Complex versus Complicated Systems Biology, Universality versus Detailed Modelling

TL;DR

This paper discusses the contrast between complicated and complex systems biology models, emphasizing the importance of universality and detailed modeling, and highlights integrating data-driven omics with universal approaches.

Contribution

It provides a conceptual framework comparing complicated and complex models and advocates for combining universality principles with data-driven methods in systems biology.

Findings

Universal properties are supported by evolutionary robustness.

Dimensionality reduction aids in understanding high-dimensional states.

Integrating omics data with universality enhances systems biology.

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.