Localgini: A method for harnessing inequality in gene expression to improve the quality of context-specific models

Abstract

AbstractGenome-scale metabolic models (GEMs) are valuable tools for investigating normal and disease phenotypes of biological systems through the prediction of fluxes in biochemical reactions. However, in specific contexts such as different cell lines, tissues, or diseases, only a subset of reactions is active. To address this, several model extraction methods (MeMs) have been developed to filter the reactions in GEMs and extract context-specific models. These methods utilize gene expression data as a source of context-specific information. To construct context-specific models, MeMs require core reactions specific to the given context as input. Typically, core reactions are derived using a single threshold applied to gene expression data. Reactions associated with genes whose expression values exceed the threshold are considered as core reactions. However, it is important to note that enzyme activity is not solely determined by gene expression levels. This approach based on a single threshold may inadvertently exclude reactions that require enzymes in smaller quantities. In this study, we propose a novel thresholding algorithm called‘Localgini’, which leverages the Gini coefficient and transcriptomics data to derive gene-specific thresholds. Localgini is implemented as a pre-processing step to obtain core reactions for MeMs. To demonstrate the effectiveness of Localgini, we construct context-specific models for NCI-60 cancer cell lines and human tissues using different MeMs. We compare the performance of Localgini with existing thresholding methods, namely LocalT2 and StanDep. The results show that the models derived using Localgini recover a greater number of housekeeping functionalities compared to the other thresholding methods. Moreover, the Localgini-derived core reactions exhibit increased self-consistency and display enhanced consensus among models built using different MeMs. By incorporating transcriptomic support, Localgini includes low-expression reactions in the core reaction list, enhancing the comprehensiveness of the resulting models. Codes used in this study, compatible with COBRA toolbox are available athttps://github.com/NiravBhattLab/LocalginiAuthor summaryGenome-scale models are becoming a desirable tool to understand the metabolism of a biological system and hence find applications in the fields of systems and synthetic biology. These models are often integrated with transcriptomics data to improve prediction accuracy. Algorithms developed to integrate transcriptomics data with genome-scale models require core reactions to be derived from omics data using a threshold. In this work, we propose a thresholding method that uses an inequality-based metric to derive thresholds. We implied the proposed method and other existing methods to datasets of cancer cell lines and human tissue. We showed that our method improves the inclusion of reactions required for basic cellular maintenance. Furthermore, we validated the built models for the reduction in variance owing to the model-extraction algorithms. Overall, the proposed method improves the quality of metabolic models by inferring inequality in the distribution of gene expression levels across samples/contexts.