Grants-in-Aid for Scientific Research (KAKENHI),
Ministry of Education, Culture, Sports, Science and Technology (MEXT)

A new science for detecting evolvability dynamics:
Integration of bioinformatics, evo-devo, and paleontology

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公開キックオフシンポジウム開催

6月7日(土)於東京大学本郷キャンパス 理学部1号館 Read more→

Objectives of the Research Area

Our understanding of evolution has been improved through comparative analyses and experiments using extant species. These approaches assume, consciously or unconsciously, uniformity, that ancestral species evolved based on the same properties as extant species. Is this premise really correct?
When we look at various evolutionary histories, we sometimes wonder if the evolvability (capability of evolution) was high in the early stages of evolution but then decreased, in other words, if evolution slowed down. However, there is still no framework for scientifically examining this naive question.
To overcome this situation, with the success of physical cosmology in our mind, we believe that in evolutionary biology, too, it is necessary to create an integrated research area where theory, experiment, and observation interact with each other.

In our research area, “Detecting Evolvability Dynamics,” we suspect the uniformity in evolution at various scales and examine the changes in evolvability between ancestors and descendants. Here, we aim at accumulating empirical data before constructing a new evolutionary theory, to clarify whether the decelerating evolution exists due to some mechanism.

News

Information about our latest research, seminar, and symposium

01.

Kickoff symposium

Date: June 7th (Sat.)
Venue: The Koshiba Hall, Faculty of Science Building 1, Hongo Campus, The University of Tokyo

Reception starts at 2:00 PM
Symposium: 2:20 PM – 5:30 PM
Discussion and Networking Reception: 6:00 PM – 8:00 PM

02.

Latest research findings

Coming Soon

03.

Seminars

Coming Soon

Our Team

Research Projects

Unraveling the dawn of vertebrate morphological evolution through integrated research in paleontology and evolutionary developmental biology

A01 Hirasawa Group

Exploring the evolvability of avian wings via deep learning-based morphological prediction

A02 Uesaka Group

Exploring the evolvability in the parallel evolution of color patterns in insects and fish

A03 Ando Group

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