scholarly journals StanDep: capturing transcriptomic variability improves context-specific metabolic models

2019 ◽  
Author(s):  
Chintan J. Joshi ◽  
Song-Min Schinn ◽  
Anne Richelle ◽  
Isaac Shamie ◽  
Eyleen J. O’Rourke ◽  
...  
Keyword(s):  
Expression Profiles ◽  
Heuristic Method ◽  
Housekeeping Genes ◽  
Extraction Methods ◽  
Expression Data ◽  
Matlab Toolbox ◽  
Metabolic Models ◽  
Using Data ◽  
Context Specific ◽  
Genome Scale

AbstractDiverse algorithms can integrate transcriptomics with genome-scale metabolic models (GEMs) to build context-specific metabolic models. These algorithms require identification of a list of high confidence (core) reactions from transcriptomics, but parameters related to identification of core reactions, such as thresholding of expression profiles, can significantly change model content. Importantly, current thresholding approaches are burdened with setting singular arbitrary thresholds for all genes; thus, resulting in removal of enzymes needed in small amounts and even many housekeeping genes. Here, we describe StanDep, a novel heuristic method for using transcriptomics to identify core reactions prior to building context-specific metabolic models. StanDep clusters gene expression data based on their expression pattern across different contexts and determines thresholds for each cluster using data-dependent statistics, specifically standard deviation and mean. To demonstrate the use of StanDep, we built hundreds of models for the NCI-60 cancer cell lines. These models successfully increased the inclusion of housekeeping reactions, which are often lost in models built using standard thresholding approaches. Further, StanDep also provided a transcriptomic explanation for inclusion of lowly expressed reactions that were otherwise only supported by model extraction methods. Our study also provides novel insights into how cells may deal with context-specific and ubiquitous functions. StanDep, as a MATLAB toolbox, is available at https://github.com/LewisLabUCSD/StanDep

scholarly journals metaGEM: reconstruction of genome scale metabolic models directly from metagenomes

2021 ◽  
Author(s):  
Francisco Zorrilla ◽  
Kiran R. Patil ◽  
Aleksej Zelezniak
Keyword(s):  
Microbial Communities ◽  
Metabolic Modeling ◽  
Large Degree ◽  
Metabolic Model ◽  
Community Level ◽  
Starting Point ◽  
Metabolic Models ◽  
Context Specific ◽  
Genome Scale ◽  
Reference Genomes

AbstractAdvances in genome-resolved metagenomic analysis of complex microbial communities have revealed a large degree of interspecies and intraspecies genetic diversity through the reconstruction of metagenome assembled genomes (MAGs). Yet, metabolic modeling efforts still tend to rely on reference genomes as the starting point for reconstruction and simulation of genome scale metabolic models (GEMs), neglecting the immense intra- and inter-species diversity present in microbial communities. Here we present metaGEM (https://github.com/franciscozorrilla/metaGEM), an end-to-end highly scalable pipeline enabling metabolic modeling of multi-species communities directly from metagenomic samples. The pipeline automates all steps from the extraction of context-specific prokaryotic GEMs from metagenome assembled genomes to community level flux balance simulations. To demonstrate the capabilities of the metaGEM pipeline, we analyzed 483 samples spanning lab culture, human gut, plant associated, soil, and ocean metagenomes, to reconstruct over 14 000 prokaryotic GEMs. We show that GEMs reconstructed from metagenomes have fully represented metabolism comparable to the GEMs reconstructed from reference genomes. We further demonstrate that metagenomic GEMs capture intraspecies metabolic diversity by identifying the differences between pathogenicity levels of type 2 diabetes at the level of gut bacterial metabolic exchanges. Overall, our pipeline enables simulation-ready metabolic model reconstruction directly from individual metagenomes, provides a resource of all reconstructed metabolic models, and showcases community-level modeling of microbiomes associated with disease conditions allowing generation of mechanistic hypotheses.

scholarly journals Increasing consensus of context-specific metabolic models by integrating data-inferred cell functions

2018 ◽  
Author(s):  
Anne Richelle ◽  
Austin W.T. Chiang ◽  
Chih-Chung Kuo ◽  
Nathan E. Lewis
Keyword(s):  
Cell Lines ◽  
Cell Types ◽  
Extraction Methods ◽  
Specific Model ◽  
Biological Knowledge ◽  
Predictive Capacity ◽  
Cell Functions ◽  
Metabolic Models ◽  
Context Specific ◽  
Diverse Context

AbstractGenome-scale metabolic models provide a valuable context for analyzing data from diverse high-throughput experimental techniques. Models can quantify the activities of diverse pathways and cellular functions. Since some metabolic reactions are only catalyzed in specific environments, several algorithms exist that build context-specific models. However, these methods make differing assumptions that influence the content and associated predictive capacity of resulting models, such that model content varies more due to methods used than cell types. Here we overcome this problem with a novel framework for inferring the metabolic functions of a cell before model construction. For this, we curated a list of metabolic tasks and developed a framework to infer the activity of these functionalities from transcriptomic data. We protected the data-inferred tasks during the implementation of diverse context-specific model extraction algorithms for 44 cancer cell lines. We show that the protection of data-inferred metabolic tasks decreases the variability of models across extraction methods. Furthermore, resulting models better capture the actual biological variability across cell lines. This study highlights the potential of using biological knowledge, inferred from omics data, to obtain a better consensus between existing extraction algorithms. It further provides guidelines for the development of the next-generation of data contextualization methods.

Integration of gene expression data into genome-scale metabolic models

2004 ◽  
Vol 6 (4) ◽  
pp. 285-293 ◽  
Author(s):  
Mats Åkesson ◽  
Jochen Förster ◽  
Jens Nielsen
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Expression Data ◽  
Metabolic Models ◽  
Genome Scale

scholarly journals On the inconsistent treatment of gene-protein-reaction rules in context-specific metabolic models

2019 ◽  
Author(s):  
Miguel Ponce-de-León ◽  
Iñigo Apaolaza ◽  
Alfonso Valencia ◽  
Francisco J. Planes
Keyword(s):  
Gene Expression ◽  
Metabolic Model ◽  
Specific Model ◽  
Omics Data ◽  
Model Reconstruction ◽  
Reconstruction Algorithms ◽  
Reconstruction Methods ◽  
Metabolic Models ◽  
Context Specific ◽  
Genome Scale

ABSTRACTWith the publication of high-quality genome-scale metabolic models for several organisms, the Systems Biology community has developed a number of algorithms for their analysis making use of ever growing –omics data. In particular, the reconstruction of the first genome-scale human metabolic model, Recon1, promoted the development of Context-Specific Model (CS-Model) reconstruction methods. This family of algorithms aims to identify the set of metabolic reactions that are active in a cell in a given condition using omics data, such as gene expression levels. Different CS-Model reconstruction algorithms have their own strengths and weaknesses depending on the problem under study and omics data available. However, after careful inspection, we found that all of these algorithms share common issues in the way GPR rules and gene expression data are treated. The first issue is related with how gapfilling reactions are managed after the reconstruction is conducted. The second issue concerns the molecular context, which is used to build the CS-model but neglected for posterior analyses. To evaluate the effect of these issues, we reconstructed ∼400 CS-Models of cancer cell lines and conducted gene essentiality analysis, using CRISPR–Cas9 essentiality data for validation purposes. Altogether, our results illustrate the importance of correcting the errors introduced during the GPR translation in many of the published metabolic reconstructions.

scholarly journals ΔFBA—Predicting metabolic flux alterations using genome-scale metabolic models and differential transcriptomic data

2021 ◽  
Vol 17 (11) ◽  
pp. e1009589
Author(s):  
Sudharshan Ravi ◽  
Rudiyanto Gunawan
Keyword(s):  
Gene Expression ◽  
Differential Gene Expression ◽  
Metabolic Flux ◽  
Balance Analysis ◽  
Differential Gene ◽  
Metabolic Models ◽  
Constraint Based Modeling ◽  
Context Specific ◽  
Modeling Strategy ◽  
Genome Scale

Genome-scale metabolic models (GEMs) provide a powerful framework for simulating the entire set of biochemical reactions in a cell using a constraint-based modeling strategy called flux balance analysis (FBA). FBA relies on an assumed metabolic objective for generating metabolic fluxes using GEMs. But, the most appropriate metabolic objective is not always obvious for a given condition and is likely context-specific, which often complicate the estimation of metabolic flux alterations between conditions. Here, we propose a new method, called ΔFBA (deltaFBA), that integrates differential gene expression data to evaluate directly metabolic flux differences between two conditions. Notably, ΔFBA does not require specifying the cellular objective. Rather, ΔFBA seeks to maximize the consistency and minimize inconsistency between the predicted flux differences and differential gene expression. We showcased the performance of ΔFBA through several case studies involving the prediction of metabolic alterations caused by genetic and environmental perturbations in Escherichia coli and caused by Type-2 diabetes in human muscle. Importantly, in comparison to existing methods, ΔFBA gives a more accurate prediction of flux differences.

scholarly journals ΔFBA: Predicting metabolic flux alterations using genome-scale metabolic models and differential transcriptomic data

2021 ◽  
Author(s):  
Sudharshan Ravi ◽  
Rudiyanto Gunawan
Keyword(s):  
Metabolic Flux ◽  
Flux Distribution ◽  
Experimental Conditions ◽  
Modeling Tools ◽  
Metabolic Flux Distribution ◽  
Balance Analysis ◽  
Metabolic Models ◽  
Constraint Based Modeling ◽  
Context Specific ◽  
Genome Scale

AbstractGenome-scale metabolic models (GEMs) provide a powerful framework for simulating the entire set of biochemical reactions occurring in a cell. Constraint-based modeling tools like flux balance analysis (FBA) developed for the purposes of predicting metabolic flux distribution using GEMs face considerable difficulties in estimating metabolic flux alterations between experimental conditions. Particularly, the most appropriate metabolic objective for FBA is not always obvious, likely context-specific, and not necessarily the same between conditions. Here, we propose a new method, called ΔFBA (deltaFBA), that employs constraint-based modeling, in combination with differential gene expression data, to evaluate changes in the intracellular flux distribution between two conditions. Notably, ΔFBA does not require specifying the cellular objective to produce the flux change predictions. We showcased the performance of ΔFBA through several case studies involving the prediction of metabolic alterations caused by genetic and environmental perturbations in Escherichia coli and caused by Type-2 diabetes in human muscle.

Reconstruction of context-specific genome-scale metabolic models using multiomics data to study metabolic rewiring

2019 ◽  
Vol 15 ◽  
pp. 1-11 ◽  
Author(s):  
Jae Sung Cho ◽  
Changdai Gu ◽  
Tae Hee Han ◽  
Jae Yong Ryu ◽  
Sang Yup Lee
Keyword(s):  
Metabolic Models ◽  
Context Specific ◽  
Genome Scale

scholarly journals Inference of Genome-Scale Gene Regulatory Networks: Are There Differences in Biological and Clinical Validations?

2018 ◽  
Vol 1 (1) ◽  
pp. 138-148
Author(s):  
Frank Emmert-Streib ◽  
Matthias Dehmer
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Human Gene ◽  
Expression Data ◽  
Research Questions ◽  
Gene Regulatory ◽  
Experimental Validations ◽  
Context Specific ◽  
Genome Scale ◽  
Network Project

Causal networks, e.g., gene regulatory networks (GRNs) inferred from gene expression data, contain a wealth of information but are defying simple, straightforward and low-budget experimental validations. In this paper, we elaborate on this problem and discuss distinctions between biological and clinical validations. As a result, validation differences for GRNs reflect known differences between basic biological and clinical research questions making the validations context specific. Hence, the meaning of biologically and clinically meaningful GRNs can be very different. For a concerted approach to a problem of this size, we suggest the establishment of the HUMAN GENE REGULATORY NETWORK PROJECT which provides the information required for biological and clinical validations alike.

On the limits of active module identification

2021 ◽  
Author(s):  
Olga Lazareva ◽  
Jan Baumbach ◽  
Markus List ◽  
David B Blumenthal
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Small Diameter ◽  
Extensive Study ◽  
Biological Knowledge ◽  
Expression Data ◽  
Module Identification ◽  
Ppi Networks ◽  
Novel Algorithms ◽  
Context Specific

Abstract In network and systems medicine, active module identification methods (AMIMs) are widely used for discovering candidate molecular disease mechanisms. To this end, AMIMs combine network analysis algorithms with molecular profiling data, most commonly, by projecting gene expression data onto generic protein–protein interaction (PPI) networks. Although active module identification has led to various novel insights into complex diseases, there is increasing awareness in the field that the combination of gene expression data and PPI network is problematic because up-to-date PPI networks have a very small diameter and are subject to both technical and literature bias. In this paper, we report the results of an extensive study where we analyzed for the first time whether widely used AMIMs really benefit from using PPI networks. Our results clearly show that, except for the recently proposed AMIM DOMINO, the tested AMIMs do not produce biologically more meaningful candidate disease modules on widely used PPI networks than on random networks with the same node degrees. AMIMs hence mainly learn from the node degrees and mostly fail to exploit the biological knowledge encoded in the edges of the PPI networks. This has far-reaching consequences for the field of active module identification. In particular, we suggest that novel algorithms are needed which overcome the degree bias of most existing AMIMs and/or work with customized, context-specific networks instead of generic PPI networks.

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