Abstract
Omics data can be integrated into a reference model using various model extraction methods (MEMs) to yield context-specific genome-scale metabolic models (GEMs). How to chose the appropriate MEM, thresholding rule and threshold remains a challenge. We integrated mouse transcriptomic data from a Cyp51 knockout mice diet experiment (GSE58271) using five MEMs (GIMME, iMAT, FASTCORE, INIT an tINIT) in a combination with a recently published mouse GEM iMM1865. Except for INIT and tINIT, the size of extracted models varied with the MEM used (t-test: p-value <0.001). The Jaccard index of iMAT models ranged from 0.27 to 1.0. Out of the three factors under study in the experiment (diet, gender and genotype), gender explained most of the variability (>90%) in PC1 for FASTCORE. In iMAT, each of the three factors explained less than 40% of the variability within PC1, PC2 and PC3. Among all the MEMs, FASTCORE captured the most of the true variability in the data by clustering samples by gender. Our results show that for the efficient use of MEMs in the context of omics data integration and analysis, one should apply various MEMs, thresholding rules, and thresholding values to select the MEM and its configuration that best captures the true variability in the data. This selection can be guided by the methodology as proposed and used in this paper. Moreover, we describe certain approaches that can be used to analyse the results obtained with the selected MEM and to put these results in a biological context.
Highlights
The advent of high-throughput technologies generate large volumes of omics data making it possible to study organisms at the cellular level
We aim to address these problems by suggesting a methodology for performing analyses of omics data using genome-scale metabolic models (GEMs) in combination with different model extraction methods (MEMs)
The mouse gene expression dataset was downloaded from the GEO database and processed to obtain normalised gene expression values. This dataset was generated from a study in which the mice were divided into three groups and fed on three diets i.e. low fat without cholesterol (LFnC), high fat without cholesterol (HFnC) and high fat with cholesterol (HFC)
Summary
The advent of high-throughput technologies generate large volumes of omics data making it possible to study organisms at the cellular level. The phenotypic characteristics of an organism are determined by intricately connected reactions and pathways that generate energy and other forms of biological products necessary to sustain life and organise organismal development. These pathways are connected to form and function as biological systems. The degree of complexity of biological systems differs greatly among organisms, for example, humans are far more complex than Drosophila melanogaster despite the latter’s importance in modelling human diseases [9,10,11] It is extremely difficult, or even impossible for higher level organisms, to study the entirety of their pathways either in vitro or in vivo. Mathematical tools such as genome-scale models can be used to gain insight into how these biological systems function [12]
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