Research unit for coevolution and coexistence

The purpose of this project is to clarify the mechanisms of symbiosis and coevolution driven by environmental changes and interactions with other organisms. Based on those findings, we will explore ecosystem transformations and the potential for sustainable coexistence from an international perspective. We will systematically discover, identify, and investigate specific examples of symbiosis and coevolution among diverse species, including humans, and analyze the accompanying changes in ecosystems from both fundamental and applied perspectives. This project takes a comprehensive approach to a wide range of topics, including symbiotic relationships among humans, between humans and nature, between animals and plants, as well as the molecular coevolution between DNA and transcription factors, and between proteins. We aim not only to elucidate the mechanisms underlying diverse forms of coexistence and coevolution, but also to explore the mechanisms by which ecosystems respond to changes at both the individual organism and community levels. Another focus of this project is to understand how symbiotic relationships are altered by human activities in various ecosystems such as oceans, forests, and satoyama (woodland areas surrounding villages). This project will clarify the adaptive strategies that organisms use to respond to environmental changes and stress, focusing on topics such as the impact of underwater noise on marine animals, the shift in wildlife distribution areas due to changes in forest use, and the effects of agricultural expansion on the balance between plants and animals in satoyama ecosystems. Our goal is to construct a model of symbiosis and coexistence between humans and both plants and animals within a framework we refer to as “One World”.

The significant advancement of information science and data science is driving digitalization across all facets of society. Meanwhile, the societal challenges requiring academic engagement—both classical issues predating the modern era (such as healthcare and social stability) and those emerging from the modern era through the 20th century (such as technological development, global environment, and energy constraints)—continue to deepen. Furthermore, the progress of digitalization itself is bringing new developments to these challenges. This presents two aspects: the potential for digital technologies to bring new solutions to each challenge, and the risk of further complicating and exacerbating existing challenges or inducing new ones. The purpose of this unit is to organize a research field that contributes to solving societal challenges in a digital society by promoting transdisciplinary research that integrates fieldwork and data science. Various academic disciplines will collaborate to explore how effective solutions to societal challenges transformed by digitalization can be provided through the application of digital technologies.

At various levels in nature, we observe phenomena in which the behavior of an entire system emerges from the interactions of numerous factors. The aim of this unit is to understand the dynamics of such complex systems from the topology of interactions between factors. In recent years, it has been independently demonstrated in different fields of natural science, such as physics and life sciences, that the topology of interactions is critically important for dynamics. For example, in life sciences, it has been found that important subsets of genes can be determined based on mathematical theory using only the topology of gene networks. These theories of topology are independent of the specific properties of phenomena and therefore have the potential to be applied to different levels and phenomena. In this unit, researchers from physics, chemistry, life sciences, ecology, and economics will engage in interdisciplinary research, sharing methods and insights across fields. This will advance our understanding of complex systems at different levels. Through the activities of this unit, we will seek to identify common methods and universal principles for complex systems in general.