Complex versus Complicated Systems Biology, Universality versus Detailed Modelling

Abstract

Biological systems are generally complicated and/or complex. In the former approach, one sets up a model with a large number of parameters to describe the system in detail. The latter approach focuses on understanding the universal aspects of biological systems. In this case, an appropriate simple model represents a universality class. The extraction of universal properties is supported by evolutionary robustness and the reduction of dimensionality in high-dimensional states. Integrating the data-driven omics approach with the universality approach is an important step in systems biology.

Figures (2)

• Figure 1: Evolutionary dimensional reduction. Even though biological states are potentially quite high-dimensional, most changes induced by noise, genetic changes, and environmental changes are constrained along a common low-dimensional space (manifold), if we focus on a robust state achieved by evolution. The low-dimensional structure as in this figure was observed in a reaction network model of cells after evolutionCFKK-PRE. The adopted model involves thousand components, and thus the dimension of the system is thousand, but the low-dimensional structure as in the figure was detected by principal component analysis.
• Figure 2: Models and universality class. There are many models that show essentially same behavior, when one focuses on macroscopically universal properties. Such range of models form a universality class. If some "coarse-graining procedure" to eliminate details is defined, an idealistic simple model can be reached by it.