2015/08/17 by Georg Basler, Basler, Georg, Zoran Nikoloski +9
Biochemistry, Genetics and Molecular Biology · Engineering · #Biofuel production and bioconversion #FOS: Biological sciences #Gene Regulatory Network Analysis #Microbial Metabolic Engineering and Bioproduction #Molecular Networks (q-bio.MN) #q-bio.MN
paper · pdf · doi:10.48550/arxiv.1509.01211
24 pages, 5 figures, 1 table
arxiv created 2015/08/17 · openalex publication_date 2015/08/17 · arxiv updated 2015/09/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.