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Takigawa Lab
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The lab develops new information science and machine learning to understand life.
AboutAbout the lab
New information science for tackling the complexity of life
The Takigawa Lab conducts research to create "new information science" that helps us understand life.
The lab does no experiments and is a 100% dry lab, doing all of its research computationally.
The lab designs machine learning and machine discovery algorithms for complex data that cannot be represented by numbers alone, such as metabolism, chemical reactions, and three-dimensional structures.
It is a new lab that started in April 2025, and it accepts undergraduate thesis students from the Department of Bioinformatics and Systems Biology.
Key terms
- Machine learning
- A technology that lets computers learn patterns from large amounts of data and use them to make predictions or decisions
- AI for Science
- A research field that aims to apply artificial intelligence to scientific discovery and understanding themselves
- Non-equilibrium dynamics
- The behavior of a system in which matter and energy continuously flow in and out, so that it keeps changing and never settles into a static state
NewsLab news
View More ↗ResearchResearch themes
Using the analysis of molecular interactions and non-equilibrium dynamics as testbeds, the lab designs machine learning and machine discovery algorithms for complex data. Its goal is to build new tools for the life sciences that reveal how information allows "life" to assemble itself from ordinary materials.
*Illustrative imageMachine learning for structured data
The lab redesigns machine learning itself to suit data with complex structure rather than simple numerical values, such as genomes, proteins, gene networks, and molecular structures.
*Illustrative imageMachine discovery and machine understanding
Viewing AI for Science as "the science of how to do science," the lab uses life as a testbed to explore new tools for reaching scientific discovery and understanding.
*Illustrative imageFrom life as chemistry to life as information
The lab's main interest is understanding intermolecular interactions and non-equilibrium dynamics. It validates the tools it builds on testbeds such as metabolism, transcription, catalysis, chemical reactions, mass spectrometry, and three-dimensional structures.
MembersFaculty
The members of the Takigawa Lab include faculty, postdoctoral researchers, master's and undergraduate students, visiting students, and administrative assistants.As of 2026
- ProfessorIchigaku TakigawaPublications ↗
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “Docking representation 2” Scigenis / CC BY-SA 4.0 (cropped)
- Machine learning for structured data: “GNN building blocks” NickDiCicco / CC BY-SA 4.0 (cropped)
- Machine discovery and machine understanding: “Artificial Neural Network with Chip” mikemacmarketing / photo on flickr / CC BY 2.0 (cropped)
- From life as chemistry to life as information: “Carbon Catabolism” Mplanine / CC BY 4.0 (cropped)
- Related lab (Tsuda Lab): “Caffeine molecule ball from xtal (1)” Jynto (more from this user) / CC0 (cropped)
- Related lab (Frith Lab): “DNA double helix 45” Jerome Walker,Dennis Myts / Public domain (cropped)
- Related lab (Kiryu Lab): “RNA structure (2)” Thomas Shafee / CC BY 4.0 (cropped)
- Related lab (Kasahara Lab): “MinION portable sequencer” Mongan A.E. / CC BY 4.0 (cropped)
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