Optimization of Multi-Omic Genome-Scale Models: Methodologies, Hands-on Tutorial, and Perspectives

Vijayakumar, Supreeta; Conway, Max; Lió, Píetro; Angione, Claudio

doi:10.1007/978-1-4939-7528-0_18

Cited by 15 publications

(9 citation statements)

References 60 publications

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“…Optimization processes have therefore been proposed that take into account multiple objectives (e.g., protein or energy production, detoxification, proliferation) with the advantage of ensuring metabolic flexibility for possible reorganizations performed during adaptations to changes in the environmental conditions [97]. These methods include a probabilistic approach [98], lexicographic ordering [99], and the reduction to a single objective through the definition of weights for the objectives [100, 101].…”

Section: Metabolic Systems Biologymentioning

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: Metabolic Systems Biologymentioning

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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“…Confirmed or potential producers of new antimicrobial agents are further analysed by diverse "omics" approaches [342,[354][355][356][357]. The downstream evaluation of the collected data sets using bioinformatics tools enables for example the identification of the BGC for the product of interest and/or provides an overview on the overall biosynthetic potential of target strain (genome mining) [9,[358][359][360].…”

Section: Strategies and Tools For The Discovery Of Natural Productsmentioning

confidence: 99%

Actinomycete-Derived Polyketides as a Source of Antibiotics and Lead Structures for the Development of New Antimicrobial Drugs

Robertsen

Musiol-Kroll

2019

Antibiotics

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Actinomycetes are remarkable producers of compounds essential for human and veterinary medicine as well as for agriculture. The genomes of those microorganisms possess several sets of genes (biosynthetic gene cluster (BGC)) encoding pathways for the production of the valuable secondary metabolites. A significant proportion of the identified BGCs in actinomycetes encode pathways for the biosynthesis of polyketide compounds, nonribosomal peptides, or hybrid products resulting from the combination of both polyketide synthases (PKSs) and nonribosomal peptide synthetases (NRPSs). The potency of these molecules, in terms of bioactivity, was recognized in the 1940s, and started the “Golden Age” of antimicrobial drug discovery. Since then, several valuable polyketide drugs, such as erythromycin A, tylosin, monensin A, rifamycin, tetracyclines, amphotericin B, and many others were isolated from actinomycetes. This review covers the most relevant actinomycetes-derived polyketide drugs with antimicrobial activity, including anti-fungal agents. We provide an overview of the source of the compounds, structure of the molecules, the biosynthetic principle, bioactivity and mechanisms of action, and the current stage of development. This review emphasizes the importance of actinomycetes-derived antimicrobial polyketides and should serve as a “lexicon”, not only to scientists from the Natural Products field, but also to clinicians and others interested in this topic.

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“…Understanding how the transcriptomic alterations change the metabolic phenotype can provide an effective method for data interpretation and analysis (Vijayakumar et al, 2018; Stephens et al, 2015). To this end, we also used the P. putida metabolic model to investigate the transcriptomic effects on different pathways and reactions by including gene expression data into the proposed model.…”

Section: Methodsmentioning

confidence: 99%

In silico engineering ofPseudomonasmetabolism reveals new biomarkers for increased biosurfactant production

Occhipinti

Eyassu

Rahman

et al. 2018

PeerJ

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BackgroundRhamnolipids, biosurfactants with a wide range of biomedical applications, are amphiphilic molecules produced on the surfaces of or excreted extracellularly by bacteria including Pseudomonas aeruginosa. However, Pseudomonas putida is a non-pathogenic model organism with greater metabolic versatility and potential for industrial applications.MethodsWe investigate in silico the metabolic capabilities of P. putida for rhamnolipids biosynthesis using statistical, metabolic and synthetic engineering approaches after introducing key genes (RhlA and RhlB) from P. aeruginosa into a genome-scale model of P. putida. This pipeline combines machine learning methods with multi-omic modelling, and drives the engineered P. putida model toward an optimal production and export of rhamnolipids out of the membrane.ResultsWe identify a substantial increase in synthesis of rhamnolipids by the engineered model compared to the control model. We apply statistical and machine learning techniques on the metabolic reaction rates to identify distinct features on the structure of the variables and individual components driving the variation of growth and rhamnolipids production. We finally provide a computational framework for integrating multi-omics data and identifying latent pathways and genes for the production of rhamnolipids in P. putida.ConclusionsWe anticipate that our results will provide a versatile methodology for integrating multi-omics data for topological and functional analysis of P. putida toward maximization of biosurfactant production.

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Optimization of Multi-Omic Genome-Scale Models: Methodologies, Hands-on Tutorial, and Perspectives

Cited by 15 publications

References 60 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

Actinomycete-Derived Polyketides as a Source of Antibiotics and Lead Structures for the Development of New Antimicrobial Drugs

In silico engineering ofPseudomonasmetabolism reveals new biomarkers for increased biosurfactant production

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