Hokkaido University, Center of Education & Research for Topological Science & Technology

Topology in the life sciences

This project aims to analyze the topological structures of various networks found in biological systems, and to elucidate the relationships with biological phenomena.

One major theme is the elucidation of the relationships between tissue anomalies and topology. For instance, it is known that the topology of networks produced by tissue anomalies, as represented by cancer tissue, is different from the networks of normal tissues. As a method to characterize these networks, we will carry out analysis using their topological properties. In cooperation with the Novel topology-related technologies project, we will develop technology to visualize the tissue and extract its topological structure. The establishment of this technology may enable us to identify and diagnose cancer tissues without surgical operations.

Another theme of the life science topology studies is to elucidate the spatial topological recognition mechanism of the slime mold (myxomycete) network. Slime mold can freely change its shape, and is known to change its shape by recognizing the topology of its environment. As shown in the figures, when two feeders (AG) were provided to the slime mold plasmodia (marked yellow in Figure a), which was widely spread throughout the maze (approximately 4 cm square), the slime mold shrank its body in approximately 4 hours (as shown in Figure b), to the shortest route (as shown in Figure c), and gathered at the two feeders. This body shape enables the most efficient nutrient absorption possible. Therefore, the slime mold successfully solved the given maze, and obtained the nutrients. This project probes the topological mechanism of the ability to derive the shortest route, which can be considered a type of intelligence of the slime mold.

There are many other phenomena that suggest relationships between the statistical physical properties of various life system networks and their topologies. For instance, it is known that neural networks and the spin glass system have a complementary relationship, and it is possible that topological properties have some effect on memory efficiency. In addition, the network topology of life system interactions will have a substantial effect on the collective motion of those systems. We will explore the basis of life system topology underlying such relationships.