Control principles of metabolic networks

TL;DR

This paper introduces a computational framework to understand and identify key reactions controlling metabolic fluxes across diverse organisms, revealing differences between unicellular and multicellular species and implications for disease and biotechnology.

Contribution

It develops a comprehensive method for flux control analysis that overcomes previous limitations, enabling large-scale metabolic network analysis without predefined objectives.

Findings

Most unicellular organisms require fewer driver reactions than multicellular organisms.

Driver reactions in E. coli are strongly regulated transcriptionally.

In human cancer cells, driver reactions are crucial for tumor development and potential therapeutic targeting.

Abstract

Deciphering the control principles of metabolism and its interaction with other cellular functions is central to biomedicine and biotechnology. Yet, understanding the efficient control of metabolic fluxes remains elusive for large-scale metabolic networks. Existing methods either require specifying a cellular objective or are limited to small networks due to computational complexity. Here we develop an efficient computational framework for flux control by introducing a complete set of flux coupling relations. We analyze 23 metabolic networks from all kingdoms of life, and identify the driver reactions facilitating their control on a large scale. We find that most unicellular organisms require less extensive control than multicellular organisms. The identified driver reactions are under strong transcriptional regulation in Escherichia coli. In human cancer cells driver reactions play pivotal roles in tumor development, representing potential therapeutic targets. The proposed framework helps us unravel the regulatory principles of complex diseases and design novel engineering strategies at the interface of gene regulation, signaling, and metabolism.