Laboratory for Biologically Inspired Computing | RIKEN BDR

Laboratory for Biologically Inspired Computing

Team Leader

Koichi TakahashiPh.D.

Photo of principal investigator

  • Location:Osaka / Quantitative Biology Buildings
  • E-mail:ktakahashi[at]riken.jpPlease replace [at] with @.

Accelerating life sciences by combining simulation, robotics and brain-inspired AI

Research Summary

We are pursuing two different but mutually interconnected research interests: research and development of biologically inspired computing architectures including artificial neural networks and applying such advanced information processing technologies to life science research. More specifically, we are working on development of BriCA (Brain-inspired Computing Architecture), a high-performance software platform for brain-inspired computing, development of high-performance genome-scale simulation software platform E-Cell System, and automating various processes in biological experiments by combining robotics and AI.

Research Theme

  • Genome-scale cell simulation software platform
  • Robotic biology
  • Brain-inspired computing architectures

Main Publications List

  • Itoh TD, Horinouchi T, Uchida H, et al.
    Optimal Scheduling for Laboratory Automation of Life Science Experiments with Time Constraints.
    SLAS Technology (2021) doi: 10.1177/24726303211021790
  • Ochiai K, Motozawa N, Terada M, et al.
    A variable-scheduling maintenance culture platform for mammalian cells
    SLAS Technology (2020) doi: 10.1177/2472630320972109
  • Yamakawa H, Arakawa N, Takahashi K.
    Reinterpreting The Cortical Circuit.
    Pre-proceedings of the IJCAI-17 Workshop on Architectures for Generality & Autonomy (2017)
  • Yachie N, Natsume T, Takahashi K, et al.
    Robotic crowd biology with Maholo LabDroids.
    Nature Biotechnology 35(4). 310-312 (2017) doi: 10.1038/nbt.3758
  • Iwamoto K, Shindo Y, Takahashi K.
    Modeling Cellular Noise Underlying Heterogeneous Cell Responses in the Epidermal Growth Factor Signaling Pathway.
    Plos Computational Biology 12(11). e1005222 (2016) doi: 10.1371/journal.pcbi.1005222
  • Itaya K, Takahashi K, Nakamura M, et al.
    BriCA: A modular software platform for whole brain architecture.
    Neural information processing – 23rd international conference, ICONIP 334-341. (2016)
  • Shindo Y, Iwamoto K, Mouri K, et al.
    Conversion of graded phosphorylation into switch-like nuclear translocation via autoregulatory mechanisms in ERK signalling.
    Nature Communications 7. 10485 (2016) doi: 10.1038/ncomms10485
  • Karr JR, Takahashi K, Funahashi A.
    The principles of whole-cell modeling.
    Current Opinion in Microbiology 27. 18-24 (2015) doi: 10.1016/j.mib.2015.06.004
  • Watabe M, Arjunan SNV, Fukushima S, et al.
    A Computational Framework for Bioimaging Simulation.
    Plos One 10(7). e0130089 (2015) doi: 10.1371/journal.pone.0130089
  • Shimo H, Arjunan SNV, Machiyama H, et al.
    Particle Simulation of Oxidation Induced Band 3 Clustering in Human Erythrocytes.
    Plos Computational Biology 11(6). UNSP e1004 (2015) doi: 10.1371/journal.pcbi.1004210
  • Kaizu K, de Ronde W, Paijmans J, et al.
    The Berg-Purcell Limit Revisited.
    Biophysical Journal 106(4). 976-985 (2014) doi: 10.1016/j.bpj.2013.12.030
  • Hihara S, Pack CG, Kaizu K, et al.
    Local Nucleosome Dynamics Facilitate Chromatin Accessibility in Living Mammalian Cells.
    Cell Reports 2(6). 1645-1656 (2012) doi: 10.1016/j.celrep.2012.11.008
  • Takahashi K, Tanase-Nicola S, ten Wolde PR.
    Spatio-temporal correlations can drastically change the response of a MAPK pathway.
    Proceedings of the National Academy of Sciences of the United States of America 107(6). 2473-2478 (2010) doi: 10.1073/pnas.0906885107