Iterative Multi Level Calibration of Metabolic Networks

Conway, Max; Angione, Claudio; Lió, Píetro

doi:10.2174/1574893611666151203222505

Cited by 4 publications

(4 citation statements)

References 28 publications

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“…It requires information about biochemical reactions and stoichiometric coefficients but does not involve kinetic parameters. This makes it well suited to metabolic engineering studies that identify and characterize optimal perturbations such as different substrates or genetic interventions (e.g., knockouts) leading to obligatory coupling between the growth rate and the overproduction of the desired metabolite [38–42]. In general, FBA is a powerful tool for predictions of cell behavior under different metabolic conditions.…”

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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“…STAble was successfully integrated with a new analysis workflow based on metabolic model network recently described in [ 12 ]. The combination of STAble with this workflow can be used as an “expert system” to obtain more punctual information about the metabolic pathways activated in a bacterial community.…”

Section: Resultsmentioning

confidence: 99%

“…The contingency tables with read count for each orthologous gene were processed with metabolic models to interpret gene expression. The method adopted is described in [ 12 ]. Briefly, we performed a blind Monte-Carlo simulation over feasible flux configurations.…”

Section: Methodsmentioning

confidence: 99%

STAble: a novel approach to de novo assembly of RNA-seq data and its application in a metabolic model network based metatranscriptomic workflow

et al. 2018

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BackgroundDe novo assembly of RNA-seq data allows the study of transcriptome in absence of a reference genome either if data is obtained from a single organism or from a mixed sample as in metatranscriptomics studies. Given the high number of sequences obtained from NGS approaches, a critical step in any analysis workflow is the assembly of reads to reconstruct transcripts thus reducing the complexity of the analysis. Despite many available tools show a good sensitivity, there is a high percentage of false positives due to the high number of assemblies considered and it is likely that the high frequency of false positive is underestimated by currently used benchmarks. The reconstruction of not existing transcripts may false the biological interpretation of results as – for example – may overestimate the identification of “novel” transcripts. Moreover, benchmarks performed are usually based on RNA-seq data from annotated genomes and assembled transcripts are compared to annotations and genomes to identify putative good and wrong reconstructions, but these tests alone may lead to accept a particular type of false positive as true, as better described below.ResultsHere we present a novel methodology of de novo assembly, implemented in a software named STAble (Short-reads Transcriptome Assembler). The novel concept of this assembler is that the whole reads are used to determine possible alignments instead of using smaller k-mers, with the aim of reducing the number of chimeras produced. Furthermore, we applied a new set of benchmarks based on simulated data to better define the performance of assembly method and carefully identifying true reconstructions.STAble was also used to build a prototype workflow to analyse metatranscriptomics data in connection to a steady state metabolic modelling algorithm. This algorithm was used to produce high quality metabolic interpretations of small gene expression sets obtained from already published RNA-seq data that we assembled with STAble.ConclusionsThe presented results, albeit preliminary, clearly suggest that with this approach is possible to identify informative reactions not directly revealed by raw transcriptomic data.

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“…A pathway-based perspective has been often taken in genome-scale models with the aim of investigating sensitivity analysis [37,38,39], and coupled with Bayesian techniques to detect pathway crosstalks and temporal activation profiles [40]. To assess the variation in the average flux of each pathway with respect to the unconstrained breast cancer model, we here compute a normalized average pathway flux di = w(i) − w…”

Section: Pathway-based Flux Analysismentioning

confidence: 99%

Integrating splice-isoform expression into genome-scale models characterizes breast cancer metabolism

Angione

2017

Bioinformatics

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Iterative Multi Level Calibration of Metabolic Networks

Cited by 4 publications

References 28 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

STAble: a novel approach to de novo assembly of RNA-seq data and its application in a metabolic model network based metatranscriptomic workflow

Integrating splice-isoform expression into genome-scale models characterizes breast cancer metabolism

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