Computational modelling of metabolic processes has proven to be a useful approach to formulate our knowledge and improve our understanding of core biochemical systems that are crucial to maintaining cellular functions. Towards understanding the broader role of metabolism on cellular decision-making in health and disease conditions, it is important to integrate the study of metabolism with other core regulatory systems and omics within the cell, including gene expression patterns. After quantitatively integrating gene expression profiles with a genome-scale reconstruction of human metabolism, we propose a set of combinatorial methods to reverse engineer gene expression profiles and to find pairs and higher-order combinations of genetic modifications that simultaneously optimize multi-objective cellular goals. This enables us to suggest classes of transcriptomic profiles that are most suitable to achieve given metabolic phenotypes. We demonstrate how our techniques are able to compute beneficial, neutral or “toxic” combinations of gene expression levels. We test our methods on nine tissue-specific cancer models, comparing our outcomes with the corresponding normal cells, identifying genes as targets for potential therapies. Our methods open the way to a broad class of applications that require an understanding of the interplay among genotype, metabolism, and cellular behaviour, at scale.
|Journal||IEEE/ACM Transactions on Computational Biology and Bioinformatics|
|Early online date||2 Apr 2020|
|Publication status||Published - 2 Apr 2020|
Occhipinti, A., Hamadi, Y., Kugler, H., Wintersteiger, C., Yordanov, B., & Angione, C. (2020). Discovering Essential Multiple Gene Effects through Large Scale Optimization: an Application to Human Cancer Metabolism. IEEE/ACM Transactions on Computational Biology and Bioinformatics. https://ieeexplore.ieee.org/document/9055142