研究者詳細 - 倉田　博之

論文 - 倉田　博之

分割表示251 件中 21 - 40 件目／全件表示 >>

Prednts: Improved and robust prediction of nitrotyrosine sites by integrating multiple sequence features 査読有り

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

International Journal of Molecular Sciences 22 ( 5 ) 1 - 11 2021年03月

記述言語：英語掲載種別：研究論文（学術雑誌）

Nitrotyrosine, which is generated by numerous reactive nitrogen species, is a type of protein post-translational modification. Identification of site-specific nitration modification on tyro-sine is a prerequisite to understanding the molecular function of nitrated proteins. Thanks to the progress of machine learning, computational prediction can play a vital role before the biological experimentation. Herein, we developed a computational predictor PredNTS by integrating multiple sequence features including K-mer, composition of k-spaced amino acid pairs (CKSAAP), AAindex, and binary encoding schemes. The important features were selected by the recursive feature elimination approach using a random forest classifier. Finally, we linearly combined the successive random forest (RF) probability scores generated by the different, single encoding-employing RF models. The resultant PredNTS predictor achieved an area under a curve (AUC) of 0.910 using five-fold cross validation. It outperformed the existing predictors on a comprehensive and independent dataset. Furthermore, we investigated several machine learning algorithms to demonstrate the superiority of the employed RF algorithm. The PredNTS is a useful computational resource for the prediction of nitrotyrosine sites. The web-application with the curated datasets of the PredNTS is publicly available.

DOI： 10.3390/ijms22052704

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

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

Journal of Computer-Aided Molecular Design 35 ( 3 ) 315 - 323 2021年03月

担当区分：責任著者記述言語：英語掲載種別：研究論文（学術雑誌）

Redox-sensitive cysteine (RSC) thiol contributes to many biological processes. The identification of RSC plays an important role in clarifying some mechanisms of redox-sensitive factors; nonetheless, experimental investigation of RSCs is expensive and time-consuming. The computational approaches that quickly and accurately identify candidate RSCs using the sequence information are urgently needed. Herein, an improved and robust computational predictor named IRC-Fuse was developed to identify the RSC by fusing of multiple feature representations. To enhance the performance of our model, we integrated the probability scores evaluated by the random forest models implementing different encoding schemes. Cross-validation results exhibited that the IRC-Fuse achieved accuracy and AUC of 0.741 and 0.807, respectively. The IRC-Fuse outperformed exiting methods with improvement of 10% and 13% on accuracy and MCC, respectively, over independent test data. Comparative analysis suggested that the IRC-Fuse was more effective and promising than the existing predictors. For the convenience of experimental scientists, the IRC-Fuse online web server was implemented and publicly accessible at http://kurata14.bio.kyutech.ac.jp/IRC-Fuse/.

DOI： 10.1007/s10822-020-00368-0

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Virtual metabolic human dynamic model for pathological analysis and therapy design for diabetes 査読有り

Kurata H.

iScience 24 ( 2 ) 2021年02月

Pup-fuse: Prediction of protein pupylation sites by integrating multiple sequence representations 査読有り

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

International Journal of Molecular Sciences 22 ( 4 ) 1 - 12 2021年02月

担当区分：責任著者記述言語：英語掲載種別：研究論文（学術雑誌）

Pupylation is a type of reversible post-translational modification of proteins, which plays a key role in the cellular function of microbial organisms. Several proteomics methods have been developed for the prediction and analysis of pupylated proteins and pupylation sites. However, the traditional experimental methods are laborious and time-consuming. Hence, computational algorithms are highly needed that can predict potential pupylation sites using sequence features. In this research, a new prediction model, PUP-Fuse, has been developed for pupylation site prediction by integrating multiple sequence representations. Meanwhile, we explored the five types of feature encoding approaches and three machine learning (ML) algorithms. In the final model, we integrated the successive ML scores using a linear regression model. The PUP-Fuse achieved a Mathew correlation value of 0.768 by a 10-fold cross-validation test. It also outperformed existing predictors in an independent test. The web server of the PUP-Fuse with curated datasets is freely available.

DOI： 10.3390/ijms22042120

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

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

Genomics, Proteomics and Bioinformatics 2021年01月

担当区分：責任著者記述言語：英語掲載種別：研究論文（学術雑誌）

Linear B-cell epitopes are critically important for immunological applications, such as vaccine design, immunodiagnostic test, and antibody production, as well as disease diagnosis and therapy. The accurate identification of linear B-cell epitopes remains challenging despite several decades of research. In this work, we have developed a novel predictor, Identification of B-Cell Epitope (iLBE), by integrating evolutionary and sequence-based features. The successive feature vectors were optimized by a Wilcoxon-rank sum test. Then the random forest (RF) algorithm using the optimal consecutive feature vectors was applied to predict linear B-cell epitopes. We combined the RF scores by the logistic regression to enhance the prediction accuracy. iLBE yielded an area under curve score of 0.809 on the training dataset and outperformed other prediction models on a comprehensive independent dataset. iLBE is a powerful computational tool to identify the linear B-cell epitopes and would help to develop penetrating diagnostic tests. A web application with curated datasets for iLBE is freely accessible at http://kurata14.bio.kyutech.ac.jp/iLBE/.

DOI： 10.1016/j.gpb.2019.04.004

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85106659050&origin=inward

Robust in-phase synchronization in repressor-based coupled gene oscillators 査読有り

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

IFAC-PapersOnLine 54 ( 15 ) 574 - 579 2021年01月

記述言語：英語掲載種別：研究論文（学術雑誌）

Inside living cells, proteins or mRNA can show oscillations even without a periodic driving force. Such genetic oscillations are precise timekeepers for cell-cycle regulations, pattern formation during embryonic development in higher animals, and daily cycle maintenance in most organisms. The synchronization between oscillations in adjacent cells happens via intercellular coupling, which is essential for periodic segmentation formation in vertebrates and other biological processes. While molecular mechanisms of generating sustained oscillations are quite well understood, how do simple intercellular coupling produces robust synchronizations are still poorly understood? To address this question, we investigate two models of coupled gene oscillators - activator-based coupled oscillators (ACO) and repressor-based coupled oscillators (RCO) models. In our study, a single autonomous oscillator (that operates in a single cell) is based on a negative feedback, which is delayed by intracellular dynamics of an intermediate species. For the ACO model (RCO), the repressor protein of one cell activates (represses) the production of another protein in the neighbouring cell after a intercellular time delay. We investigate the coupled models in the presence of intrinsic noise due to the inherent stochasticity of the biochemical reactions. We analyze the collective oscillations from stochastic trajectories in the presence and absence of explicit coupling delay and make careful comparison between two models. Our results show no clear synchronizations in the ACO model when the coupling time delay is zero. However, a non-zero coupling delay can lead to anti-phase synchronizations in ACO. Interestingly, the RCO model shows robust in-phase synchronizations in the presence and absence of the coupling time delay. Our results suggest that the naturally occurring intercellular couplings might be based on repression rather than activation where in-phase synchronization is crucial.

DOI： 10.1016/j.ifacol.2021.10.318

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RCGAToolbox: A Real-coded Genetic Algorithm Software for Parameter Estimation of Kinetic Models 査読有り

Maeda Kazuhiro, Boogerd Fred C., Kurata Hiroyuki

IPSJ Transactions on Bioinformatics ( 一般社団法人情報処理学会 ) 14 ( 0 ) 30 - 35 2021年01月

記述言語：英語掲載種別：研究論文（学術雑誌）

<p>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 (REX<sup>star</sup>/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.</p>

DOI： 10.2197/ipsjtbio.14.30

その他リンク： https://ci.nii.ac.jp/naid/130008120842

RCGAToolbox: A Real-coded Genetic Algorithm Software for Parameter Estimation of Kinetic Models 査読有り

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

IPSJ Transactions on Bioinformatics 14 30 - 35 2021年01月

記述言語：英語掲載種別：研究論文（学術雑誌）

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

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85122021287&origin=inward

Coupling protocol of interlocked feedback oscillators in circadian clocks 査読有り

Rashid M.M., Kurata H.

Journal of the Royal Society, Interface 17 ( 167 ) 2020年06月

担当区分：責任著者記述言語：英語掲載種別：研究論文（学術雑誌）

Circadian rhythms (approx. 24 h) show the robustness of key oscillatory features such as phase, period and amplitude against external and internal variations. The robustness of Drosophila circadian clocks can be generated by interlocked transcriptional-translational feedback loops, where two negative feedback loops are coupled through mutual activations. The mechanisms by which such coupling protocols have survived out of many possible protocols remain to be revealed. To address this question, we investigated two distinct coupling protocols: activator-coupled oscillators (ACO) and repressor-coupled oscillators (RCO). We focused on the two coupling parameters: coupling dissociation constant and coupling time-delay. Interestingly, the ACO was able to produce anti-phase or morning-evening cycles, whereas the RCO produced in-phase ones. Deterministic and stochastic analyses demonstrated that the anti-phase ACO provided greater fluctuations in amplitude not only with respect to changes in coupling parameters but also to random parameter perturbations than the in-phase RCO. Moreover, the ACO deteriorated the entrainability to the day-night master clock, whereas the RCO produced high entrainability. Considering that the real, interlocked feedback loops have evolved as the ACO, instead of the RCO, we first proposed a hypothesis that the morning-evening or anti-phase cycle is more essential for Drosophila than achieving robustness and entrainability.

DOI： 10.1098/rsif.2020.0287

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

Matsuoka Y., Kurata H.

Frontiers in Bioengineering and Biotechnology 8 2020年04月

担当区分：責任著者記述言語：英語掲載種別：研究論文（学術雑誌）

© Copyright © 2020 Matsuoka and Kurata. Lignocellulosic biomass can be hydrolyzed into two major sugars of glucose and xylose, and thus the strategy for the efficient consumption of both sugars is highly desirable. NADPH is the essential molecule for the production of industrially important value-added chemicals, and thus its availability is quite important. Escherichia coli mutant lacking the pgi gene encoding phosphoglucose isomerase (Pgi) has been preferentially used to overproduce the NADPH. However, there exists a disadvantage that the cell growth rate becomes low for the mutant grown on glucose. This limits the efficient NADPH production, and therefore, it is quite important to investigate how addition of different carbon source such as xylose (other than glucose) effectively improves the NADPH production. In this study, we have developed a kinetic model to propose an efficient NADPH production system using E. coli pgi-knockout mutant with a mixture of glucose and xylose. The proposed system adds xylose to glucose medium to recover the suppressed growth of the pgi mutant, and determines the xylose content to maximize the NADPH productivity. Finally, we have designed a mevalonate (MVA) production system by implementing ArcA overexpression into the pgi-knockout mutant using a mixture of glucose and xylose. In addition to NADPH overproduction, the accumulation of acetyl-CoA (AcCoA) is necessary for the efficient MVA production. In the present study, therefore, we considered to overexpress ArcA, where ArcA overexpression suppresses the TCA cycle, causing the overflow of AcCoA, a precursor of MVA. We predicted the xylose content that maximizes the MVA production. This approach demonstrates the possibility of a great progress in the computer-aided rational design of the microbial cell factories for useful metabolite production.

DOI： 10.3389/fbioe.2020.00277

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Recent development of machine learning methods in microbial phos-phorylation sites 査読有り

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

Current Genomics 21 ( 3 ) 194 - 203 2020年01月

担当区分：責任著者記述言語：英語掲載種別：記事・総説・解説・論説等（学術雑誌）

© 2020 Bentham Science Publishers. A variety of protein post-translational modifications has been identified that control many cellular functions. Phosphorylation studies in mycobacterial organisms have shown critical importance in diverse biological processes, such as intercellular communication and cell division. Recent technical advances in high-precision mass spectrometry have determined a large number of microbial phosphorylated proteins and phosphorylation sites throughout the proteome analysis. Identification of phosphorylated proteins with specific modified residues through experimentation is often labor-intensive, costly and time-consuming. All these limitations could be overcome through the application of machine learning (ML) approaches. However, only a limited number of computational phosphory-lation site prediction tools have been developed so far. This work aims to present a complete survey of the existing ML-predictors for microbial phosphorylation. We cover a variety of important aspects for developing a successful predictor, including operating ML algorithms, feature selection methods, win-dow size, and software utility. Initially, we review the currently available phosphorylation site data-bases of the microbiome, the state-of-the-art ML approaches, working principles, and their perfor-mances. Lastly, we discuss the limitations and future directions of the computational ML methods for the prediction of phosphorylation.

DOI： 10.2174/1389202921666200427210833

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

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

Computational and Structural Biotechnology Journal 18 906 - 912 2020年01月

担当区分：責任著者記述言語：英語掲載種別：研究論文（学術雑誌）

© 2020 The Authors N4-methylcytosine (4mC) is one of the most important DNA modifications and involved in regulating cell differentiations and gene expressions. The accurate identification of 4mC sites is necessary to understand various biological functions. In this work, we developed a new computational predictor called i4mC-Mouse to identify 4mC sites in the mouse genome. Herein, six encoding schemes of k-space nucleotide composition (KSNC), k-mer nucleotide composition (Kmer), mono nucleotide binary encoding (MBE), dinucleotide binary encoding, electron–ion interaction pseudo potentials (EIIP) and dinucleotide physicochemical composition were explored that cover different characteristics of DNA sequence information. Subsequently, we built six RF-based encoding models and then linearly combined their probability scores to construct the final predictor. Among the six RF-based models, the Kmer, KSNC, MBE, and EIIP encodings are sufficient, which contributed to 10%, 45%, 25%, and 20% of the prediction performance, respectively. On the independent test the i4mC-Mouse predicted the 4mC sites with accuracy and MCC of 0.816 and 0.633, respectively, which were approximately 2.5% and 5% higher than those of the existing method (4mCpred-EL). For experimental biologists, a freely available web application was implemented at http://kurata14.bio.kyutech.ac.jp/i4mC-Mouse/.

DOI： 10.1016/j.csbj.2020.04.001

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

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

Spinal Cord 2020年01月

記述言語：英語掲載種別：研究論文（学術雑誌）

© 2020, The Author(s), under exclusive licence to International Spinal Cord Society. Study design: Retrospective statistical analysis of database. Objectives: Prediction of the Spinal Cord Independence Measure version III Total Score (SCIM-TS) at 6 months after injury based on physical findings at 1 month after injury is an important index for rehabilitation approach in the recovery phase. Setting: Spinal Injuries Center, Fukuoka, Japan. Methods: The study participants were selected from patients with traumatic spinal cord injuries who were registered in the Japan Single Center Study for Spinal Cord Injury Data Base (JSSCI-DB) of the Japan Spinal Injuries Center specializing in spine and spinal cord injuries. Of the 534 participants registered with the JSSCI-DB between January 2012 and October 2018, we retrospectively extracted 137 participants for 6 months after injury, and these participants were included in this study. Results: According to multiple regression analysis, SCIM-TS at 6 months after injury could be predicted based on only six variables, i.e., age at injury, three key muscles (C6 wrist extensors, C8 finger flexors, and L3 knee extensors), and two mobility assessments (WISCI and SCIM−item13) (Adjusted R-Squared: 0.83). These six independent variables were significant factors reflecting SCIM-TS at 6 months. Conclusions: In rehabilitation after traumatic spinal cord injuries, a simple and reliable prognostic model can help accurately predict the achievable activity of daily living competency to set a goal. In addition, if the procedure is simple, evaluation can be completed in a short period of time, and the physical burden on both treating staff and patients can be reduced.

DOI： 10.1038/s41393-020-0488-5

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ヒト全身代謝システムの数理モデル招待有り査読有り

倉田博之

細胞 52 376 - 379 2020年01月

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

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

Plant Molecular Biology 2020年01月

担当区分：責任著者記述言語：英語掲載種別：研究論文（学術雑誌）

© 2020, Springer Nature B.V. DNA N6-methyladenine (6 mA) is one of the most vital epigenetic modifications and involved in controlling the various gene expression levels. With the avalanche of DNA sequences generated in numerous databases, the accurate identification of 6 mA plays an essential role for understanding molecular mechanisms. Because the experimental approaches are time-consuming and costly, it is desirable to develop a computation model for rapidly and accurately identifying 6 mA. To the best of our knowledge, we first proposed a computational model named i6mA-Fuse to predict 6 mA sites from the Rosaceae genomes, especially in Rosa chinensis and Fragaria vesca. We implemented the five encoding schemes, i.e., mononucleotide binary, dinucleotide binary, k-space spectral nucleotide, k-mer, and electron–ion interaction pseudo potential compositions, to build the five, single-encoding random forest (RF) models. The i6mA-Fuse uses a linear regression model to combine the predicted probability scores of the five, single encoding-based RF models. The resultant species-specific i6mA-Fuse achieved remarkably high performances with AUCs of 0.982 and 0.978 and with MCCs of 0.869 and 0.858 on the independent datasets of Rosa chinensis and Fragaria vesca, respectively. In the F. vesca-specific i6mA-Fuse, the MBE and EIIP contributed to 75% and 25% of the total prediction; in the R. chinensis-specific i6mA-Fuse, Kmer, MBE, and EIIP contribute to 15%, 65%, and 20% of the total prediction. To assist high-throughput prediction for DNA 6 mA identification, the i6mA-Fuse is publicly accessible at https://kurata14.bio.kyutech.ac.jp/i6mA-Fuse/.

DOI： 10.1007/s11103-020-00988-y

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Evolution of sequence-based bioinformatics tools for protein-protein interaction prediction 査読有り

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

Current Genomics 21 ( 6 ) 454 - 463 2020年01月

担当区分：責任著者記述言語：英語掲載種別：記事・総説・解説・論説等（学術雑誌）

© 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

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85089875758&origin=inward

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

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

Journal of Computer-Aided Molecular Design 2020年01月

担当区分：責任著者記述言語：英語掲載種別：研究論文（学術雑誌）

© 2020, Springer Nature Switzerland AG. A proinflammatory peptide (PIP) is a type of signaling molecules that are secreted from immune cells, which contributes to the first line of defense against invading pathogens. Numerous experiments have shown that PIPs play an important role in human physiology such as vaccines and immunotherapeutic drugs. Considering high-throughput laboratory methods that are time consuming and costly, effective computational methods are great demand to timely and accurately identify PIPs. Thus, in this study, we proposed a computational model in conjunction with a multiple feature representation, called ProIn-Fuse, to improve the performance of PIPs identification. Specifically, a feature representation learning model was utilized to generate the probabilistic scores by using the random forest models employing eight sequence encoding schemes. Finally, the ProIn-Fuse was constructed by linearly combining the resultant eight probabilistic scores. Evaluated through independent test, the ProIn-Fuse yielded an accuracy of 0.746, which was 10% higher than those obtained by the state-of-the-art PIP predictors. The proposed ProIn-Fuse can facilitate faster and broader applications of PIPs in drug design and development. The web server, datasets and online instruction are freely accessible at http://kurata14.bio.kyutech.ac.jp/ProIn-Fuse.

DOI： 10.1007/s10822-020-00343-9

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85091282929&origin=inward

Improvement of the memory function of a mutual repression network in a stochastic environment by negative autoregulation 査読有り国際誌

Hasan A., Kurata H., Pechmann S.

BMC Bioinformatics 20 ( 1 ) 2019年12月

担当区分：責任著者記述言語：英語掲載種別：研究論文（学術雑誌）

© 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

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Computational identification of microbial phosphorylation sites by the enhanced characteristics of sequence information 査読有り国際誌

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

Scientific Reports 9 ( 1 ) 2019年12月

担当区分：責任著者記述言語：英語掲載種別：研究論文（学術雑誌）

© 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

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85066627531&origin=inward

Ranking network mechanisms by how they fit diverse experiments and deciding on E. coli's ammonium transport and assimilation network 査読有り国際誌

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

npj Systems Biology and Applications 5 ( 1 ) 2019年12月

担当区分：責任著者記述言語：英語掲載種別：研究論文（学術雑誌）

© 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

その他リンク： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85064343332&origin=inward

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