Laboratories for Mathematics, Lifesciences, and Informatics


Research


_ Neuroscience and Biological Information Processing

We have theoretically studied neural network models [1-3] and developed new methods to analyze real data of neuronal spikes [4-6] for understanding information processing and various behaviors in living systems. We are also collaborating with other laboratories performing experiments on nervous activities [7]. Moreover, we are constructing a basic methodology for realizing a novel computation device by using information processing schemes of living systems [8].

  1. Y. Kakimoto and K. Aihara, New Math. and Natural Comput., Vol.5, No. 1, pp.123-134 (2009).
  2. T. Kanamaru and K. Aihara, Neural Comput., Vol.22, No.5, pp.1383-1398 (2009).
  3. S. Kubota, K. Hamaguchi, and K. Aihara, Neural Computing and Applications, Vol.18, No.6, pp.591-602 (2009).
  4. K. Fujiwara and K. Aihara, J. Artificial Life and Robotics, Vol.13, No.2, pp.470-473 (2009).
  5. K. Fujiwara and K. Aihara, European Phys. J. B, Vol.68, No.2, pp.283-289 (2009).
  6. Y. Hirata and K. Aihara, J. Neurosci. Methods, Vol.183, No.2, pp.277-286 (2009).
  7. H. Mushiake, K. Sakamoto, N. Saito, T. Inui, and K. Aihara, Int. Review of Neurobiology, Vol.85, pp.1-11 (2009).
  8. H. Tanaka, T. Morie, and K. Aihara, IEICE Trans. Fund. Electron. Comm. Comput. Sci., Vol.E92-A, No.7, pp.1096-1098 (2009).

_ Nonlinear Dynamical Systems and its Applications

The aim of our research on nonlinear science is to understand the essence of complex behaviors through mathematical modelling and analyses of nonlinear systems. The topics of our research include coupled oscillators [1,2], game theory [3,4], complex networks [5], recurrence plots [6], and hybrid dynamical systems [7]. We are also trying to develop a new method to address determinism and nonlinearity of real systems and applying them to data analysis of wind profile [8], gray-scale image restoration [9], pattern analysis of partial discharges, and economic data analysis.

  1. I. Nishikawa, N. Tsukamoto, and K. Aihara, Physica D, Vol.238, pp.1197-1202 (2009).
  2. Y. Hirata, M. Aono, and K. Aihara, Chaos, Vol.20, 013117 (2010).
  3. B. Wang, Y. Han, L. Chen, and K. Aihara, Phys. Lett. A, Vol.373, pp.1519-1523 (2009).
  4. K. Hashimoto and K. Aihara, J. Theor. Biol., Vol.258, pp.637-645 (2009).
  5. H. Fan, Z. Wang, L. Chen, and K. Aihara, Phys. Rev. E, Vol.79, 026107 (2009).
  6. Y. Hirata and K. Aihara, Phys. Rev. E, Vol.81, 016203 (2009).
  7. G. Tanaka, S. Tsuji, and K. Aihara, Phys. Lett. A, Vol.373, pp.3134-3139 (2009).
  8. D. Mandic, S. Javidi, A. Kuh, and K. Aihara, Renewable Energy, Vol.34, No.1, pp.196-201 (2009).
  9. G. Tanaka and K. Aihara, IEEE Trans, Neural Networks, Vol.20, No.9, pp.1463-1473 (2009).

_ Mathematical Modeling of Biochemical Systems

We have constructed mathematical models and developed methods for analyzing experimental images and data to understand quantitative features of cell development and differentiation. We have clarified mechanisms and roles of stochastic fluctuations in cell systems [1,2] and elucidated the mechanism of circadian rhythms related to jet lag syncrome [3]. Moreover, we perform theoretical studies on oscillations signal transductions in biomolecular networks [4-7].

  1. H. Tozaki, T. J. Kobayashi, et al., FEBS Letter, Vol.582, No.7, pp.1067-1072 (2008).
  2. H. Okano, T. J Kobayashi, et al., Biophys. J., Vol.95, pp.1063-1074 (2008).
  3. H. Ukai, T. J. Kobayashi, et al., Nature Cell Biology, Vol.9, No.11, pp.1327—1334 (2007).
  4. R. Wang, C. Li, L. Chen, and K. Aihara, Proc. IEEE, Vol.96, No.8, pp.1361-1385 (2008).
  5. X.-M. Zhao, R.-S. Wang, L. Chen, and K. Aihara, Nucleic Acids Res., Vol.36, No.9, e48 (2008).
  6. X.-M. Zhao, R.-S. Wang, L. Chen, and K. Aihara, Journal of Bioinformatics and Computational Biology, Vol.7, No.2, pp.309-322 (2009).
  7. L. Chen, R. Wang, C. Li, and K. Aihara, Modeling Biomolecular Networks in Cells: Structures and Dynamics?, Springer (2010).

_ Mathematical Modeling of Diseases

We have been studied mathematical modeling of diseases that should be addressed emergently. Especially, we aim at understanding the essential mechanism and the origin of a disease and proposing a possible effective approach to avoid or treat the disease. We have constructed mathematical models of prostate cancer and discussed the efficacy of intermittent hormone therapy compared with conventional continuous one [1-6]. We have also developed mathematical methods to predict clinical time series data of the biomarker [7] and optimize the schedule of treatments [8] towards practical applications. Infectious diseases like pandemic flu are the other major topic of our research, for which we have approached with mathematical model analyses [9] and large-scale simulations based on individual-based-models by using person trip data.

  1. A. Ideta, G. Tanaka, T. Takeuchi, and K. Aihara, J. Nonlinear Sci. Vol.18, No.6, 593-614 (2008).
  2. G. Tanaka, K. Tsumoto, S. Tsuji, and K. Aihara, Physica D , Vol.237, No.20, 2616-2627 (2008).
  3. N. Shimada and K. Aihara, Math. Biosci. Vol.214, No.1/2, 134-139 (2008).
  4. Q. Guo, Y. Tao, and K. Aihara, Int. J. Bifur. Chaos, Vol.18, No.12, 3789-3797 (2008).
  5. Y. Tao, Q. Guo, and K. Aihara, J. Nonl. Sci., Vol.20, No.2, pp.219-240 (2010).
  6. G. Tanaka, Y. Hirata, N. Bruchovsky, S. Goldenberg, and K. Aihara, Phil. Trans. R. Soc. A, Vol.368, pp.5029-5044 (2010).
  7. Y. Hirata, N. Bruchovsky, and K. Aihara, J. Theor. Biol., Vol.264, No.2, pp.517-527 (2010).
  8. T. Suzuki, N. Bruchovsky, and K. Aihara, Phil. Trans. R. Soc. A, Vol.368, pp.5045-5059 (2010).
  9. B. Wang, K. Aihara, and B. J. Kim, Phys. Lett. A, Vol.373, pp.3877-3882 (2009).