Cells are massive factories, containing a multitude of substations devoted to specific tasks all devoted to keeping the overarching organism alive. Until now, researchers have questioned how such diverse components evolve in tandem — especially when each component can evolve in a variety of ways.
Two researchers based in Tokyo, Japan, have developed a statistical physics model to demonstrate how such evolution is possible. The results were published on May 26 in Physical Review Letters.
Their work is based on the idea that the potential evolutionary tracts of each cellular component are considered to be high dimensional, but that the function of each component actually evolves in way to make the cell as a whole the most fit. This is low dimensionality, where each piece can be affected by the other pieces as a result of the evolution.
“How can such complex systems adapt to environment and evolve by somehow controlling such large degrees of freedom?” said Ayaka Sakata, an associate professor at the Institute of Statistical Mathematics. “Recent experiments have suggested that physical changes due to adaptation and evolution are highly constrained in a low-dimensional subspace. How does a drastic dimension reduction emerge?”
Sakata and co-author Kunihiko Kaneko, a professor of theoretical biology at the University of Tokyo, used a spin-glass model to understand how each piece of the puzzle evolves individually to improve the system as a whole. The model is based on how an organized system — where the system repeats in a stable fashion — can transition to a disorganized system — where the connections are stable but random as the materials change from nonmagnetic to magnetic. The spins, which is an intrinsic property of electrons in such materials, become aligned under a certain condition, a phenomenon that can be mapped in biology, where the activity of genes instead of the spins.
“By formulating the problem in terms of the statistical physics of a spin-glass model, we demonstrate that the dimensional reduction emerges through the evolution of robustness to noise,” Sakata said.
The noise is the unpredictability of variation in the environment that can cause changes to the cellular components themselves. Computer simulations of evolution under high noise led to random, disorganized changes while low noise led to too much variability in the components. The evolution under a moderate level of noise led to low dimensionality where the variety of the components is restricted, a characteristic considered to be a result of evolution robust to this noise level.
“Although the present statistical physics model is highly simplified, it gives a theoretical basis for dimension reduction in biological systems, in which robustness to noise is also essential,” Sakata said. “In fact, the present model can be interpreted as the evolution of protein to have a certain function.”
This work was supported in part by the Japanese Society for the Promotion of Science and the Ministry of Education, Culture, Sports, Science and Technology of Japan.
About the Research Organization of Information and Systems (ROIS)
ROIS is a parent organization of four national institutes (National Institute of Polar Research, National Institute of Informatics, the Institute of Statistical Mathematics and National Institute of Genetics) and the Joint Support-Center for Data Science Research. It is ROIS’s mission to promote integrated, cutting-edge research that goes beyond the barriers of these institutions, in addition to facilitating their research activities, as members of inter-university research institutes.
About the Institute of Statistical Mathematics
The Institute of Statistical Mathematics (ISM) is part of Japan’s Research Organization of Information and Systems (ROIS). With more than 75 years of history, the institute is an internationally renowned facility for research on statistical mathematics including machine learning and evolutionary biology. ISM comprises three different departments including the Department of Statistical Modeling, the Department of Statistical Data Science, and the Department of Statistical Inference and Mathematics, as well as several key data and research centers. Through the efforts of various research departments and centers, ISM aims to continuously facilitate cutting edge research collaboration with universities, research institutions, and industries both in Japan and other countries.
URA Station Planning Unit, ISM