Computational Systems Biology

Research Staff

  • Prof. Shigehiko Kanaya

    Prof.
    Shigehiko Kanaya

  • Prof. Hidehiro Iida

    Affiliate Prof.
    Hidehiro Iida

  • Assoc.Prof. Altaf-Ul-Amin

    Assoc.Prof.
    Altaf-Ul-Amin

  • Assist.Prof. Naoaki Ono

    Assoc.Prof.
    Naoaki Ono

  • Affiliate Assoc.Prof.Tetsuo Sato

    Affiliate Assoc.Prof.
    Tetsuo Sato

  • Assist.Prof. Mei Kou

    Assist.Prof.
    Ming Huang

E-mail { skanaya, iidahide, amin-m, nono, tsato, alex-mhuang }[at] is.naist.jp

Research Areas

Systems biology

Biology has been significantly advanced by reductive approaches. Huge biological data sets, such as more than 1,000 genome sequences, have caused a paradigm shift into a holistic approach to understanding living things as systems. We study these approaches by modeling several biological systems to elucidate cellular mechanisms. In this field, we keep incorporating state-of-the-art data modeling/manipulating techniques such as deep learning techniques to better our understanding.

Network analysis

With the development of omics technologies, it has become imperative to systematically analyze all biological components (genes, mRNA, proteins and metabolites). To meet this challenge, we have developed a clustering algorithm (DPClus) to extract highly connected clusters.

Transcriptomes

A transcriptome is defined as a total set of transcripts in an organism. To elucidate transcriptome networks, we study transcriptome analyses using microarrays and new generation sequencers with the use of BL-SOM and novel methods.

Metabolomes

Cells consist of a few thousand molecules. Of those, metabolites are mainly produced by enzymatic reactions. The objective of metabolome analysis is to comprehensively identify which particular metabolites affect cellular networks. As a metabolome analysis platform, we have developed a species-metabolite database, KNApSAcK, covering almost all reported metabolites. To date, 50,048 metabolites and 101,500 species-metabolite relationships have been accumulated.

Biomedical informatics

In collaboration with medical hospitals and other academic institutions, we are developing various biomedical engineering technologies based on information technology and state-of-the-art deep learning techniques.

A computer-aided diagnosis assistance system for Medical images

A computer-aided educational system for radiologist training

Health informatics

By incorporation of the strengths of the wearable/unconstrained sensing techniques and state-of-the-art information technology such as deep learning techniques, we are developing health monitoring systems for daily use.

A wearable deep body thermometer monitoring system

A cuffless blood pressure monitoring system

A heart health monitoring system based on contactless electrocardiograph

Medical imaging

A cardiac MRI in clinical imaging for coronary arteries and decision support technology for motion compensation has been developed. Diffusion Tensor MRI (DT-MRI) and tractography techniques are being investigated for the analysis of human brain cognitive functions.

Volume visualization in biology

We have developed a high speed volume rendering method for visualizing high resolution microscopic 3D images such as two-photon microscopy techniques.

Volume graphics

Neuron tracing

Microscope image analysis

Key Features

We work in an interdisciplinary field between information technology and bio-medical science. Our aim is to integrate knowledge in biology, medical science, and health-care. Students study a wide variety of technologies, such as signal and image processing, data-analysis and machine learning. We have been developing techniques to understand gene function and disease mechanisms.

Our laboratory members, who have come from a wide variety of backgrounds, aim to elucidate the robustness and diversity of biological systems throughby chemo- and bio-informatics. In our lab, students study a wide range of areas and attain broad perspectives. We always discuss important issues regarding research to enhance each other's knowledge.

Fig.1 Feature Map: Expression Profile in Bacillus subtilis

Fig.1 Feature map: expression profile in Bacillus subtilis

Fig.2 Main page of "KNApSAcK Family"

Fig.2 Main page of "KNApSAcK Family"

(http://kanaya.naist.jp/ KNApSAcK_Family/)

Fig.3  Examples of biomedical imaging taken by various imaging schemes

Fig.3 Examples of biomedical imaging taken by various imaging schemes