Discovering missing reactions of metabolic networks by using gene co-expression data

Hosseini, Zhaleh; Marashi, Sayed-Amir

doi:10.1038/srep41774

Cited by 15 publications

(13 citation statements)

References 40 publications

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“…Computational tools combining both approaches would go towards genome-scale detection of errors and missing enzymatic reactions, remarkably improving the predictive ability of metabolic models. For instance, this could be achieved by minimizing the error between predictions of flux coupling and experimental coexpression data integrated with GWAS [179, 180].…”

Section: Discussion and Perspectivementioning

confidence: 99%

Human Systems Biology and Metabolic Modelling: A Review—From Disease Metabolism to Precision Medicine

Angione

2019

BioMed Research International

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In cell and molecular biology, metabolism is the only system that can be fully simulated at genome scale. Metabolic systems biology offers powerful abstraction tools to simulate all known metabolic reactions in a cell, therefore providing a snapshot that is close to its observable phenotype. In this review, we cover the 15 years of human metabolic modelling. We show that, although the past five years have not experienced large improvements in the size of the gene and metabolite sets in human metabolic models, their accuracy is rapidly increasing. We also describe how condition-, tissue-, and patient-specific metabolic models shed light on cell-specific changes occurring in the metabolic network, therefore predicting biomarkers of disease metabolism. We finally discuss current challenges and future promising directions for this research field, including machine/deep learning and precision medicine. In the omics era, profiling patients and biological processes from a multiomic point of view is becoming more common and less expensive. Starting from multiomic data collected from patients and N-of-1 trials where individual patients constitute different case studies, methods for model-building and data integration are being used to generate patient-specific models. Coupled with state-of-the-art machine learning methods, this will allow characterizing each patient’s disease phenotype and delivering precision medicine solutions, therefore leading to preventative medicine, reduced treatment, andin silicoclinical trials.

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Section: Discussion and Perspectivementioning

confidence: 99%

Human Systems Biology and Metabolic Modelling: A Review—From Disease Metabolism to Precision Medicine

Angione

2019

BioMed Research International

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“…All methods that use various kinds of omics data to fill the gaps of a metabolic model are in the third group, e.g. , Sequence-based (45) and Likelihood-based (46) methods, Mirage (47), and GAUGE (26).…”

Section: Resultsmentioning

confidence: 99%

“…In the present study, four independent approaches were used for the gap-filling of i CHO1766. The first two approaches were based on automatic gap-filling tools, namely, GapFind/GapFill (25) and GAUGE (26). The GapFind algorithm uses mixed integer linear programming (MILP) to find all metabolites that cannot be produced in steady-state.…”

Section: Methodsmentioning

confidence: 99%

Systematically gap-filling the genome-scale metabolic model of CHO cells

Fouladiha

Marashi

et al. 2020

Preprint

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ObjectiveChinese hamster ovary (CHO) cells are the leading cell factories for producing recombinant proteins in the biopharmaceutical industry. In comparison to other mammalian cell types, in vitro handling of CHO cells is relatively easy. For example, CHO cells can grow in suspension and reach high cellular densities in bioreactors. Therefore, studying the metabolism of CHO cells to improve the bio-production of these cells is an important subject of research. In this regard, constraint-based metabolic models are useful platforms to perform computational analysis of cell metabolism.ResultsHere, we expanded the existing model of Chinese hamster metabolism (iCHO1766) with the help of four gap-filling approaches, leading to the addition of 773 new reactions and 335 new genes. We incorporated these into an updated genome-scale metabolic network model of CHO cells, named iCHO2101. This updated model substantially increased the number of reactions capable of carrying flux.ConclusionsThe addition of these new data provides an important step towards more complete metabolic models of CHO cells.

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“…gapFind / gapFill [7], fastGapFill [8], and Gauge [9] are based on exhaustive searches. Thus, these methodologies identify gaps all over the metabolic network, regardless of a given objective.…”

Section: Introductionmentioning

confidence: 99%

“…To the best of our knowledge, all gap-gill tools require a dataset of metabolic reactions, usually retrieved from a biochemical database (e.g. KEGG [13], BiGG [14] or MetaCyc [15]), to fulfil metabolic gaps [6–9, 11, 12]. Besides a database of metabolic reactions, both Gauge [9] and Mirage [12] require gene expression data.…”

Section: Introductionmentioning

confidence: 99%

BioISO: an objective-oriented application for assisting the curation of genome-scale metabolic models

Cruz

Capela

Ferreira

et al. 2021

Preprint

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As the reconstruction of Genome-Scale Metabolic Models becomes standard practice in systems biology, the number of organisms having at least one metabolic model at the genome-scale is peaking at an unprecedented scale. The automation of several laborious tasks, such as gap-finding and gap-filling, allowed to develop GSMMs for poorly described organisms. However, such models' quality can be compromised by the automation of several steps, which may lead to erroneous phenotype simulations. The Biological networks constraint-based In Silico Optimization (BioISO) is a computational tool aimed at accelerating the reconstruction of Genome-Scale Metabolic Models. This tool facilitates the manual curation steps by reducing the large search spaces often met when debugging in silico biological models. BioISO uses a recursive relation-like algorithm and Flux Balance Analysis to evaluate and guide debugging of in silico phenotype simulations. The potential of BioISO to guide the debugging of model reconstructions was showcased using GSMMs available in literature and compared with the results of two other state-of-the-art gap-filling tools (Meneco and fastGapFill). Furthermore, BioISO was used as Meneco's gap-finding algorithm to reduce the number of proposed solutions (reaction sets) for filling the gaps. BioISO was implemented as a webserver available at https://bioiso.bio.di.uminho.pt; and integrated into merlin as a plugin. BioISO's implementation as a Python package can also be retrieved from https://github.com/BioSystemsUM/BioISO.

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Discovering missing reactions of metabolic networks by using gene co-expression data

Cited by 15 publications

References 40 publications

Human Systems Biology and Metabolic Modelling: A Review—From Disease Metabolism to Precision Medicine

Human Systems Biology and Metabolic Modelling: A Review—From Disease Metabolism to Precision Medicine

Systematically gap-filling the genome-scale metabolic model of CHO cells

BioISO: an objective-oriented application for assisting the curation of genome-scale metabolic models

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