Ricci curvature of random and empirical directed hypernetworks

Abstract Relationships in real systems are often not binary, but of a higher order, and therefore cannot be faithfully modelled by graphs, but rather need hypergraphs. In this work, we systematically develop formal tools for analyzing the geometry and the dynamics of hypergraphs. In particular, we show that Ricci curvature concepts, inspired by the corresponding notions of Forman and Ollivier for graphs, are powerful tools for probing the local geometry of hypergraphs. In fact, these two curvature concepts complement each other in the identification of specific connectivity motifs. In order to have a baseline model with which we can compare empirical data, we introduce a random model to generate directed hypergraphs and study properties such as degree of nodes and edge curvature, using numerical simulations. We can then see how our notions of curvature can be used to identify connectivity patterns in the metabolic network of E. coli that clearly deviate from those of our random model. Specifically, by applying hypergraph shuffling to this metabolic network we show that the changes in the wiring of a hypergraph can be detected by Forman Ricci and Ollivier Ricci curvatures.

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Parallel labeling experiments with [U-13C]glucose validate E. coli metabolic network model for 13C metabolic flux analysis

Metabolic Engineering ◽

10.1016/j.ymben.2012.06.003 ◽

2012 ◽

Vol 14 (5) ◽

pp. 533-541 ◽

Cited By ~ 71

Author(s):

Robert W. Leighty ◽

Maciek R. Antoniewicz

Keyword(s):

Metabolic Network ◽

Network Model ◽

Metabolic Flux Analysis ◽

Metabolic Flux ◽

Flux Analysis ◽

E Coli ◽

13C Metabolic Flux Analysis ◽

Metabolic Network Model

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Finding Minimum Reaction Cuts of Metabolic Networks Under a Boolean Model Using Integer Programming and Feedback Vertex Sets

Computer Engineering ◽

10.4018/978-1-61350-456-7.ch318 ◽

2012 ◽

pp. 774-791

Author(s):

Takeyuki Tamura ◽

Kazuhiro Takemoto ◽

Tatsuya Akutsu

Keyword(s):

Integer Programming ◽

Metabolic Network ◽

Drug Targets ◽

Metabolic Networks ◽

Boolean Model ◽

Pentose Phosphate ◽

Minimum Size ◽

E Coli ◽

Potential Applications ◽

Vertex Set

In this paper, the authors consider the problem of, given a metabolic network, a set of source compounds and a set of target compounds, finding a minimum size reaction cut, where a Boolean model is used as a model of metabolic networks. The problem has potential applications to measurement of structural robustness of metabolic networks and detection of drug targets. They develop an integer programming-based method for this optimization problem. In order to cope with cycles and reversible reactions, they further develop a novel integer programming (IP) formalization method using a feedback vertex set (FVS). When applied to an E. coli metabolic network consisting of Glycolysis/Glyconeogenesis, Citrate cycle and Pentose phosphate pathway obtained from KEGG database, the FVS-based method can find an optimal set of reactions to be inactivated much faster than a naive IP-based method and several times faster than a flux balance-based method. The authors also confirm that our proposed method works even for large networks and discuss the biological meaning of our results.

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In vivo estimation of energetic parameters of a metabolic network model for substrate limited growth of E. coli

New Biotechnology ◽

10.1016/j.nbt.2009.06.816 ◽

2009 ◽

Vol 25 ◽

pp. S337-S338

Author(s):

H. Taymaz-Nikerel ◽

P.J.T. Verheijen ◽

A.E. Borujeni ◽

J.J. Heijnen ◽

W.M. van Gulik

Keyword(s):

Metabolic Network ◽

Network Model ◽

Limited Growth ◽

E Coli ◽

Metabolic Network Model

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An Unexpected Route to an Essential Cofactor:Escherichia coliRelies on Threonine for Thiamine Biosynthesis

mBio ◽

10.1128/mbio.01840-15 ◽

2016 ◽

Vol 7 (1) ◽

Cited By ~ 14

Author(s):

Jannell V. Bazurto ◽

Kristen R. Farley ◽

Diana M. Downs

Keyword(s):

Escherichia Coli ◽

Metabolic Network ◽

Metabolic Networks ◽

Thiamine Pyrophosphate ◽

Metabolic Potential ◽

E Coli ◽

Regulatory Strategies ◽

Phosphoribosylpyrophosphate Amidotransferase ◽

Pyrimidine Moiety ◽

Thiamine Biosynthesis

ABSTRACTMetabolism consists of biochemical reactions that are combined to generate a robust metabolic network that can respond to perturbations and also adapt to changing environmental conditions.Escherichia coliandSalmonella entericaare closely related enterobacteria that share metabolic components, pathway structures, and regulatory strategies. The synthesis of thiamine inS. entericahas been used to define a node of the metabolic network by analyzing alternative inputs to thiamine synthesis from diverse metabolic pathways. To assess the conservation of metabolic networks in organisms with highly conserved components, metabolic contributions to thiamine synthesis inE. coliwere investigated. Unexpectedly, we found that, unlikeS. enterica,E. colidoes not use the phosphoribosylpyrophosphate (PRPP) amidotransferase (PurF) as the primary enzyme for synthesis of phosphoribosylamine (PRA).In fact, our data showed that up to 50% of the PRA used byE. colito make thiamine requires the activities of threonine dehydratase (IlvA) and anthranilate synthase component II (TrpD). Significantly, the IlvA- and TrpD-dependent pathway to PRA functions inS. entericaonly in the absence of a functionalreactiveintermediatedeaminase (RidA) enzyme, bringing into focus how these closely related bacteria have distinct metabolic networks.IMPORTANCEIn most bacteria, includingSalmonellastrains andEscherichia coli, synthesis of the pyrimidine moiety of the essential coenzyme, thiamine pyrophosphate (TPP), shares enzymes with the purine biosynthetic pathway. Phosphoribosylpyrophosphate amidotransferase, encoded by thepurFgene, generates phosphoribosylamine (PRA) and is considered the first enzyme in the biosynthesis of purines and the pyrimidine moiety of TPP. We show here that, unlikeSalmonella,E. colisynthesizes significant thiamine from PRA derived from threonine using enzymes from the isoleucine and tryptophan biosynthetic pathways. These data show that two closely related organisms can have distinct metabolic network structures despite having similar enzyme components, thus emphasizing caveats associated with predicting metabolic potential from genome content.

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Quantifying cumulative phenotypic and genomic evidence for procedural generation of metabolic network reconstructions

10.1101/2021.08.13.456222 ◽

2021 ◽

Author(s):

Thomas James Moutinho ◽

Benjamin C Neubert ◽

Matthew L Jenior ◽

Jason A. Papin

Keyword(s):

Metabolic Network ◽

Metabolic Networks ◽

Biological Data ◽

Supporting Evidence ◽

Phenotypic Data ◽

E Coli ◽

Simultaneous Integration ◽

Improve Model ◽

K 12 ◽

Genome Scale

Genome-scale metabolic network reconstructions (GENREs) are valuable tools for understanding microbial community metabolism. The process of automatically generating GENREs includes identifying metabolic reactions supported by sufficient genomic evidence to generate a draft metabolic network. The draft GENRE is then gapfilled with additional reactions in order to recapitulate specific growth phenotypes as indicated with associated experimental data. Previous methods have implemented absolute mapping thresholds for the reactions automatically included in draft GENREs; however, there is growing evidence that integrating annotation evidence in a continuous form can improve model accuracy. There is a need for flexibility in the structure of GENREs to better account for uncertainty in biological data, unknown regulatory mechanisms, and context specificity associated with data inputs. To address this issue, we present a novel method that provides a framework for quantifying combined genomic, biochemical, and phenotypic evidence for each biochemical reaction during automated GENRE construction. Our method, Constraint-based Analysis Yielding reaction Usage across metabolic Networks (CANYUNs), generates accurate GENREs with a quantitative metric for the cumulative evidence for each reaction included in the network. The structure of a CANYUN GENRE allows for the simultaneous integration of three data inputs while maintaining all supporting evidence for biochemical reactions that may be active in an organism. CANYUNs is designed to maximize the utility of experimental and annotation datasets and to ultimately assist in the curation of the reference datasets used for the automatic reconstruction of metabolic networks. We validated CANYUNs by generating an E. coli K-12 model and compared it to the manually curated reconstruction iML1515. Finally, we demonstrated the use of CANYUNs to build a model by generating an E. coli Nissle CANYUN GENRE using novel phenotypic data that we collected. This method may address key challenges for the procedural construction of metabolic networks by leveraging uncertainty and redundancy in biological data.

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Numerical Simulations and Empirical Data for the Evaluation of Daylight Factors in Existing Buildings in Sweden

Energies ◽

10.3390/en12112200 ◽

2019 ◽

Vol 12 (11) ◽

pp. 2200 ◽

Cited By ~ 1

Author(s):

Sara Eriksson ◽

Lovisa Waldenström ◽

Max Tillberg ◽

Magnus Österbring ◽

Angela Sasic Kalagasidis

Keyword(s):

Numerical Simulations ◽

Empirical Data ◽

Residential Buildings ◽

Energy Performance ◽

Field Investigation ◽

Urban Environments ◽

Indoor Environmental Quality ◽

Existing Buildings ◽

Building Shape ◽

Architectural Styles

Point Daylight Factor (DFP) has been used for daylighting design in Sweden for more than 40 years. Progressive densification of urban environments, in combination with stricter regulations on energy performance and indoor environmental quality of buildings, creates complex daylight design challenges that cannot be adequately solved with DFP. To support a development of the current and future daylight indicators in the Swedish context, the authors have developed a comprehensive methodology for the evaluation of daylight levels in existing buildings. The methodology comprises sample buildings of various use and their digital replicas in 3D, detailed numerical simulations and correlations of diverse DF metrics in existing buildings, a field investigation on residents’ satisfaction with available daylight levels in their homes, and a comparison between the numerical and experimental data. The study was deliberately limited to the evaluation of DF metrics for their intuitive understanding and easy evaluation in real design projects. The sample buildings represent typical architectural styles and building technologies between 1887 and 2013 in Gothenburg and include eight residential buildings, two office buildings, two schools, two student apartment buildings, and two hospitals. Although the simulated DFP is 1.4% on average, i.e., above the required 1%, large variations have been found between the studied 1200 rooms. The empirical data generally support the findings from the numerical simulations, but also bring unique insights in the residences’ preferences for rooms with good daylight. The most remarkable result is related to kitchens, typically the spaces with the lowest DF values, based on simulations, while the residents wish them to be the spaces with the most daylight. Finally, the work introduces a new DF metric, denoted DFW, which allows daylighting design in early stages when only limited data on the building shape and windows’ arrangement are available.

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Quantitative Microbial Risk Assessment for Escherichia coli O157:H7 in Fresh-Cut Lettuce

Journal of Food Protection ◽

10.4315/0362-028x.jfp-16-246 ◽

2017 ◽

Vol 80 (2) ◽

pp. 302-311 ◽

Cited By ~ 30

Author(s):

Hao Pang ◽

Elisabetta Lambertini ◽

Robert L. Buchanan ◽

Donald W. Schaffner ◽

Abani K. Pradhan

Keyword(s):

Escherichia Coli ◽

Foodborne Pathogens ◽

The United States ◽

Intervention Strategies ◽

Sensitivity Analyses ◽

Escherichia Coli O157 ◽

Baseline Model ◽

Microbial Risk ◽

E Coli ◽

Fresh Cut

ABSTRACT Leafy green vegetables, including lettuce, are recognized as potential vehicles for foodborne pathogens such as Escherichia coli O157:H7. Fresh-cut lettuce is potentially at high risk of causing foodborne illnesses, as it is generally consumed without cooking. Quantitative microbial risk assessments (QMRAs) are gaining more attention as an effective tool to assess and control potential risks associated with foodborne pathogens. This study developed a QMRA model for E. coli O157:H7 in fresh-cut lettuce and evaluated the effects of different potential intervention strategies on the reduction of public health risks. The fresh-cut lettuce production and supply chain was modeled from field production, with both irrigation water and soil as initial contamination sources, to consumption at home. The baseline model (with no interventions) predicted a mean probability of 1 illness per 10 million servings and a mean of 2,160 illness cases per year in the United States. All intervention strategies evaluated (chlorine, ultrasound and organic acid, irradiation, bacteriophage, and consumer washing) significantly reduced the estimated mean number of illness cases when compared with the baseline model prediction (from 11.4- to 17.9-fold reduction). Sensitivity analyses indicated that retail and home storage temperature were the most important factors affecting the predicted number of illness cases. The developed QMRA model provided a framework for estimating risk associated with consumption of E. coli O157:H7–contaminated fresh-cut lettuce and can guide the evaluation and development of intervention strategies aimed at reducing such risk.

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