Stoichiometric and Constraint-based Modeling

2006 ◽  
Keyword(s):  
Constraint Based Modeling

scholarly journals Rare genetic variants affecting urine metabolite levels link population variation to inborn errors of metabolism

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Yurong Cheng ◽  
◽  
Pascal Schlosser ◽  
Johannes Hertel ◽  
Peggy Sekula ◽  
...  
Keyword(s):  
Genetic Variants ◽  
Inborn Errors Of Metabolism ◽  
Cell Types ◽  
Population Variation ◽  
Whole Body ◽  
Sequencing Data ◽  
Inborn Errors ◽  
Data Links ◽  
Liver And Kidney ◽  
Constraint Based Modeling

AbstractMetabolite levels in urine may provide insights into genetic mechanisms shaping their related pathways. We therefore investigate the cumulative contribution of rare, exonic genetic variants on urine levels of 1487 metabolites and 53,714 metabolite ratios among 4864 GCKD study participants. Here we report the detection of 128 significant associations involving 30 unique genes, 16 of which are known to underlie inborn errors of metabolism. The 30 genes are strongly enriched for shared expression in liver and kidney (odds ratio = 65, p-FDR = 3e−7), with hepatocytes and proximal tubule cells as driving cell types. Use of UK Biobank whole-exome sequencing data links genes to diseases connected to the identified metabolites. In silico constraint-based modeling of gene knockouts in a virtual whole-body, organ-resolved metabolic human correctly predicts the observed direction of metabolite changes, highlighting the potential of linking population genetics to modeling. Our study implicates candidate variants and genes for inborn errors of metabolism.

scholarly journals The Microbiome Modeling Toolbox: from microbial interactions to personalized microbial communities

2018 ◽  
Vol 35 (13) ◽  
pp. 2332-2334 ◽  
Author(s):  
Federico Baldini ◽  
Almut Heinken ◽  
Laurent Heirendt ◽  
Stefania Magnusdottir ◽  
Ronan M T Fleming ◽  
...  
Keyword(s):  
Microbial Communities ◽  
Microbial Interactions ◽  
Microbial Genome ◽  
Metagenomic Data ◽  
Metabolic Interactions ◽  
Constraint Based Modeling ◽  
Genome Scale

Abstract Motivation The application of constraint-based modeling to functionally analyze metagenomic data has been limited so far, partially due to the absence of suitable toolboxes. Results To address this gap, we created a comprehensive toolbox to model (i) microbe–microbe and host–microbe metabolic interactions, and (ii) microbial communities using microbial genome-scale metabolic reconstructions and metagenomic data. The Microbiome Modeling Toolbox extends the functionality of the constraint-based reconstruction and analysis toolbox. Availability and implementation The Microbiome Modeling Toolbox and the tutorials at https://git.io/microbiomeModelingToolbox.

scholarly journals The Expanding Computational Toolbox for Engineering Microbial Phenotypes at the Genome Scale

2020 ◽  
Vol 8 (12) ◽  
pp. 2050
Author(s):  
Daniel Craig Zielinski ◽  
Arjun Patel ◽  
Bernhard O. Palsson
Keyword(s):  
Data Science ◽  
Strain Engineering ◽  
Chemical Production ◽  
Protein Structure Analysis ◽  
Biological Design ◽  
Strain Design ◽  
Microbial Strains ◽  
Active Research ◽  
Constraint Based Modeling ◽  
Predictive Design

Microbial strains are being engineered for an increasingly diverse array of applications, from chemical production to human health. While traditional engineering disciplines are driven by predictive design tools, these tools have been difficult to build for biological design due to the complexity of biological systems and many unknowns of their quantitative behavior. However, due to many recent advances, the gap between design in biology and other engineering fields is closing. In this work, we discuss promising areas of development of computational tools for engineering microbial strains. We define five frontiers of active research: (1) Constraint-based modeling and metabolic network reconstruction, (2) Kinetics and thermodynamic modeling, (3) Protein structure analysis, (4) Genome sequence analysis, and (5) Regulatory network analysis. Experimental and machine learning drivers have enabled these methods to improve by leaps and bounds in both scope and accuracy. Modern strain design projects will require these tools to be comprehensively applied to the entire cell and efficiently integrated within a single workflow. We expect that these frontiers, enabled by the ongoing revolution of big data science, will drive forward more advanced and powerful strain engineering strategies.

scholarly journals Data integration uncovers the metabolic bases of phenotypic variation in yeast

2020 ◽  
Author(s):  
Marianyela Petrizzelli ◽  
Dominique de Vienne ◽  
Thibault Nidelet ◽  
Camille Noûs ◽  
Christine Dillmann
Keyword(s):  
Phenotypic Variation ◽  
Enrichment Analysis ◽  
Production Traits ◽  
Phenotypic Complexity ◽  
Powerful Approach ◽  
Novel Method ◽  
Constraint Based Modeling ◽  
The Relationship ◽  
Yeast Genetic ◽  
Levels Of Integration

The relationship between different levels of integration is a key feature for understanding the genotype-phenotype map.Here, we describe a novel method of integrated data analysis that incorporates protein abundance data into constraint-based modeling to elucidate the biological mechanisms underlying phenotypic variation. Specifically, we studied yeast genetic diversity at three levels of phenotypic complexity in a population of yeast obtained by pairwise crosses of eleven strains belonging to two species, Saccha-romyces cerevisiae and S. uvarum. The data included protein abundances, integrated traits (life-history/fermentation) and computational estimates of metabolic fluxes.Results highlighted that the negative correlation between production traits such as population carrying capacity (K) and traits associated with growth and fermentation rates (Jmax) is explained by a differential usage of energy production pathways: a high K was associated with high TCA fluxes, while a high Jmax was associated with high glycolytic fluxes. Enrichment analysis of protein sets confirmed our results.This powerful approach allowed us to identify the molecular and metabolic bases of integrated trait variation, and therefore has a broad applicability domain.

Artificial Metabolic Networks: enabling neural computation with metabolic networks

2022 ◽  
Author(s):  
Leon Faure ◽  
Bastien Mollet ◽  
Wolfram Liebermeister ◽  
Jean-Loup Faulon
Keyword(s):  
Machine Learning ◽  
Recurrent Neural Networks ◽  
Metabolic Networks ◽  
Cross Validation ◽  
Regression Coefficient ◽  
Network Models ◽  
Biotechnological Applications ◽  
Neural Network Models ◽  
Surrogate Constraint ◽  
Constraint Based Modeling

Metabolic networks have largely been exploited as mechanistic tools to predict the behavior of microorganisms with a defined genotype in different environments. However, flux predictions by constraint-based modeling approaches are limited in quality unless labor-intensive experiments including the measurement of media intake fluxes, are performed. Using machine learning instead of an optimization of biomass flux - on which most existing constraint-based methods are based - provides ways to improve flux and growth rate predictions. In this paper, we show how Recurrent Neural Networks can surrogate constraint-based modeling and make metabolic networks suitable for backpropagation and consequently be used as an architecture for machine learning. We refer to our hybrid - mechanistic and neural network - models as Artificial Metabolic Networks (AMN). We showcase AMN and illustrate its performance with an experimental dataset of Escherichia coli growth rates in 73 different media compositions. We reach a regression coefficient of R2=0.78 on cross-validation sets. We expect AMNs to provide easier discovery of metabolic insights and prompt new biotechnological applications.

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