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Uda Shinsuke Last modified date:2022.07.05

Associate Professor / Division of Integrated Omics, Research Center for Transomics Medicine, Medical Institute of Bioregulation
Medical Institute of Bioregulation

Graduate School

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 Reseacher Profiling Tool Kyushu University Pure
Academic Degree
Country of degree conferring institution (Overseas)
Field of Specialization
Information science, Machine learning, Systems biology
Total Priod of education and research career in the foreign country
Research Interests
  • Analysis of cellular signal transduction from information theoretic approach
    keyword : Cellular signal transduction, Information theory
  • Analysis of multi-omics data by statistical methods
    keyword : Multi-omics (Transomics), Statistical method, Machine learning
Academic Activities
1. Fumiko Matsuzaki, Shinsuke Uda, Yukiyo Yamauchi, Masaki Matsumoto, Tomoyoshi Soga, Kazumitsu Maehara, Yasuyuki Ohkawa, Keiichi I Nakayama, Shinya Kuroda, Hiroyuki Kubota, An extensive and dynamic trans-omic network illustrating prominent regulatory mechanisms in response to insulin in the liver., Cell reports, 10.1016/j.celrep.2021.109569, 36, 8, 109569-109569, 2021.08.
2. Shinsuke Uda, Application of information theory in systems biology, Biophysical Reviews, 10.1007/s12551-020-00665-w, 12, 2, 377-384, 2020.04, Over recent years, new light has been shed on aspects of information processing in cells. The quantification of information, as described by Shannon’s information theory, is a basic and powerful tool that can be applied to various fields, such as communication, statistics, and computer science, as well as to information processing within cells. It has also been used to infer the network structure of molecular species. However, the difficulty of obtaining sufficient sample sizes and the computational burden associated with the high-dimensional data often encountered in biology can result in bottlenecks in the application of information theory to systems biology. This article provides an overview of the application of information theory to systems biology, discussing the associated bottlenecks and reviewing recent work..
3. Hiroyuki Kubota, Shinsuke Uda, Fumiko Matsuzaki, Yukiyo Yamauchi, Shinya Kuroda, In Vivo Decoding Mechanisms of the Temporal Patterns of Blood Insulin by the Insulin-AKT Pathway in the Liver, Cell Systems, 10.1016/j.cels.2018.05.013, 7, 1, 118-128.e3, 2018.07, Cells respond to various extracellular stimuli through a limited number of signaling pathways. One strategy to process such stimuli is to code the information into the temporal patterns of molecules. Although we showed that insulin selectively regulated molecules depending on its temporal patterns using Fao cells, the in vivo mechanism remains unknown. Here, we show how the insulin-AKT pathway processes the information encoded into the temporal patterns of blood insulin. We performed hyperinsulinemic-euglycemic clamp experiments and found that, in the liver, all temporal patterns of insulin are encoded into the insulin receptor, and downstream molecules selectively decode them through AKT. S6K selectively decodes the additional secretion information. G6Pase interprets the basal secretion information through FoxO1, while GSK3β decodes all secretion pattern information. Mathematical modeling revealed the mechanism via differences in network structures and from sensitivity and time constants. Given that almost all hormones exhibit distinct temporal patterns, temporal coding may be a general principle of system homeostasis by hormones. Kubota et al. show that the insulin-AKT pathway in the liver processes the information encoded into the temporal patterns of blood insulin and selectively regulates downstream molecules. Given that almost all hormones exhibit distinct temporal patterns, our study demonstrates the possibility of temporal coding as a general principle of systemic homeostasis by hormones..
4. Shinsuke Uda, Takeshi H. Saito, Takamasa Kudo, Toshiya Kokaji, Takaho Tsuchiya, Hiroyuki Kubota, Yasunori Komori, Yu Ichi Ozaki, Shinya Kuroda, Robustness and compensation of information transmission of signaling pathways, Science, 10.1126/science.1234511, 341, 6145, 558-561, 2013.01, Robust transmission of information despite the presence of variation is a fundamental problem in cellular functions. However, the capability and characteristics of information transmission in signaling pathways remain poorly understood. We describe robustness and compensation of information transmission of signaling pathways at the cell population level. We calculated the mutual information transmitted through signaling pathways for the growth factor-mediated gene expression. Growth factors appeared to carry only information sufficient for a binary decision. Information transmission was generally more robust than average signal intensity despite pharmacological perturbations, and compensation of information transmission occurred. Information transmission to the biological output of neurite extension appeared robust. Cells may use information entropy as information so that messages can be robustly transmitted despite variation in molecular activities among individual cells..
1. Shinsuke Uda, Network structure inference by conditional independence, 第57回日本生物物理学会年会, 2019.09.
2. Shinsuke Uda, Shinya Kuroda, Robustness and compensation of information transmission of signaling pathways despite perturbation, the Joint Annual Meeting of the Japanese Society for Mathematical Biology and the Society for Mathematical Biology, 2014.07.
Membership in Academic Society
  • Physical society of Japan
  • MBSJ