Discovering Essential Multiple Gene Effects Through Large Scale Optimization: An Application to Human Cancer Metabolism.

Annalisa Occhipinti,Hillel Kugler,Christoph M Wintersteiger,Boyan Yordanov,Claudio Angione,Youssef Hamadi

doi:10.1109/tcbb.2020.2973386

Abstract

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.

Highlights

Metabolism, the set of biochemical reactions that transform various compounds in living cells and organisms, is one of the core systems responsible for maintaining cellular functions
In order to add transcriptomic information to an Flux Balance Analysis (FBA) model in a quantitative fashion, we model the effect of each gene expression profile as a change in the lower and upper bounds of the metabolic reactions, yielding a rerouted flux distribution across the network
In the bilevel formulation in Eq (3), we take as a secondlevel objective t v the minimization/maximization of phosphoglycerate dehydrogenase (PHGDH) in the model, with the maximum/minimum possible biomass

Summary

Introduction

Metabolism, the set of biochemical reactions that transform various compounds in living cells and organisms, is one of the core systems responsible for maintaining cellular functions. Metabolic models (reconstructions) of bacteria have been developed to facilitate the study and manipulation of biochemical processes [1], allowing the bio-production of valuable compounds to be optimized through metabolic engineering [2]. The study of human metabolism, on the other hand, is becoming increasingly important for biomedical applications as an approach for understanding health and diseases. This is enabled by the availability of human metabolic reconstructions [3], [4], A. Occhipinti is with the Department of Computer Science and Information Systems, Teesside University, UK. Angione is with the Department of Computer Science and Information Systems, and with the Healthcare Innovation Centre, Teesside University, UK which integrate extensive metabolic information from various resources

Methods

Results

Conclusion