops. Overall, the simulator predicted reactions that were critically responsible for ERK∗ and Akt∗ in response to the dose of EGF and HRG, illustrated the response characteristics of ERK∗ and Akt∗, and estimated mechanisms for generating robustness in the ErbB signaling network.

DOI： 10.1371/journal.pone.0178250

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© 2017 The Royal Society of Chemistry. Cysteine S-sulfenylation is a major type of posttranslational modification that contributes to protein structure and function regulation in many cellular processes. Experimental identification of S-sulfenylation sites is challenging, due to the low abundance of proteins and the inefficient experimental methods. Computational identification of S-sulfenylation sites is an alternative strategy to annotate the S-sulfenylated proteome. In this study, a novel computational predictor SulCysSite was developed for accurate prediction of S-sulfenylation sites based on multiple sequence features, including amino acid index properties, binary amino acid codes, position specific scoring matrix, and compositions of profile-based amino acids. To learn the prediction model of SulCysSite, a random forest classifier was applied. The final SulCysSite achieved an AUC value of 0.819 in a 10-fold cross-validation test. It also exhibited higher performance than other existing computational predictors. In addition, the hidden and complex mechanisms were extracted from the predictive model of SulCysSite to investigate the understandable rules (i.e. feature combination) of S-sulfenylation sites. The SulCysSite is a useful computational resource for prediction of S-sulfenylation sites. The online interface and datasets are publicly available at http://kurata14.bio.kyutech.ac.jp/SulCysSite/.

DOI： 10.1039/c7mb00491e

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© 2016, Spandidos Publications. All rights reserved. Aurora kinase B (AURKB) inhibitors are regarded as potential molecular-targeting drugs for cancer therapy. The present study evaluated the cytotoxic effect of a combination of AZD1152-hQPA, an AURKB inhibitor, and various anticancer agents on the HeLa human cervical cancer cell line, as well as its cisplatin-resistant equivalent HCP4 cell line. It was demonstrated that AZD1152-hQPA had an antagonistic effect on the cytotoxicity of cisplatin, etoposide and doxorubicin, but had a synergistic effect on that of all-trans-retinoic acid (ATRA), Am80 and TAC-101, when tested on HeLa cells. Cisplatin, etoposide and doxorubicin were shown to increase the cellular expression of AURKB, while ATRA, Am80 and TAC-101 downregulated its expression. These results suggested that AURKB expression is regulated by these anticancer agents at the transcriptional level, and that the level of expression of AURKB may influence the cytotoxic effect of AZD1152-hQPA. Therefore, when using anticancer agents, decreasing the expression of AURKB using a molecular-targeting drug may be an optimal therapeutic strategy.

DOI： 10.3892/ol.2016.5156

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© 2015 The Society for Biotechnology, Japan. Chinese hamster ovary (CHO) cells are commonly used as the host cell lines concerning their ability to produce therapeutic proteins with complex post-translational modifications. In this study, we have investigated the time course extra- and intracellular metabolome data of the CHO-K1 cell line, under a control and stress conditions. The addition of NaCl and trehalose greatly suppressed cell growth, where the maximum viable cell density of NaCl and trehalose cultures were 2.2-fold and 2.8-fold less than that of a control culture. Contrariwise, the antibody production of both the NaCl and trehalose cultures was sustained for a longer time to surpass that of the control culture. The NaCl and trehalose cultures showed relatively similar dynamics of cell growth, antibody production, and substrate/product concentrations, while they indicated different dynamics from the control culture. The principal component analysis of extra- and intracellular metabolome dynamics indicated that their dynamic behaviors were consistent with biological functions. The qualitative pattern matching classification and hierarchical clustering analyses for the intracellular metabolome identified the metabolite clusters whose dynamic behaviors depend on NaCl and trehalose. The volcano plot revealed several reporter metabolites whose dynamics greatly change between in the NaCl and trehalose cultures. The elastic net identified some critical, intracellular metabolites that are distinct between the NaCl and trehalose. While a relatively small number of intracellular metabolites related to the cell growth, glucose, glutamine, lactate and ammonium ion concentrations, the mechanism of antibody production was suggested to be very complicated or not to be explained by elastic net regression analysis.

DOI： 10.1016/j.jbiosc.2015.12.013

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© 2016 The Author(s).Background: A kinetic model provides insights into the dynamic response of biological systems and predicts how their complex metabolic and gene regulatory networks generate particular functions. Of many biological systems, Escherichia coli metabolic pathways have been modeled extensively at the enzymatic and genetic levels, but existing models cannot accurately reproduce experimental behaviors in a batch culture, due to the inadequate estimation of a specific cell growth rate and a large number of unmeasured parameters. Results: In this study, we developed a detailed kinetic model for the central carbon metabolism of E. coli in a batch culture, which includes the glycolytic pathway, tricarboxylic acid cycle, pentose phosphate pathway, Entner-Doudoroff pathway, anaplerotic pathway, glyoxylate shunt, oxidative phosphorylation, phosphotransferase system (Pts), non-Pts and metabolic gene regulations by four protein transcription factors: cAMP receptor, catabolite repressor/activator, pyruvate dehydrogenase complex repressor and isocitrate lyase regulator. The kinetic parameters were estimated by a constrained optimization method on a supercomputer. The model estimated a specific growth rate based on reaction kinetics and accurately reproduced the dynamics of wild-type E. coli and multiple genetic mutants in a batch culture. Conclusions: This model overcame the intrinsic limitations of existing kinetic models in a batch culture, predicted the effects of multilayer regulations (allosteric effectors and gene expression) on central carbon metabolism and proposed rationally designed fast-growing cells based on understandings of molecular processes.

DOI： 10.1186/s12934-016-0511-x

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DOI： 10.1016/j.jbiosc.2015.12.001

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

DOI： 10.1007/s00449-016-1554-4

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Language：English   Publishing type：Research paper (international conference proceedings)

Singapore   Singapore   2015.11.23  -  2015.11.26

Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)

Malaysia   Kuala Lumpur   2015.11.15  -  2015.11.19

Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)

Malaysia   Kuala Lumpur   2015.11.15  -  2015.11.19

Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)

Japan   Iizuka   2015.03.05  -  2015.03.06

Kyutacar

Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)

Japan   Iizuka   2015.03.05  -  2015.03.06

Kyutacar

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

DOI： 10.1186/1752-0509-8-S5-S1

Kyutacar

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Authorship：Corresponding author   Language：English   Publishing type：Research paper (international conference proceedings)

Japan   Tokyo   2014.12.15  -  2014.12.17

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DOI： 10.1016/j.bej.2014.05.022

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USA   San Antonio   2014.03.24  -  2014.03.26

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DOI： 10.1007/s00449-014-1167-8

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CiNii Article

Other Link： https://ci.nii.ac.jp/naid/110009675848

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Japan   Iizuka   2014.02.19  -  2014.02.20

Kyutacar

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DOI： 10.1252/jcej.13we152

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CiNii Article

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CiNii Article

Other Link： https://ci.nii.ac.jp/naid/10031199937

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CiNii Article

Other Link： https://ci.nii.ac.jp/naid/110009605329

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

DOI： 10.1016/j.jbiosc.2013.03.010

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CiNii Article

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DOI： 10.1093/bib/bbt048

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

DOI： 10.4236/abb.2013.43A063

Kyutacar

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Japan   Iizuka   2013.02.28  -  2013.03.01

Kyutacar

Language：English   Publishing type：Research paper (international conference proceedings)

Bangladesh   Rajshahi   2012.12.22  -  2012.12.24

Kyutacar

Language：English   Publishing type：Research paper (international conference proceedings)

Bangladesh   Rajshahi   2012.12.22  -  2012.12.24

Kyutacar

Language：English   Publishing type：Research paper (international conference proceedings)

Japan   Tsuruoka   2012.10.21  -  2012.10.25

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CiNii Article

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Korea   Deagu   2012.09.18  -  2012.09.18

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DOI： 10.1186/1751-0473-7-9

Kyutacar

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

DOI： 10.1007/s00449-012-0789-y

Scopus

Language：English   Publishing type：Research paper (international conference proceedings)

Australia   Adelaide   2012.07.09  -  2012.07.12

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Australia   Adelaide   2012.07.09  -  2012.07.12

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DOI： 10.1371/journal.pone.0037739

Kyutacar

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Next generation sequencers enable decoding human genome at a surprisingly high speed. Furthermore, biological and medical data have rapidly been produced due to the development of systemic high-throughput technologies. Bioinformatics and systems biology have emerged, facilitating the interdisciplinary research and development among medicine, informatics, and engineering. In such a world-wide stream, we extensively apply the information technology and systems engineering to the research and development of medicine and medical care systems. In this study, we demonstrate that information technology is effective in analysis of metabolic networks.

CiNii Article

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DOI： 10.1371/journal.pone.0030489

Kyutacar

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DOI： 10.1162/artl_a_00049

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Malaysia   Kuala Lumpur   2011.11.30  -  2011.12.02

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DOI： 10.1016/j.bej.2011.02.022

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DOI： 10.1007/s00449-010-0486-7

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DOI： 10.1016/j.jtbi.2010.12.012

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China   Shanghai   2011.05.11  -  2011.05.15

Authorship：Corresponding author   Language：Japanese   Publishing type：Research paper (other academic)

Kyutacar

Authorship：Corresponding author   Language：Japanese   Publishing type：Research paper (other academic)

Kyutacar

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

A goal of systems biology is to analyze large-scale molecular networks including gene expressions and protein-protein interactions, revealing the relationships between network structures and their biological functions. Dividing a protein-protein interaction (PPI) network into naturally grouped parts is an essential way to investigate the relationship between topology of networks and their functions. However, clear modular decomposition is often hard due to the heterogeneous or scale-free properties of PPI networks. To address this problem, we propose a diffusion model-based spectral clustering algorithm, which analytically solves the cluster structure of PPI networks as a problem of random walks in the diffusion process in them. To cope with the heterogeneity of the networks, the power factor is introduced to adjust the diffusion matrix by weighting the transition (adjacency) matrix according to a node degree matrix. This algorithm is named adjustable diffusion matrix-based spectral clustering (ADMSC). To demonstrate the feasibility of ADMSC, we apply it to decomposition of a yeast PPI network, identifying biologically significant clusters with approximately equal size. Compared with other established algorithms, ADMSC facilitates clear and fast decomposition of PPI networks. ADMSC is proposed by introducing the power factor that adjusts the diffusion matrix to the heterogeneity of the PPI networks. ADMSC effectively partitions PPI networks into biologically significant clusters with almost equal sizes, while being very fast, robust and appealing simple.

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CiNii Article

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CiNii Article

Other Link： https://ci.nii.ac.jp/naid/110007700578

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DOI： 10.1016/j.jbiosc.2010.01.015

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CiNii Article

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DOI： 10.1371/journal.pone.0012623

Kyutacar

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DOI： 10.2197/ipsjtbio.2.2

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CiNii Article

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Bacterial cells evolve complex networks to survive nutrient starvation. The E. coli ammonia assimilation system consists of many positive and negative feedbacks for synthesizing glutamine and glutamate, the source of most nitrogen-containing compounds. Our objectives are to understand the molecular architecture of how those feedback loops act in concert for enhanced robustness with respect to ammonia depletion. The ammonia assimilation system is decomposed into the hierarchical modular structure in analogy to engineering control architecture. This modular architecture is elaborately designed to solve contradictory design issues. Comparison of biological modules with engineering systems identifies interesting biologically specific design features. © 2009 SICE.

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CiNii Article

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Japan   Fukuoka   2009.08.18  -  2009.08.21

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Japan   Kobe   2009.11.24  -  2009.11.28

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

Motivation: Gene deletion and overexpression are critical technologies for designing or improving the metabolic flux distribution of microbes. Some algorithms including flux balance analysis (FBA) and minimization of metabolic adjustment (MOMA) predict a flux distribution from a stoichiometric matrix in the mutants in which some metabolic genes are deleted or non-functional, but there are few algorithms that predict how a broad range of genetic modifications, such as over- and underexpression of metabolic genes, alters the phenotypes of the mutants at the metabolic flux level. Results: To overcome such existing limitations, we develop a novel algorithm that predicts the flux distribution of the mutants with a broad range of genetic modification, based on elementary mode analysis. It is denoted as genetic modification of flux (GMF), which couples two algorithms that we have developed: modified control effective flux (mCEF) and enzyme control flux (ECF). mCEF is proposed based on CEF to estimate the gene expression patterns in genetically modified mutants in terms of specific biological functions. GMF is demonstrated to predict the flux distribution of not only gene deletion mutants, but also the mutants with underexpressed and overexpressed genes in Escherichia coli and Corynebacterium glutamicum. This achieves breakthrough in the a priori flux prediction of a broad range of genetically modified mutants. © The Author 2009. Published by Oxford University Press. All rights reserved.

DOI： 10.1093/bioinformatics/btp298

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Authorship：Corresponding author   Language：Japanese   Publishing type：Research paper (other academic)

Kyutacar

Authorship：Corresponding author   Language：Japanese   Publishing type：Research paper (other academic)

Kyutacar

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CiNii Article

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Kyutacar

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

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CiNii Article

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DOI： 10.11185/imt.4.377

CiNii Article

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Enzyme Control Flux (ECF) is a method of correlating enzyme activity and flux distribution. The advantage of ECF is that the measurement integrates proteome data with metabolic flux analysis through Elementary Modes (EMs). But there are a few methods of effectively determining the Elementary Mode Coefficient (EMC) in cases where no objective biological function is available. Therefore, we proposed a new algorithm implementing the maximum entropy principle (MEP) as an objective function for estimating the EMC. To demonstrate the feasibility of using the MEP in this way, we compared it with Linear Programming and Quadratic Programming for modeling the metabolic networks of Chinese Hamster Ovary, Escherichia coli, and Saccharomyces cerevisiae cells. The use of the MEP presents the most plausible distribution of EMCs in the absence of any biological hypotheses describing the physiological state of cells, thereby enhancing the prediction accuracy of the flux distribution in various mutants. © 2008 Elsevier Inc. All rights reserved.

DOI： 10.1016/j.jbiosc.2008.09.011

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China   Dailen   2008.10.12  -  2008.10.17

Language：English   Publishing type：Research paper (international conference proceedings)

China   Dailen   2008.10.12  -  2008.10.17

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CiNii Article

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China   2008.10.12  -  2008.10.17

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CiNii Article

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

For complex biological networks, graphical representations are highly desired for understanding some design principles, but few drawing methods are available that capture topological features of a large and highly heterogenous network, such as a protein interaction network. Here we propose the circular perspective drawing (CPD) method to visualize global structures of large complex networks. The presented CPD combines the quasi-continuous search (QCS) analogous to the steepest descent method with a random node swapping strategy for an enhanced calculation speed. The CPD depicts a network in an aesthetic manner by showing connection patterns between different parts of the network instead of detailed links between nodes. Global structural features of networks exhibited by CPD provide clues toward a comprehensive understanding of the network organizations. © 2008 Li, Kurata.

DOI： 10.1371/journal.pone.0002541

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

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

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

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

Kyutacar

Language：English   Publishing type：Research paper (bulletin of university, research institution)

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中華民国   台北   2007.11.04  -  2007.11.07

Language：English   Publishing type：Research paper (international conference proceedings)

中華民国   台北   2007.11.04  -  2007.11.07

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

Biochemical network maps are helpful for understanding the mechanism of how a collection of biochemical reactions generate particular functions within a cell. We developed a new and computationally feasible notation that enables drawing a wide resolution map from the domain-level reactions to phenomenological events and implemented it as the extended GUI network constructor of CADLIVE (Computer-Aided Design of LIVing systEms). The new notation presents 'Domain expansion' for proteins and RNAs, 'Virtual reaction and nodes' that are responsible for illustrating domain-based interaction and 'InnerLink' that links real complex nodes to virtual nodes to illustrate the exact components of the real complex. A modular box is also presented that packs related reactions as a module or a subnetwork, which gives CADLIVE a capability to draw biochemical maps in a hierarchical modular architecture. Furthermore, we developed a pathway search module for virtual knockout mutants as a built-in application of CADLIVE. This module analyzes gene function in the same way as molecular genetics, which simulates a change in mutant phenotypes or confirms the validity of the network map. The extended CADLIVE with the newly proposed notation is demonstrated to be feasible for computational simulation and analysis. © 2007 The Author(s).

DOI： 10.1093/nar/gkm769

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Dynamic simulations are necessary for understanding the mechanism of how biochemical network generate robust properties to environmental stresses or genetic changes. Sensitivity analysis allows the linking of robustness to network structure. However, it yields only local properties regarding a particular choice of plausible parameter values, because it is hard to know the exact parameter values in vivo. Global and firm results are needed that do not depend on particular parameter values. We propose mathematical analysis for robustness (MAR) that consists of the novel evolutionary search that explores all possible solution vectors of kinetic parameters satisfying the target dynamics and robustness analysis. New criteria, parameter spectrum width and the variability of solution vectors for parameters, are introduced to determine whether the search is exhaustive. In robustness analysis, in addition to single parameter sensitivity analysis, robustness to multiple parameter perturbation is defined. Combining the sensitivity analysis and the robustness analysis to multiple parameter perturbation enables identifying critical reactions. Use of MAR clearly identified the critical reactions responsible for determining the circadian cycle in the Drosophila interlocked circadian clock model. In highly robust models, while the parameter vectors are greatly varied, the critical reactions with a high sensitivity are uniquely determined. Interestingly, not only the per-timloop but also the dclk-cyc loop strongly affect the period of PER, although the dclk-cyc loop hardly changes its amplitude and it is not potentially influential. In conclusion, MAR is a powerful method to explore wide parameter space without human-biases and to link a robust property to network architectures without knowing the exact parameter values. MARS identifies the reactions critically responsible for determining the period and amplitude in the interlocked feedback model and suggests that the circadian clock intensively evolves or designs the kinetic parameters so that it creates a highly robust cycle. © 2007 Kurata et al.

DOI： 10.1371/journal.pone.0001103

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CiNii Article

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The deterministic simulation that uses ordinary differential equations to clarify dynamic behaviors of life-giving molecular networks is actively implemented now. However, gene expressions in cells happen stochastically and materials diffusion. In the deterministic simulation, time variations of molecular concentrations are decided when parameters are decided. Therefore, we propose the new method of spatiotemporal Monte Carlo simulation for achieving the simulation applied to gene expression and diffusion of materials in space and time.

CiNii Article

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Dynamic simulation is a way to analyze dynamics of biomolecular networks consisting of proteins, metabolites and genes. In order to simulate their intrinsic dynamics, it is necessary to optimize the values of many kinetic parameters by reverse-engineering. We developed a new optimization method, DIS (Decomposition and Integration with Statistical analysis) combining decomposition and integration method with statistical analysis. By using DIS and genetic algorithm (GA), we optimized the parameters of large-scale biomolecular network which conventional GA cannot optimize. In this article, we also show that this method can obtain global solutions.

CiNii Article

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In the field of systems biology, biochemical networks are being reconstructed in computer to understand their dynamic features. In this study, we focused on the estimation problem for a dynamic model of the Drosophila circadian oscillator, which is defined by two evaluation functions that represent oscillatory features. However, since the search space is multimodal and logarithmically large, it is quite difficult for ordinary GAs to optimize this evaluation problem. Therefore, we had used two-step optimizing, a random search with GA. It successfully optimized the circadian oscillator, but required a long calculation time, causing a local search. On the other hand, to optimize two evaluation functions simultaneously, we proposed the survival ratio GA, where genes have lifetimes and a population holds search histories. By alternating two evaluation functions every generation, the population holds previously evaluated genes and children inherit each and mixed properties. The survival ratio GA exhibited wider space search and higher success ratio, thus we applied the one-step optimizing with the survival ratio GA to the circadian estimation problem, which shortens the total calculation times and finds out more local optimums than the previous two-step optimizing method. © 2006 IEEE.

DOI： 10.1109/ICSMC.2006.384687

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Background: In systems biology, network-based pathway analysis facilitates understanding or designing metabolic systems and enables prediction of metabolic flux distributions. Network-based flux analysis requires considering not only pathway architectures but also the proteome or transcriptome to predict flux distributions, because recombinant microbes significantly change the distribution of gene expressions. The current problem is how to integrate such heterogeneous data to build a network-based model. Results: To link enzyme activity data to flux distributions of metabolic networks, we have proposed Enzyme Control Flux (ECF), a novel model that integrates enzyme activity into elementary mode analysis (EMA). ECF presents the power-law formula describing how changes in enzyme activities between wild-type and a mutant are related to changes in the elementary mode coefficients (EMCs). To validate the feasibility of ECF, we integrated enzyme activity data into the EMCs of Escherichia coli and Bacillus subtilis wild-type. The ECF model effectively uses an enzyme activity profile to estimate the flux distribution of the mutants and the increase in the number of incorporated enzyme activities decreases the model error of ECF. Conclusion: The ECF model is a non-mechanistic and static model to link an enzyme activity profile to a metabolic flux distribution by introducing the power-law formula into EMA, suggesting that the change in an enzyme profile rather reflects the change in the flux distribution. The ECF model is highly applicable to the central metabolism in knockout mutants of E. coli and B. subtilis. © 2007 Kurata et al; licensee BioMed Central Ltd.

DOI： 10.1186/1752-0509-1-31

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

主要雑誌

Authorship：Corresponding author   Language：Japanese   Publishing type：Research paper (other academic)

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

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

Computer simulation predicted that the nitrogen assimilation system in the GlnK knockout mutant of E. coli shows hysteresis with respect to changes in the extracellular ammonia concentration. To theoretically elucidate the mechanism of how hysteresis is generated, we reduced the dynamic model to a simple model with third-order equations. Theoretical analysis of the simple model shows that a positive feedback control is responsible for hysteresis. The system has two steady-state solutions within a specific range of ammonia concentrations. The range that provides two solutions expands when the positive feedback becomes strong, while the system has only one solution when the positive feedback is weakened.

CiNii Article

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CiNii Article

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Advances in the postgenomic technology produce huge data regarding molecular interactions. Systems biology requires a model-based analysis to understand the molecular architecture of biological systems. Systems biology consists of four stages: network identification, system analysis, system control, and system design. First, a large-scale biochemical network model is reconstructed in computer by elucidating gene functions or by inferring gene regulation networks from postgenomic data. Second, we analyze the network architectures that generate robust properties to various types of perturbations and explore some design principles underlying molecular architectures. Third, dynamic behaviors are controlled at the molecular level. Finally, we develop new technologies to rationally design a biochemical system at the molecular levels.<br>

DOI： 10.2142/biophys.46.244

CiNii Article

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CiNii Article

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Biological systems have evolved complex regulatory mechanisms, even in situations where much simpler designs seem to be sufficient for generating nominal functionality. Using module-based analysis coupled with rigorous mathematical comparisons, we propose that in analogy to control engineering architectures, the complexity of cellular systems and the presence of hierarchical modular structures can be attributed to the necessity of achieving robustness. We employ the Escherichia coli heat shock response system, a strongly conserved cellular mechanism, as an example to explore the design principles of such modular architectures. In the heat shock response system, the sigma-factor σ32 is a central regulator that integrates multiple feedforward and feedback modules. Each of these modules provides a different type of robustness with its inherent tradeoffs in terms of transient response and efficiency. We demonstrate how the overall architecture of the system balances such tradeoffs. An extensive mathematical exploration nevertheless points to the existence of an array of alternative strategies for the existing heat shock response that could exhibit similar behavior. We therefore deduce that the evolutionary constraints facing the system might have steered its architecture toward one of many robustly functional solutions. © 2006 Kurata et al.

DOI： 10.1371/journal.pcbi.0020059

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

Computer simulation predicted that the nitrogen assimilation system shows hysteresis with respect to the change in ammonia concentration in environment. To theoretically elucidate the mechanism of how hysteresis is generated, we reduced the dynamic model to the simple model with the third-order equations. Theoretical analysis of the simple model shows that the positive feedback control is responsible for hysteresis. The system has two steady-state solutions for the ammonia concentration within specific ranges. The range of an ammonia concentration that provides two solutions expands when the positive feedback becomes strong, while the system has only one solution when the positive feedback is weakened.

CiNii Article

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Language：English   Publishing type：Research paper (international conference proceedings)

中華民国   Taipei   2006.04  -  2006.04

Language：Japanese   Publishing type：Research paper (bulletin of university, research institution)

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

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

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In the field of bioinformatics, several studies have attempted to reconstruct biological reactions on a computer in order to understand their essence. In this study, we optimized the parameter tuning problem of the heat shock response in E. coli, one of the gene regulatory network simulations, which exhibits a transient peak by genetic algorithms (GAs). However, if the search area is too large, the optimization performance deteriorated significantly. To optimize this problem more efficiently, we defined two evaluation functions that represent two features of the simulation and used the survival ratio GA. This GA has a gene's lifetime as a new concept, that is, the population holds previous search histories. By alternating between two evaluation functions every generation, the population holds both previously evaluated genes and children inherit both properties. In the two-evaluation problem of the heat shock response, the survival ratio GA exhibited a considerably better optimization performance than traditional GA methods. © 2005 IEEE.

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In the field of bioinformatics, biochemical networks have been reconstructed in computer to understand their dynamic features. A problem is how to estimate the values of many kinetic parameters that are hard to measure experimentally. In this study, we have proposed a novel parameter estimation method based on genetic algorithms (GAs) and applied it to the Drosophila circadian oscillator. We defined two evaluation functions that represent two features of the dynamic model. To optimize the two functions simultaneously, we proposed the survival ratio GA, which has a gene's lifetime as a new concept, that is, the population holds previous search histories. By alternating between two evaluation functions every generation, the population holds both previously evaluated genes and children inherit both properties. In the two-evaluation problem of the Drosophila circadian oscillation system, the survival ratio GA exhibited a considerably better optimizing performance than traditional GA methods. © 2005 IEEE.

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Austria   2005.11.28  -  2005.11.30

Language：English   Publishing type：Research paper (international conference proceedings)

米国   Hawaii   2005.10.10  -  2005.10.12

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

Motivation: Visualization is indispensable in the research of complex biochemical networks. Available graph layout algorithms are not adequate for satisfactorily drawing such networks. New methods are required to visualize automatically the topological architectures and facilitate the understanding of the functions of the networks. Results: We propose a novel layout algorithm to draw complex biochemical networks. A network is modeled as a system of interacting nodes on squared grids. A discrete cost function between each node pair is designed based on the topological relation and the geometric positions of the two nodes. The layouts are produced by minimizing the total cost. We design a fast algorithm to minimize the discrete cost function, by which candidate layouts can be produced efficiently. A simulated annealing procedure is used to choose better candidates. Our algorithm demonstrates its ability to exhibit cluster structures clearly in relatively compact layout areas without any prior knowledge. We developed Windows software to implement the algorithm for CADLIVE. © The Author 2005. Published by Oxford University Press. All rights reserved.

DOI： 10.1093/bioinformatics/bti290

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

主要雑誌

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

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Language：English   Publishing type：Research paper (international conference proceedings)

Korea   Jezu Isaland   2005.04  -  2005.04

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

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

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

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

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

We have developed the CADLIVE (Computer-Aided Design of LIVing systEms) Simulator that provided a rule-based automatic way to convert biochemical network maps into dynamic models, which enables simulating their dynamics without going through all of the reactions down to the details of exact kinetic parameters. The simulator supports the biochemical reaction maps that are generated by the previously developed GUI editor. Notice that the part of the GUI editor had been previously published, but, as yet, not the simulator. To directly link biochemical network maps to dynamic simulation, we have created the strategy of three layers and two stages with the efficient conversion rules in an XML representation. This strategy divides a molecular network into three layers, i.e., gene, protein, and metabolic layers, and partitions the conversion process into two stages. Once a biochemical map is provided, CADLIVE automatically builds a mathematical model, thereby facilitating one to simulate and analyze it. In order to demonstrate the feasibility of CADLIVE, we analyzed the Escherichia coli nitrogen-assimilation system (64 equations with 64 variables) that consists of multiple and complicated negative and positive feedback loops. CADLIVE predicted that the glnK gene is responsible for hysteresis or reversibility of nitrogen-related (Ntr) gene expression with respect to the ammonia concentration, supporting the experimental observation of the runaway expression of the Ntr genes. ©2005 by Cold Spring Harbor Laboratory Press.

DOI： 10.1101/gr.3463705

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Molecular biology studies the cause-and-effect relationships among microscopic processes initiated by individual molecules within a cell and observes their macroscopic phenotypic effects on cells and organisms. These studies provide a wealth of information about the underlying networks and pathways responsible for the basic functionality and robustness of biological systems. At the same time, these studies create exciting opportunities for the development of quantitative and predictive models that connect the mechanism to its phenotype then examine various modular structures and the range of their dynamical behavior. The use of such models enables a deeper understanding of the design principles underlying biological organization and makes their reverse engineering and manipulation both possible and tractable The heat shock response presents an interesting mechanism where such an endeavor is possible. Using a model of heat shock, we extract the design motifs in the system and justify their existence in terms of various performance objectives. We also offer a modular decomposition that parallels that of traditional engineering control architectures. © 2005 by The National Academy of Sciences of the USA.

DOI： 10.1073/pnas.0403510102

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The progress of computer made the analysis that was difficult in traditional technology possible. In Gene Regulatory Network Simulation to recreate and understand actual biological reaction, we build a reaction model and find the several unknown parameters in order to show the same behavior as actual. However it is difficult to optimize them because the scopes of them are logarithm scale wide and the fitness space is multidimensional and multimodal. We optimized it using Distributed Genetic Algorithms (DGA) which has multiple populations. DGA showed better performance, if and only if the parameter of the emigrating operation is appropriate. Therefore we propose Distributed and Integrated Genetic Algorithms (DIGA) to reduce the cost to find the appropriate parameters of operation. We carried out some optimizing experiments on parameter tuning of Heat Shock Response simulation of E.Coli and several mathematical functions and inspected the characteristic of DIGA. © 2004 IEEE.

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Language：Japanese   Publishing type：Research paper (other academic)

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Kitakyushu   2004.10.17  -  2004.10.21

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

Hague, Netherlands   2004.10.10  -  2004.10.13

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

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

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

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

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

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

DOI： 10.11234/gi1990.15.2_161

CiNii Article

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A goal of systems biology is to build a concrete biochemical network map, which provides an important instruction to trace the pathways of interest or to understand the mechanism of a biological system. In the postgenomic era, not only the concrete biochemical maps, but also postgenomic maps (mRNA coexpression and protein-protein interaction networks) have been extensively produced. In the biochemical map, the individual reactions are reliable, but the number of the reactions is limited, because molecular biology requires extensive experiments to verify them. By contrast, postgenomic data provide much information regarding interactions, but are coarse-grained. To expand the biochemical network, an intuitional approach, which superposes postgenomic data on the map one by one, has been carried out, but it is not effective when a large amount of the coarse-grained data is handled. In order to effectively integrate such postgenomic interactions into a biochemical map, a statistical approach would be suitable rather than intuition. In this article, we proposed a novel statistical approach that integrates postgenomic interaction networks into the biochemical network, predicting novel pathways. A statistical correlation for such different types of networks identifies functional modules; subsequently the superposition of the different networks on the functional modules predicts inter-modular relations, which are the key pathways to construct a large-scale biochemical network.

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DOI： 10.11491/apcche.2004.0.419.0

CiNii Article

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Language：English   Publishing type：Research paper (other academic)

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Language：English   Publishing type：Research paper (other academic)

Language：English   Publishing type：Research paper (other academic)

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Real-Corded GA is used in optimization problems in a lot of fields. But, there is a problem that GA demands a large quantity of computational complexity. Therefore Hybrid Genetic Algorithm (gradient GA) to use a gradient shows high optimization performance. But we cannot apply gradient GA for the optimization problem that it is impossible to calculate a gradient value of fitness function. So Dominant Direction Priority search method (DDP Searching Method) is suggested in order to reduce these problems. In DDP Searching method, we suppose a vector facing a superior gene from an inferior gene in population to be an indirect gradient direction (a dominant direction) And we carry out prior local search of dominant direction and GA crossover simultaneously. In this paper, we inspected the optimization performance in some mathematical benchmark functions and gene regulatory network simulation using DDP Searching Method.

CiNii Article

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GA is used in optimization problems in a lot of fields. But, there is a problem that GA demands a large quantity of computational complexity. Therefore we propose DDP algorithm in order to reduce this problem. DDP calculates DDP vector from the inferior gene to superior of population and create multiple new genes on the DDP vector. We carried out computer simulation to optimize some functions. As a result, DDP adds a search of a global area at early stage of optimization and a search of local area at a late stage to GA. GA+DDP showed the effectiveness in optimization problems of mathematical function and gene regulatory network simulation.

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The further understanding of the mechanisms of gene regulatory networks requires comprehensive tools for both the representation of complicated signal transduction pathways and the in silico, identification of genomic signals that govern the regulation of gene expression. Consequently, sophisticated notation must be developed to represent the signal transduction pathways in a form that can be readily processed by both computers and humans. We propose the regulator-reaction equations combined with detailed attributes including the associated cellular component, molecular function, and biological process and present the simulation-directed graphical notation that is derived from modification of Kohn's method. We have developed the software suite, CADLIVE (Computer-Aided Design of LIVing systEms), which features a graphical user interface (GUI) to edit large-scale maps of complicated signal transduction pathways using a conventional XML-based representation. The regulator-reaction equations represent not only mechanistic reactions, but also semantic models containing ambiguous and incomplete processes. In order to demonstrate the feasibility of CADLIVE, we constructed a detailed map of the budding yeast cell cycle, which consists of 184 molecules and 152 reactions, in a really compact space. CADLIVE enables one to look at the whole view of a large-scale map, to integrate postgenomic data into the map, and to computationally simulate the signal transduction pathways, which greatly facilitates exploring novel or unexpected interactions.

DOI： 10.1093/nar/gkg461

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CiNii Article

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GA is used in optimization problems in a lot of fields. But, there is a problem that GA demands a large quantity of computational complexity. Therefore we propose DDP algorithm in order to reduce this problem. GA+DDP showed the effectiveness in optimization problems of mathematical function and gene regulatory network simulation.

CiNii Article

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Language：English   Publishing type：Research paper (international conference proceedings)

Las Vegas, Nevada, USA   2003.06.23  -  2003.06.23

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

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DOI： 10.11234/gi1990.14.611

CiNii Article

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DOI： 10.11234/gi1990.14.609

CiNii Article

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DOI： 10.11234/gi1990.14.388

CiNii Article

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Language：English   Publishing type：Research paper (other academic)

Language：English   Publishing type：Research paper (other academic)

Language：English   Publishing type：Research paper (other academic)

Language：English   Publishing type：Research paper (other academic)

Anchorage   2002.05.15  -  2002.05.15

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To establish a strategy for stably and highly expressing a target gene in transformed plant cell suspensions, we developed the co-selection method that linked the hygromycin phosphotransferase gene (hph) to the β-glucuronidase (uidA, GUS) gene in the opposite direction under the same transcriptional regulation of the cauliflower mosaic virus (CaMV) 35S promoter. The linked genes were transferred into a tobacco BY-2 cell suspension, which was cultivated under various levels of the antibiotic pressure. Under the high pressure of hygromycin, GUS expression was increased and maintained over 1.5 years. We presented a successful example for selecting the plant cell suspension that highly expressed the target gene out of genetically heterogeneous cells. © 2002 Elsevier Science B.V. All rights reserved.

DOI： 10.1016/S1369-703X(01)00182-6

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DOI： 10.11234/gi1990.13.467

CiNii Article

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DOI： 10.11234/gi1990.13.301

CiNii Article

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The bacterial heat shock response refers to the mechanism by which bacteria react to a sudden increase in the ambient temperature of growth. The consequences of such an unmediated temperature increase at the cellular level is the unfolding, misfolding, or aggregation of cell proteins, which threatens the life of the cell. Cells respond to the heat stress by initiating the production of heat-shock proteins whose function is to refold denatured proteins into their native states. The heat shock response, through the elevated synthesis of molecular chaperones and proteases, enables the repair of protein damage and the degradation of aggregated proteins. In a previous work [1], we have devised a dynamic model for the heat shock response in E. coli. In the present paper, we provide a thorough discussion of the dynamical nature of this model. We use sensitivity analysis and simulation tools to illustrate the remarkable efficiency, robustness, and stability of the heat shock response system.

DOI： 10.1109/ACC.2002.1023817

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Language：English   Publishing type：Research paper (other academic)

Language：English   Publishing type：Research paper (other academic)

Language：English   Publishing type：Research paper (other academic)

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DOI： 10.11234/gi1990.12.284

CiNii Article

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The phenomenon known as RNA interference (RNAi) by double-stranded RNA (dsRNA) that was reported recently in the nematode Caenorhabditis elegans has been shown to operate by a mechanism that is widely conserved among species including plant cells. No quantitative analysis of the effects of RNAi on the expression of specific genes in plant cultured cells has been reported. An RNAi effect was observed 24 h after the introduction of dsRNA expression plasmids into tobacco BY-2 cells by electroporation. The simple system for suppression of specific genes in plant cells should be useful in attempts to elucidate the roles of individual genes in plant cells.

DOI： 10.1093/nass/1.1.235

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A glucocorticoid-induced target gene expression system was used to control the expression of the uidA gene, whose product was β-glucuronidase (GUS), in tobacco BY-2 cell suspension culture. This targeting system showed quick, sensitive, and reversible response to dexamethazone (DEX), an artificial glucocorticoid hormone. Addition of DEX greatly and quickly enhanced uidA gene expression, whose level was as high as that under the control of the CaMV 35S promoter whereas in the absence of DEX, the GUS specific activity was suppressed to be as low as that of nontransformed BY-2 cells. The dilution of DEX decreased GUS specific activity showing that the concentration of DEX plays a major role in controlling the expression level of the target. The use of the glucocorticoid-induced system in plant cell suspension culture was demonstrated to precisely control target gene expression. (C) 2000 Elsevier Science S.A.

DOI： 10.1016/S1369-703X(00)00087-5

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Ribozymes are catalytic RNAs that can cleave RNAs at specific sites, thus they have been employed to degrade a target mRNA in vivo. Development of allosterically controllable ribozymes is of great current interest, but it remained difficult to furnish such functions to ribozymes in cultured cells or in animals. Recently, we designed allosterically controllable ribozymes termed maxizymes, which have sensor arms that recognize target mRNA sequences and, in the presence of such target sequences only, they form a cavity that can capture catalytically indispensable Mg2+ ions, cleaving the target The maxizyme was applied to therapy for chronic myelogenous leukemia (CML). It cleaved specifically the chimeric BCR-ABL mRNA, which caused CML, without damaging the normal ABL or BCR mRNA in mammalian cells and also in mice, providing the first successful example for allosteric control of the activity of artificial ribozymes in vivo.

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Anthocyanin production by strawberry cells depends not only on light intensity but also on the light/dark cycle operation with hour- or second- scale periods. These findings are useful for designing and operating photobioreactors for enhanced anthocyanin production. Intermittent illumination with a second-scale period produces the same amount of anthocyanin as continuous light, suggesting that the light intensity distribution within a photobioreactor does not cause suppressed production. In the hour-scale cycle, continuous light operation enhanced anthocyanin production more than the light/dark cycle process. (C) 2000 Elsevier Science Inc.

DOI： 10.1016/S0141-0229(00)00143-5

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Language：English   Publishing type：Research paper (other academic)

Language：Japanese   Publishing type：Research paper (other academic)

Language：English   Publishing type：Research paper (other academic)

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DOI： 10.11234/gi1990.11.185

CiNii Article

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To accelerate the calculation speed for simulating a biological system, we proposed a novel simulation method, the two-phase partition method, which calculated molecular processes at a higher speed than any other proposed method. This method divides a biological system, which can be described by chemical reaction equations, into two-phases: the binding and reaction phases. We demonstrated the capability of the two-phase partition method to simulate a complex biological system at an extremely high speed and clarified the accuracy of the simulation. The two-phase partition method is very useful for simulating complex interactions among proteins and DNAs.

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The β-glucuronidase (GUS) reporter gene was fused to the 2.0-kb upstream region of Arabidopsis thaliana chalcone synthase (CHS) gene and transferred into BY-2 cells. CHS promoter expression showed a quick response to light/darkness and its expression level was more than that by the Cauliflower Mosaic Virus (CaMV) 35S promoter [1]. In this study, a kinetic model was developed for describing light-enhanced expression of the CHS promoter by tobacco BY-2 cells. This model simulates the behavior of CHS promoter expression by two factors: the specific light absorption rate (light intensity) and the illumination time. The dependency of GUS synthesis on light intensity is well characterized by using the Monod equation. The dependency on the illumination time is calculated by using a saturation time constant of illumination. The mathematical model demonstrated that the conditioned medium or past illumination changed the kinetics of CHS promoter expression.

DOI： 10.1016/S1369-703X(99)00034-0

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To understand the elaborate system of a living organism, it is essential to know its "software", as well as its "hardware". The software of a living organism may be defined as the package of algorithms (methods and procedures) for its survival. The hardware of a living organism is encoded by genes on the chromosome. Its behaviors should be explainable on the basis of algorithms. The algorithms of a living system can be viewed as biological information that has been envolved over its long history of evolution. Among living organisms are bacteria that are probably the simplest systems for analyzing the software of living organisms. In the present paper, we describe our method for constructing a virtual bacterial system as a tool for analyzing the software of a living organism. We also describe bacterial algorithms for surviving during phosphate starvation and exhibiting an intelligent behavior called chemotaxis. In addition, we demonstrate a mobile robot whose behavior is controlled by a computer program designed on the basis of the bacterial chemotaxis algorithm.

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We developed novel equipment that intermittently illuminates Coffea arabica cell suspensions at a second-scale interval and investigated how intermittent irradiation enhances caffeine biosynthesis by C. arabica cells. The light/dark cycles consisting of 2 s of illumination and 18 s of darkness enhanced caffeine production, reaching the same level as for continuous light. The intermittent illumination increased the production efficiency regarding light consumption by a factor of 10. Caffeine production was determined by light intensity regardless of intermittent or continuous light irradiation. We propose a new concept for designing a photobioreactor that is applicable to secondary metabolite production by plant cell culture.

DOI： 10.1021/bp980065u

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Enhanced expression of the rbcS3A promoter was achieved by overproducing phytochrome in BY-2 tobacco cells. The transformed cells contained the oat phytochrome A gene fused the 35S promoter of Cauliflower Mosaic Virus (CaMV) and the gus gene to the rbcS3A promoter. The transformed cells exhibited 1.5- to -2.8 fold enhanced the β-glucuronidase (GUS) activity when subjected to light.

DOI： 10.1023/A:1005484011673

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The intermittent light irradiation with an hour-scale period is used for producing caffeine by Coffea arabica cells. Three factors concerning the light/dark cycle operation such as light intensity, the length of the cycle (period), and the ratio of the illumination time to the dark time (light/dark ratio) were investigated to optimize the caffeine production efficiency regarding light consumption. The light/dark ratio of 1/1 enhanced caffeine production, reaching the same level as continuous light; thus, the intermittent light irradiation improved the production efficiency twofold. The production was not influenced by the period, but was determined by light intensity regardless of intermittent or continuous light irradiation. Copyright (C) 1998 Elsevier Science Inc.

DOI： 10.1016/S0141-0229(98)00081-7

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The light-activated chalcone synthase (CHS) gene promoter was used to control the expression of a foreign gene - the uidA gene, whose product is β-glucuronidase (GUS) - in tobacco BY-2 cell suspensions. Light enhanced GUS specific activity rapidly and greatly in transgenic BY-2 containing a fusion of the CHS promoter and uidA gene. GUS production was turned on/off by using a light/dark cycle. The level of promoter activity was almost proportional to the intensity of light, surpassing the strength of the 35S promoter of Cauliflower Mosaic Virus (CaMV). Intermittent illumination induced the same level of GUS production as continuous light. The CHS promoter is thus useful for producing a foreign protein in plant cell bioreactors.

DOI： 10.1016/S0922-338X(98)80137-2

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Light irradiation not only enhanced purine alkaloid (caffeine and theobromine) production by a Coffea arabica cell suspension culture, but also caused physiological changes in cell growth, and sugar and oxygen uptake rates. Both sugar and oxygen uptake rates showed maxima between 7 and 10 W/m 2 of light intensity, where the specific production rate reached the highest, although the specific cell growth rate was reduced by more than 50%. Light acts as a stress for enhanced production. The activities of enzymes related to purine alkaloid production were induced by light after a lag-time of 1 day. Light is an inducer for these enzymes. However, the de novo synthesis of purine alkaloids required at least 6 days of light irradiation. This discrepancy was caused by the insufficient supply of purine rings, the precursors for biosynthesis of purine alkaloids.

DOI： 10.1016/S0168-9452(96)04588-8

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Since caffeine suppresses purine alkaloid (caffeine and theobromine) production by Coffea arabica cells, the effect of removing caffeine from the medium was investigated using the hydrophobic resin Amberlite XAD-4. An in situ removal method did not result in increased production because the adsorbent removed not only the products, but also essential components (hormones). Therefore, the use of an adsorption column employing the hydrophobic resin was investigated with a 35-d semicontinuous culture under light irradiation. Caffeine was removed within 30 min by the adsorption column on day 23 of the cultivation. In this system, the negative effects of hormone removal by the adsorbent were canceled out by the addition of fresh medium. This method increased alkaloid production 1.4-fold compared to the culture with no removal treatment. © 1994.

DOI： 10.1016/0922-338X(94)90192-9

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Coffea arabica cells immobilized by calcium alginate gel were photocultured using a bubble‐column reactor under controlled light intensity. This process was carried out after their alkaloid productivity was improved by increasing the cell density in the initial gel matrix and preculturing the immobilized cells in the dark prior to light irradiation. The cells were grown in the form of a biofilm on gel beads, producing 100 mg/L of purine alkaloids in a 24‐day batch culture. Alkaloid production was relatively constant with respect to light intensity changes, and also cell growth was not suppressed much at high light intensity, with these behaviors being different from those obtained using suspended cells. These phenomena are explained by estimating the light intensity gradient within the cell‐immobilizing particles and by measuring the viable cell distribution within them. It subsequently suggests that the subsurface cells affect both the production and growth behaviors. © 1993 John Wiley & Sons, Inc. Copyright © 1993 John Wiley & Sons, Inc.

DOI： 10.1002/bit.260420413

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A nonisotropic scattering model is proposed to estimate the light absorption rates in a heterogeneous photoreactor containing plant cells of Coffea arabica and is compared with an isotropic scattering model. In order to determine the directions of noniso‐tropically scattered light, optical theorems (geometrical optics and wave optics) are applied to light scattering caused by a homogeneous and perfect sphere. Two parameters, relative refractive index and attenuation coefficient, are used to characterize the ideal sphere. The primary feature of this model is that various phenomena of light scattering and absorption can be simulated by the combination of the two parameters. A measuring system composed of parallel plates was developed for evaluating the model. The nonisotropic scattering model was successfully applied to the heterogeneous reactor with the cultured plant cells. Copyright © 1993 American Institute of Chemical Engineers (AIChE)

DOI： 10.1021/bp00019a600

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The effect of light on caffeine production by Coffea arabica cells was investigated using chemical actinometry for the purpose of industrial application of plant cell cultures. Caffeine production by the Coffea arabica cells was greatly increased by light. About a six-day irradiation period was required to increase the caffeine production in a batch culture. Caffeine production significantly increased at a low light irradiation rate of 0.11 J/L/s. Meanwhile, cell growth was suppressed by increasing the irradiation rate. Maximum production was achieved at a moderate irradiation rate of 0.70 J/L/s in a 20-day batch culture. It was shown there was a light irradiation rate that optimized production. Since long-term continuous light greatly suppressed cell growth, the culture was divided into two stages: the production period in the light and the growth period in the dark. The high production and good growth were maintained for 65 days. © 1991, The Society of Chemical Engineers, Japan. All rights reserved.

DOI： 10.1252/jcej.24.783

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DOI： 10.1111/j.1749-6632.1990.tb18216.x

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第61回BMIRC研究会
非AUG開始コドンの翻訳制御―広がる生物学的役割
日時：2017年7月2日 16:20-17:50
場所：九州工業大学飯塚キャンパス 1202講義室
カンザス州立大学生物学科 教授
浅野 桂,　Asano, Katsura

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九工大のバイオメディカルインフォマティクスの成果と発展 日時：2018年3月8日 15:30-17:30
場所：九州工業大学飯塚キャンパス
大学院セミナー室N712

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バイオメディカルデザイン演習最終発表会 日時：2018年3月2日 14:40-15:40
場所：九州工業大学飯塚キャンパス
大学院セミナー室N712

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大学院セミナー室N712

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2017年度シリコンバレー研修旅行発表会 日時：2018年1月15日 16:20-17:20
場所：九州工業大学飯塚キャンパス
グローバルコミュニケーションラウンジ
九州工業大学
平安克也　（情報工学部）
岩崎美夏　（生命体工学研究科）
井原康　　（工学部）
村竹菜々瀬（工学部）