Scalable metabolic pathway analysis

A Genome ◽

Scope Of Application ◽

The scope of application of genome-scale constraint-based models (CBMs) of metabolic networks rapidly expands toward multicellular systems. However, comprehensive analysis of CBMs through metabolic pathway analysis remains a major computational challenge because pathway numbers grow combinatorially with model sizes. Here, we define the minimal pathways (MPs) of a metabolic (sub)network as a subset of its elementary flux vectors. We enumerate or sample them efficiently using iterative minimization and a simple graph representation of MPs. These methods outperform the state of the art and they allow scalable pathway analysis for microbial and mammalian CBMs. Sampling random MPs from Escherichia coli’s central carbon metabolism in the context of a genome-scale CBM improves predictions of gene importance, and enumerating all minimal exchanges in a host-microbe model of the human gut predicts exchanges of metabolites associated with host-microbiota homeostasis and human health. MPs thereby open up new possibilities for the detailed analysis of large-scale metabolic networks.

Metabolic Pathway Analysis of Nitrogen and Phosphorus Uptake by the Consortium between C. vulgaris and P. aeruginosa

International Journal of Molecular Sciences ◽

10.3390/ijms20081978 ◽

2019 ◽

Vol 20 (8) ◽

pp. 1978 ◽

Cited By ~ 3

Author(s):

A. Suggey Guerra-Renteria ◽

M. Alberto García-Ramírez ◽

César Gómez-Hermosillo ◽

Abril Gómez-Guzmán ◽

Yolanda González-García ◽

...

Keyword(s):

Pathway Analysis ◽

Metabolic Pathway ◽

Phosphorus Uptake ◽

Reaction Network ◽

Biochemical Reaction ◽

Urban Wastewater ◽

Biochemical Reaction Network ◽

A Genome ◽

Anthropogenic activities have increased the amount of urban wastewater discharged into natural aquatic reservoirs containing a high amount of nutrients such as phosphorus (Pi and PO 4 − 3 ), nitrogen (NH 3 and NO 3 − ) and organic contaminants. Most of the urban wastewater in Mexico do not receive any treatment to remove nutrients. Several studies have reported that an alternative to reduce those contaminants is using consortiums of microalgae and endogenous bacteria. In this research, a genome-scale biochemical reaction network is reconstructed for the co-culture between the microalga Chlorella vulgaris and the bacterium Pseudomonas aeruginosa. Metabolic Pathway Analysis (MPA), is applied to understand the metabolic capabilities of the co-culture and to elucidate the best conditions in removing nutrients. Theoretical yields for phosphorus removal under photoheterotrophic conditions are calculated, determining their values as 0.042 mmol of PO 4 − 3 per g DW of C. vulgaris, 19.43 mmol of phosphorus (Pi) per g DW of C. vulgaris and 4.90 mmol of phosphorus (Pi) per g DW of P. aeruginosa. Similarly, according to the genome-scale biochemical reaction network the theoretical yields for nitrogen removal are 10.3 mmol of NH 3 per g DW of P. aeruginosa and 7.19 mmol of NO 3 − per g DW of C. vulgaris. Thus, this research proves the metabolic capacity of these microorganisms in removing nutrients and their theoretical yields are calculated.

Metabolic Pathway Analysis for Nutrient Removal of the Consortium between C. vulgaris and P. aeruginosa

10.20944/preprints201903.0002.v1 ◽

2019 ◽

Author(s):

A. Suggey Guerra-Rentería ◽

Mario García-Ramírez ◽

César Gómez-Hermosillo ◽

Abril Goméz-Guzmán ◽

Yolanda González-García ◽

...

Keyword(s):

Pathway Analysis ◽

Metabolic Pathway ◽

Anthropogenic Activities ◽

Reaction Network ◽

Inorganic Phosphorus ◽

Biochemical Reaction ◽

Biochemical Reaction Network ◽

A Genome ◽

Anthropogenic activities have increased the amount of urban wastewater discharged into natural aquatic reservoirs confining in them a high amount of nutrients and organics contaminants. Several studies have reported that an alternative to reduce those contaminants is using consortiums of microalgae and endogenous bacteria. In this research, a genome-scale biochemical reaction network is reconstructed for the co-culture between the microalga Chlorella vulgaris and the bacterium Pesudomonas aeruginosa. Metabolic Pathway Analysis (MPA), is applied to understand the metabolic capabilities of the co-culture and to elucidate the best conditions in removing nutrients such as Phosphorus (inorganic phosphorous and phosphate) and Nitrogen (nitrates and ammonia). Theoretical yields for Phosphorus removal under photoheterotrophic conditions are calculated, determining their values as 0.042 mmol of PO4/ g DW of C. vulgaris, 19.53 mmol of inorganic Phosphorus /g DW of C. vulgaris and 4.90 mmol of inorganic Phosphorus/ g DW of P. aeruginosa. Similarly, according to the genome-scale biochemical reaction network the theoretical yields for Nitrogen removal are 10.3 mmol of NH3/g DW of P. aeruginosa and 7.19 mmol of NO3 /g DW of C. vulgaris. Thus, this research proves the metabolic capacity of these microorganisms in removing nutrients and their theoretical yields are calculated.

Towards genome-scale metabolic pathway analysis: metabolome integration allows efficient enumeration of elementary flux modes in metabolic networks

New Biotechnology ◽

10.1016/j.nbt.2014.05.1678 ◽

2014 ◽

Vol 31 ◽

pp. S29

Author(s):

Jürgen Zanghellini ◽

Matthias P. Gerstl ◽

David Ruckerbauer ◽

Christian Jungreuthmayer

Keyword(s):

Pathway Analysis ◽

Metabolic Pathway ◽

Metabolic Networks ◽

Elementary Flux Modes ◽

Toward Genome-Scale Metabolic Pathway Analysis

Industrial Biotechnology ◽

10.1002/9783527807796.ch3 ◽

2016 ◽

pp. 111-123 ◽

Cited By ~ 2

Author(s):

Jürgen Zanghellini ◽

Matthias P. Gerstl ◽

Michael Hanscho ◽

Govind Nair ◽

Georg Regensburger ◽

...

Keyword(s):

Pathway Analysis ◽

Metabolic Pathway ◽

Metabolic pathway analysis of yeast strengthens the bridge between transcriptomics and metabolic networks

Biotechnology and Bioengineering ◽

10.1002/bit.20020 ◽

2004 ◽

Vol 86 (3) ◽

pp. 251-260 ◽

Cited By ~ 50

Author(s):

Tunahan Çakir ◽

Betül Kirdar ◽

Kutlu Ö. Ülgen

Keyword(s):

Pathway Analysis ◽

Metabolic Pathway ◽

Metabolic Networks ◽

Metabolic Pathway Analysis

Cellular determinants of metabolite concentration ranges

10.1101/439232 ◽

2018 ◽

Author(s):

Jeanne M. O. Eloundou-Mbebi ◽

Anika Küken ◽

Georg Basler ◽

Zoran Nikoloski

Keyword(s):

Escherichia Coli ◽

Large Scale ◽

Metabolic Networks ◽

Reaction Rates ◽

Mass Action ◽

Cellular Mechanisms ◽

Real World Data ◽

A Genome ◽

Metabolic Models ◽

AbstractCellular functions are shaped by reaction networks whose dynamics are determined by the concentrations of underlying components. However, cellular mechanisms ensuring that a component’s concentration resides in a given range remain elusive. We present network properties which suffice to identify components whose concentration ranges can be efficiently computed in mass-action metabolic networks. We show that the derived ranges are in excellent agreement with simulations from a detailed kinetic metabolic model of Escherichia coli. We demonstrate that the approach can be used with genome-scale metabolic models to arrive at predictions concordant with measurements from Escherichia coli under different growth scenarios. By application to 14 genome-scale metabolic models from diverse species, our approach specifies the cellular determinants of concentration ranges that can be effectively employed to make predictions for a variety of biotechnological and medical applications.Author SummaryWe present a computational approach for inferring concentration ranges from genome-scale metabolic models. The approach specifies a determinant and molecular mechanism underling facile control of concentration ranges for components in large-scale cellular networks. Most importantly, the predictions about concentration ranges do not require knowledge of kinetic parameters (which are difficult to specify at a genome scale), provided measurements of concentrations in a reference state. The approach assumes that reaction rates follow the mass action law used in the derivations of other types of kinetics. We apply the approach with large-scale kinetic and stoichiometric metabolic models of organisms from different kingdoms of life to show that we can identify a proportion of metabolites to which our approach is applicable. By challenging the predictions of concentration ranges in the genome-scale metabolic network of E. coli with real-world data sets, we further demonstrate the prediction power and limitations of the approach.

Dynamic Solution Space Division-Based Methods for Calculating Reaction Deletion Strategies for Constraint-Based Metabolic Networks for Substance Production: DynCubeProd

Frontiers in Bioinformatics ◽

10.3389/fbinf.2021.716112 ◽

2021 ◽

Vol 1 ◽

Author(s):

Yier Ma ◽

Takeyuki Tamura

Keyword(s):

Large Scale ◽

Metabolic Networks ◽

Solution Space ◽

Design Strategies ◽

Metabolite Production ◽

Dynamic Solution ◽

Space Division ◽

A Genome ◽

Balance Analysis ◽

Flux balance analysis (FBA) is a crucial method to analyze large-scale constraint-based metabolic networks and computing design strategies for strain production in metabolic engineering. However, as it is often non-straightforward to obtain such design strategies to produce valuable metabolites, many tools have been proposed based on FBA. Among them, GridProd, which divides the solution space into small squares by focusing on the cell growth rate and the target metabolite production rate to efficiently find the reaction deletion strategies, was extended to CubeProd, which divides the solution space into small cubes. However, as GridProd and CubeProd naively divide the solution space into equal sizes, even places where solutions are unlikely to exist are examined. To address this issue, we introduce dynamic solution space division methods based on CubeProd for faster computing by avoiding searching in places where the solutions do not exist. We applied the proposed method DynCubeProd to iJO1366, which is a genome-scale constraint-based model of Escherichia coli. Compared with CubeProd, DynCubeProd significantly accelerated the calculation of the reaction deletion strategy for each target metabolite production. In addition, under the anaerobic condition of iJO1366, DynCubeProd could obtain the reaction deletion strategies for almost 40% of the target metabolites that the elementary flux vector-based method, which is one of the most effective methods in existence, could not. The developed software is available on https://github.com/Ma-Yier/DynCubeProd.

Arabidopsis Genes Essential for Seedling Viability: Isolation of Insertional Mutants and Molecular Cloning

Genetics ◽

10.1093/genetics/159.4.1765 ◽

2001 ◽

Vol 159 (4) ◽

pp. 1765-1778

Author(s):

Gregory J Budziszewski ◽

Sharon Potter Lewis ◽

Lyn Wegrich Glover ◽

Jennifer Reineke ◽

Gary Jones ◽

...

Keyword(s):

Large Scale ◽

Protein Translocation ◽

Gene Families ◽

Mutant Phenotype ◽

Lethal Mutant ◽

A Genome ◽

Genes Encoding ◽

High Level ◽

Mutant Lines ◽

Abstract We have undertaken a large-scale genetic screen to identify genes with a seedling-lethal mutant phenotype. From screening ~38,000 insertional mutant lines, we identified >500 seedling-lethal mutants, completed cosegregation analysis of the insertion and the lethal phenotype for >200 mutants, molecularly characterized 54 mutants, and provided a detailed description for 22 of them. Most of the seedling-lethal mutants seem to affect chloroplast function because they display altered pigmentation and affect genes encoding proteins predicted to have chloroplast localization. Although a high level of functional redundancy in Arabidopsis might be expected because 65% of genes are members of gene families, we found that 41% of the essential genes found in this study are members of Arabidopsis gene families. In addition, we isolated several interesting classes of mutants and genes. We found three mutants in the recently discovered nonmevalonate isoprenoid biosynthetic pathway and mutants disrupting genes similar to Tic40 and tatC, which are likely to be involved in chloroplast protein translocation. Finally, we directly compared T-DNA and Ac/Ds transposon mutagenesis methods in Arabidopsis on a genome scale. In each population, we found only about one-third of the insertion mutations cosegregated with a mutant phenotype.