Yu Matsuoka

503 total citations
21 papers, 396 citations indexed

About

Yu Matsuoka is a scholar working on Molecular Biology, Genetics and Biomedical Engineering. According to data from OpenAlex, Yu Matsuoka has authored 21 papers receiving a total of 396 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Molecular Biology, 6 papers in Genetics and 3 papers in Biomedical Engineering. Recurrent topics in Yu Matsuoka's work include Microbial Metabolic Engineering and Bioproduction (18 papers), Gene Regulatory Network Analysis (11 papers) and Bacterial Genetics and Biotechnology (6 papers). Yu Matsuoka is often cited by papers focused on Microbial Metabolic Engineering and Bioproduction (18 papers), Gene Regulatory Network Analysis (11 papers) and Bacterial Genetics and Biotechnology (6 papers). Yu Matsuoka collaborates with scholars based in Japan. Yu Matsuoka's co-authors include Kazuyuki Shimizu, Hiroyuki Kurata, Kazuhiro Maeda, Takashi Nakagawa, Takao A. Yamamoto, Satoshi Seino, Takuya Kinoshita, Takao Kodama, Kazuhiro Fukui and Tetsuyoshi Inoue and has published in prestigious journals such as Biotechnology Advances, Journal of Magnetism and Magnetic Materials and FEBS Journal.

In The Last Decade

Yu Matsuoka

21 papers receiving 391 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Yu Matsuoka Japan 12 298 79 56 41 31 21 396
Carles de Mas Spain 12 350 1.2× 154 1.9× 61 1.1× 40 1.0× 64 2.1× 23 466
Elvira Sgobba Germany 9 388 1.3× 174 2.2× 38 0.7× 48 1.2× 35 1.1× 9 449
Qingzhuo Wang China 10 248 0.8× 72 0.9× 70 1.3× 20 0.5× 40 1.3× 14 338
Song Jiao China 12 332 1.1× 115 1.5× 59 1.1× 50 1.2× 68 2.2× 17 478
Su‐Lim Choi South Korea 9 287 1.0× 127 1.6× 29 0.5× 47 1.1× 78 2.5× 13 417
Adam Westbrook Canada 10 452 1.5× 117 1.5× 105 1.9× 21 0.5× 55 1.8× 14 534
Xianpu Ni China 11 315 1.1× 62 0.8× 40 0.7× 23 0.6× 50 1.6× 22 445
Ruben Heck Netherlands 10 380 1.3× 135 1.7× 89 1.6× 16 0.4× 42 1.4× 11 499
Xiaoqun Nie China 10 367 1.2× 70 0.9× 39 0.7× 23 0.6× 33 1.1× 16 499

Countries citing papers authored by Yu Matsuoka

Since Specialization
Citations

This map shows the geographic impact of Yu Matsuoka's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Yu Matsuoka with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yu Matsuoka more than expected).

Fields of papers citing papers by Yu Matsuoka

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yu Matsuoka. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Yu Matsuoka. The network helps show where Yu Matsuoka may publish in the future.

Co-authorship network of co-authors of Yu Matsuoka

This figure shows the co-authorship network connecting the top 25 collaborators of Yu Matsuoka. A scholar is included among the top collaborators of Yu Matsuoka based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Yu Matsuoka. Yu Matsuoka is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Shimizu, Kazuyuki & Yu Matsuoka. (2021). Feedback regulation and coordination of the main metabolism for bacterial growth and metabolic engineering for amino acid fermentation. Biotechnology Advances. 55. 107887–107887. 45 indexed citations
2.
Matsuoka, Yu & Hiroyuki Kurata. (2020). Computer-Aided Rational Design of Efficient NADPH Production System by Escherichia coli pgi Mutant Using a Mixture of Glucose and Xylose. Frontiers in Bioengineering and Biotechnology. 8. 277–277. 4 indexed citations
3.
Shimizu, Kazuyuki & Yu Matsuoka. (2019). Redox rebalance against genetic perturbations and modulation of central carbon metabolism by the oxidative stress regulation. Biotechnology Advances. 37(8). 107441–107441. 58 indexed citations
4.
Shimizu, Kazuyuki & Yu Matsuoka. (2018). Regulation of glycolytic flux and overflow metabolism depending on the source of energy generation for energy demand. Biotechnology Advances. 37(2). 284–305. 56 indexed citations
5.
Matsuoka, Yu & Hiroyuki Kurata. (2017). Modeling and simulation of the redox regulation of the metabolism in Escherichia coli at different oxygen concentrations. Biotechnology for Biofuels. 10(1). 183–183. 18 indexed citations
6.
Maeda, Kazuhiro, et al.. (2016). Development of an accurate kinetic model for the central carbon metabolism of Escherichia coli. Microbial Cell Factories. 15(1). 112–112. 43 indexed citations
7.
Matsuoka, Yu, et al.. (2016). S-system-based analysis of the robust properties common to many biochemical network models. Bioprocess and Biosystems Engineering. 39(5). 735–746. 1 indexed citations
8.
Matsuoka, Yu & Kazuyuki Shimizu. (2015). Current status and future perspectives of kinetic modeling for the cell metabolism with incorporation of the metabolic regulation mechanism. Bioresources and Bioprocessing. 2(1). 17 indexed citations
9.
Inoue, Kentaro, et al.. (2014). CADLIVE toolbox for MATLAB: automatic dynamic modeling of biochemical networks with comprehensive system analysis. Bioprocess and Biosystems Engineering. 37(9). 1925–1927. 6 indexed citations
10.
Matsuoka, Yu & Kazuyuki Shimizu. (2014). 13C-Metabolic Flux Analysis for Escherichia coli. Methods in molecular biology. 1191. 261–289. 1 indexed citations
11.
Matsuoka, Yu & Kazuyuki Shimizu. (2014). Metabolic Flux Analysis for Escherichia coli by Flux Balance Analysis. Methods in molecular biology. 1191. 237–260. 3 indexed citations
12.
Matsuoka, Yu & Kazuyuki Shimizu. (2013). Metabolic regulation of <i>Escherichia coli</i> cultivated under anaerobic and aerobic conditions in response to the specific pathway gene knockouts. Advances in Bioscience and Biotechnology. 4(3). 455–468. 5 indexed citations
13.
14.
Kurata, Hiroyuki, Kazuhiro Maeda, & Yu Matsuoka. (2013). Dynamic Modeling of Metabolic and Gene Regulatory Systems toward Developing Virtual Microbes. JOURNAL OF CHEMICAL ENGINEERING OF JAPAN. 47(1). 1–9. 4 indexed citations
15.
Matsuoka, Yu & Kazuyuki Shimizu. (2012). IMPORTANCE OF UNDERSTANDING THE MAIN METABOLIC REGULATION IN RESPONSE TO THE SPECIFIC PATHWAY MUTATION FOR METABOLIC ENGINEERING OF ESCHERICHIA COLI. Computational and Structural Biotechnology Journal. 3(4). e201210018–e201210018. 15 indexed citations
16.
Matsuoka, Yu & Kazuyuki Shimizu. (2011). Metabolic regulation in Escherichia coli in response to culture environments via global regulators. Biotechnology Journal. 6(11). 1330–1341. 27 indexed citations
17.
Komatsu, Hideyuki, et al.. (2010). Kinetics of dextran‐independent α‐(1→3)‐glucan synthesis by Streptococcus sobrinus glucosyltransferase I. FEBS Journal. 278(3). 531–540. 15 indexed citations
18.
Matsuoka, Yu & Kazuyuki Shimizu. (2010). The relationships between the metabolic fluxes and 13C-labeled isotopomer distribution for the flux analysis of the main metabolic pathways. Biochemical Engineering Journal. 49(3). 326–336. 7 indexed citations
19.
Matsuoka, Yu & Kazuyuki Shimizu. (2010). Current status of 13C-metabolic flux analysis and future perspectives. Process Biochemistry. 45(12). 1873–1881. 19 indexed citations
20.
Seino, Satoshi, Yu Matsuoka, Takuya Kinoshita, Takashi Nakagawa, & Takao A. Yamamoto. (2009). Dispersibility improvement of gold/iron-oxide composite nanoparticles by polyethylenimine modification. Journal of Magnetism and Magnetic Materials. 321(10). 1404–1407. 20 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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