Taxonomic weighting improves the accuracy of a gap-filling algorithm for metabolic models

Abstract Motivation The increasing availability of annotated genome sequences enables construction of genome-scale metabolic networks, which are useful tools for studying organisms of interest. However, due to incomplete genome annotations, draft metabolic models contain gaps that must be filled in a time-consuming process before they are usable. Optimization-based algorithms that fill these gaps have been developed, however, gap-filling algorithms show significant error rates and often introduce incorrect reactions. Results Here, we present a new gap-filling method that computes the costs of candidate gap-filling reactions from a universal reaction database (MetaCyc) based on taxonomic information. When gap-filling a metabolic model for an organism M (such as Escherichia coli), the cost for reaction R is based on the frequency with which R occurs in other organisms within the phylum of M (in this case, Proteobacteria). The assumption behind this method is that different taxonomic groups are biased toward using different metabolic reactions. Evaluation of the new gap-filler on randomly degraded variants of the EcoCyc metabolic model for E.coli showed an increase in the average F1-score to 99.0 (when using the variable weights by frequency method at the phylum level), compared to 91.0 using the previous MetaFlux gap-filler and 80.3 using a basic gap-filler. Evaluation on two other microbial metabolic models showed similar improvements. Availability and implementation The Pathway Tools software (including MetaFlux) is free for academic use and is available at http://pathwaytools.com. Additional code for reproducing the results presented here is available at www.ai.sri.com/pkarp/pubs/taxgap/supplementary.zip. Supplementary information Supplementary data are available at Bioinformatics online.

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Tn-Core: context-specific reconstruction of core metabolic models using Tn-seq data

10.1101/221325 ◽

2017 ◽

Cited By ~ 2

Author(s):

George C diCenzo ◽

Alessio Mengoni ◽

Marco Fondi

Keyword(s):

Sinorhizobium Meliloti ◽

Metabolic Networks ◽

De Novo ◽

Supplementary Information ◽

Data Sets ◽

Microbial Cell Factories ◽

Cell Factories ◽

Functional Models ◽

Metabolic Models ◽

Context Specific

ABSTRACTMotivationTn-seq (transposon mutagenesis and sequencing) and constraint-based metabolic modelling represent highly complementary approaches. They can be used to probe the core genetic and metabolic networks underlying a biological process, revealing invaluable information for synthetic biology engineering of microbial cell factories. However, while algorithms exist for integration of –omics data sets with metabolic models, no method has been explicitly developed for integration of Tn-seq data with metabolic reconstructions.ResultsWe report the development of Tn-Core, a Matlab toolbox designed to generate gene-centric, context-specific core reconstructions consistent with experimental Tn-seq data. Extensions of this algorithm allow: i) the generation of context-specific functional models through integration of both Tn-seq and RNA-seq data; ii) to visualize redundancy in core metabolic processes; and iii) to assist in curation ofde novodraft metabolic models. The utility of Tn-Core is demonstrated primarily using aSinorhizobium melilotimodel as a case study.Availability and implementationThe software can be downloaded fromhttps://github.com/diCenzo-GC/Tn-Core. All results presented in this work have been obtained with Tn-Core v. [email protected],[email protected] informationSupplementary data are available at Bioinformatics online.

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Pathway Tools version 23.0 update: software for pathway/genome informatics and systems biology

Briefings in Bioinformatics ◽

10.1093/bib/bbz104 ◽

2019 ◽

Cited By ~ 6

Author(s):

Peter D Karp ◽

Peter E Midford ◽

Richard Billington ◽

Anamika Kothari ◽

Markus Krummenacker ◽

...

Keyword(s):

Data Analysis ◽

Metabolic Networks ◽

Metabolic Flux ◽

Metabolic Model ◽

Supplementary Information ◽

Biological Knowledge ◽

Omics Data ◽

Dynamic Interactions ◽

Metabolomics Data ◽

Knowledge Resources

Abstract Motivation Biological systems function through dynamic interactions among genes and their products, regulatory circuits and metabolic networks. Our development of the Pathway Tools software was motivated by the need to construct biological knowledge resources that combine these many types of data, and that enable users to find and comprehend data of interest as quickly as possible through query and visualization tools. Further, we sought to support the development of metabolic flux models from pathway databases, and to use pathway information to leverage the interpretation of high-throughput data sets. Results In the past 4 years we have enhanced the already extensive Pathway Tools software in several respects. It can now support metabolic-model execution through the Web, it provides a more accurate gap filler for metabolic models; it supports development of models for organism communities distributed across a spatial grid; and model results may be visualized graphically. Pathway Tools supports several new omics-data analysis tools including the Omics Dashboard, multi-pathway diagrams called pathway collages, a pathway-covering algorithm for metabolomics data analysis and an algorithm for generating mechanistic explanations of multi-omics data. We have also improved the core pathway/genome databases management capabilities of the software, providing new multi-organism search tools for organism communities, improved graphics rendering, faster performance and re-designed gene and metabolite pages. Availability The software is free for academic use; a fee is required for commercial use. See http://pathwaytools.com. Contact [email protected] Supplementary information Supplementary data are available at Briefings in Bioinformatics online.

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BioISO: an objective-oriented application for assisting the curation of genome-scale metabolic models

10.1101/2021.03.07.434259 ◽

2021 ◽

Author(s):

Fernando Cruz ◽

João Capela ◽

Eugénio C. Ferreira ◽

Miguel Rocha ◽

Oscar Dias

Keyword(s):

Biological Networks ◽

In Silico ◽

Metabolic Model ◽

Gap Filling ◽

Search Spaces ◽

Link Type ◽

Manual Curation ◽

Balance Analysis ◽

Metabolic Models ◽

Genome Scale

AbstractAs the reconstruction of Genome-Scale Metabolic Models becomes standard practice in systems biology, the number of organisms having at least one metabolic model at the genome-scale is peaking at an unprecedented scale. The automation of several laborious tasks, such as gap-finding and gap-filling, allowed to develop GSMMs for poorly described organisms. However, such models’ quality can be compromised by the automation of several steps, which may lead to erroneous phenotype simulations.The Biological networks constraint-based In Silico Optimization (BioISO) is a computational tool aimed at accelerating the reconstruction of Genome-Scale Metabolic Models. This tool facilitates the manual curation steps by reducing the large search spaces often met when debugging in silico biological models. BioISO uses a recursive relation-like algorithm and Flux Balance Analysis to evaluate and guide debugging of in silico phenotype simulations. The potential of BioISO to guide the debugging of model reconstructions was showcased using GSMMs available in literature and compared with the results of two other state-of-the-art gap-filling tools (Meneco and fastGapFill). Furthermore, BioISO was used as Meneco’s gap-finding algorithm to reduce the number of proposed solutions (reaction sets) for filling the gaps.BioISO was implemented as a webserver available at https://bioiso.bio.di.uminho.pt; and integrated into merlin as a plugin. BioISO’s implementation as a Python™ package can also be retrieved from https://github.com/BioSystemsUM/BioISO.

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Systematically gap-filling the genome-scale metabolic model of CHO cells

10.1101/2020.01.27.921296 ◽

2020 ◽

Cited By ~ 1

Author(s):

Hamideh Fouladiha ◽

Sayed-Amir Marashi ◽

Shangzhong Li ◽

Zerong Li ◽

Helen O. Masson ◽

...

Keyword(s):

Cho Cells ◽

Predictive Power ◽

Cell Metabolism ◽

Chinese Hamster ◽

Metabolic Model ◽

Gap Filling ◽

Cell Factories ◽

New Genes ◽

Metabolic Models ◽

Genome Scale

AbstractObjectiveChinese hamster ovary (CHO) cells are the leading cell factories for producing recombinant proteins in the biopharmaceutical industry. In this regard, constraint-based metabolic models are useful platforms to perform computational analysis of cell metabolism. These models need to be regularly updated in order to include the latest biochemical data of the cells, and to increase their predictive power. Here, we provide an update to iCHO1766, the metabolic model of CHO cells.ResultsWe expanded the existing model of Chinese hamster metabolism with the help of four gap-filling approaches, leading to the addition of 773 new reactions and 335 new genes. We incorporated these into an updated genome-scale metabolic network model of CHO cells, named iCHO2101. In this updated model, the number of reactions and pathways capable of carrying flux is substantially increased.ConclusionsThe present CHO model is an important step towards more complete metabolic models of CHO cells.

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Learning from metabolic networks: current trends and future directions for precision medicine.

Current Medicinal Chemistry ◽

10.2174/0929867328666201217103148 ◽

2020 ◽

Vol 28 ◽

Author(s):

Ilaria Granata ◽

Mario Manzo ◽

Ari Kusumastuti ◽

Mario R Guarracino

Keyword(s):

Systems Biology ◽

Precision Medicine ◽

Metabolic Networks ◽

Information Modeling ◽

Model Systems ◽

Specific Information ◽

Important Indicator ◽

Predictive Values ◽

Metabolic Systems ◽

Metabolic Models

Purpose: Systems biology and network modeling represent, nowadays, the hallmark approaches for the development of predictive and targeted-treatment based precision medicine. The study of health and disease as properties of the human body system allows the understanding of the genotype-phenotype relationship through the definition of molecular interactions and dependencies. In this scenario, metabolism plays a central role as its interactions are well characterized and it is considered an important indicator of the genotype-phenotype associations. In metabolic systems biology, the genome-scale metabolic models are the primary scaffolds to integrate multi-omics data as well as cell-, tissue-, condition-specific information. Modeling the metabolism has both investigative and predictive values. Several methods have been proposed to model systems, which involve steady-state or kinetic approaches, and to extract knowledge through machine and deep learning. Method: This review collects, analyzes, and compares the suitable data and computational approaches for the exploration of metabolic networks as tools for the development of precision medicine. To this extent, we organized it into three main sections: "Data and Databases", "Methods and Tools", and "Metabolic Networks for medicine". In the first one, we have collected the most used data and relative databases to build and annotate metabolic models. In the second section, we have reported the state-of-the-art methods and relative tools to reconstruct, simulate, and interpret metabolic systems. Finally, we have reported the most recent and innovative studies which exploited metabolic networks for the study of several pathological conditions, not only those directly related to the metabolism. Conclusion: We think that this review can be a guide to researchers of different disciplines, from computer science to biology and medicine, in exploring the power, challenges and future promises of the metabolism as predictor and target of the so-called P4 medicine (predictive, preventive, personalized and participatory).

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The Metano Modeling Toolbox MMTB: An Intuitive, Web-Based Toolbox Introduced by Two Use Cases

Metabolites ◽

10.3390/metabo11020113 ◽

2021 ◽

Vol 11 (2) ◽

pp. 113

Author(s):

Julia Koblitz ◽

Sabine Will ◽

S. Riemer ◽

Thomas Ulas ◽

Meina Neumann-Schaal ◽

...

Keyword(s):

Metabolic Networks ◽

Point Of View ◽

Use Cases ◽

Mathematical Methods ◽

State Behavior ◽

Web Based ◽

Research Fields ◽

Balance Analysis ◽

Metabolic Models ◽

User Friendly

Genome-scale metabolic models are of high interest in a number of different research fields. Flux balance analysis (FBA) and other mathematical methods allow the prediction of the steady-state behavior of metabolic networks under different environmental conditions. However, many existing applications for flux optimizations do not provide a metabolite-centric view on fluxes. Metano is a standalone, open-source toolbox for the analysis and refinement of metabolic models. While flux distributions in metabolic networks are predominantly analyzed from a reaction-centric point of view, the Metano methods of split-ratio analysis and metabolite flux minimization also allow a metabolite-centric view on flux distributions. In addition, we present MMTB (Metano Modeling Toolbox), a web-based toolbox for metabolic modeling including a user-friendly interface to Metano methods. MMTB assists during bottom-up construction of metabolic models by integrating reaction and enzymatic annotation data from different databases. Furthermore, MMTB is especially designed for non-experienced users by providing an intuitive interface to the most commonly used modeling methods and offering novel visualizations. Additionally, MMTB allows users to upload their models, which can in turn be explored and analyzed by the community. We introduce MMTB by two use cases, involving a published model of Corynebacterium glutamicum and a newly created model of Phaeobacter inhibens.

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Model-guided identification of novel gene amplification targets for improving succinate production in Escherichia coli NZN111

Integrative Biology ◽

10.1039/c7ib00077d ◽

2017 ◽

Vol 9 (10) ◽

pp. 830-835 ◽

Cited By ~ 2

Author(s):

Xingxing Jian ◽

Ningchuan Li ◽

Qian Chen ◽

Qiang Hua

Keyword(s):

Escherichia Coli ◽

Metabolic Engineering ◽

Gene Amplification ◽

Metabolic Networks ◽

Computational Analysis ◽

Succinate Production ◽

Novel Gene ◽

Metabolic Models ◽

Genome Scale

Reconstruction and application of genome-scale metabolic models (GEMs) have facilitated metabolic engineering by providing a platform on which systematic computational analysis of metabolic networks can be performed.

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Investigating the chemolithoautotrophic and formate metabolism of Nitrospira moscoviensis by constraint-based metabolic modeling and 13C-tracer analysis

10.1101/2021.02.18.431926 ◽

2021 ◽

Author(s):

Christopher E. Lawson ◽

Aniela B. Mundinger ◽

Hanna Koch ◽

Tyler B. Jacobson ◽

Coty A. Weathersby ◽

...

Keyword(s):

Wastewater Treatment ◽

Metabolic Networks ◽

Metabolic Model ◽

Scale Model ◽

Nitrifying Bacteria ◽

Metabolomic Analysis ◽

A Genome ◽

Nitrite Oxidizing Bacteria ◽

Genome Scale ◽

Nitrospira Moscoviensis

AbstractNitrite-oxidizing bacteria belonging to the genus Nitrospira mediate a key step in nitrification and play important roles in the biogeochemical nitrogen cycle and wastewater treatment. While these organisms have recently been shown to exhibit metabolic flexibility beyond their chemolithoautotrophic lifestyle, including the use of simple organic compounds to fuel their energy metabolism, the metabolic networks controlling their autotrophic and mixotrophic growth remain poorly understood. Here, we reconstructed a genome-scale metabolic model for Nitrospira moscoviensis (iNmo686) and used constraint-based analysis to evaluate the metabolic networks controlling autotrophic and formatotrophic growth on nitrite and formate, respectively. Subsequently, proteomic analysis and 13C-tracer experiments with bicarbonate and formate coupled to metabolomic analysis were performed to experimentally validate model predictions. Our findings support that N. moscoviensis uses the reductive tricarboxylic acid cycle for CO2 fixation. We also show that N. moscoviensis can indirectly use formate as a carbon source by oxidizing it first to CO2 followed by reassimilation, rather than direct incorporation via the reductive glycine pathway. Our study offers the first measurements of Nitrospira’s in vivo central carbon metabolism and provides a quantitative tool that can be used for understanding and predicting their metabolic processes.ImportanceNitrospira are globally abundant nitrifying bacteria in soil and aquatic ecosystems and wastewater treatment plants, where they control the oxidation of nitrite to nitrate. Despite their critical contribution to nitrogen cycling across diverse environments, detailed understanding of their metabolic network and prediction of their function under different environmental conditions remains a major challenge. Here, we provide the first constraint-based metabolic model of N. moscoviensis representing the ubiquitous Nitrospira lineage II and subsequently validate this model using proteomics and 13C-tracers combined with intracellular metabolomic analysis. The resulting genome-scale model will serve as a knowledge base of Nitrospira metabolism and lays the foundation for quantitative systems biology studies of these globally important nitrite- oxidizing bacteria.

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The unraveling of balanced complexes in metabolic networks

10.1101/2021.03.23.436554 ◽

2021 ◽

Author(s):

Damoun Langary ◽

Anika Kueken ◽

Zoran Nikoloski

Keyword(s):

Steady State ◽

Large Scale ◽

Metabolic Networks ◽

Network Models ◽

Steady States ◽

Biochemical Networks ◽

Computational Approaches ◽

Metabolic Models ◽

Large Scale Networks ◽

General Conditions

Balanced complexes in biochemical networks are at core of several theoretical and computational approaches that make statements about the properties of the steady states supported by the network. Recent computational approaches have employed balanced complexes to reduce metabolic networks, while ensuring preservation of particular steady-state properties; however, the underlying factors leading to the formation of balanced complexes have not been studied, yet. Here, we present a number of factorizations providing insights in mechanisms that lead to the origins of the corresponding balanced complexes. The proposed factorizations enable us to categorize balanced complexes into four distinct classes, each with specific origins and characteristics. They also provide the means to efficiently determine if a balanced complex in large-scale networks belongs to a particular class from the categorization. The results are obtained under very general conditions and irrespective of the network kinetics, rendering them broadly applicable across variety of network models. Application of the categorization shows that all classes of balanced complexes are present in large-scale metabolic models across all kingdoms of life, therefore paving the way to study their relevance with respect to different properties of steady states supported by these networks.

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BioPartsBuilder: a synthetic biology tool for combinatorial assembly of biological parts

Bioinformatics ◽

10.1093/bioinformatics/btv664 ◽

2015 ◽

Vol 32 (6) ◽

pp. 937-939 ◽

Cited By ~ 8

Author(s):

Kun Yang ◽

Giovanni Stracquadanio ◽

Jingchuan Luo ◽

Jef D. Boeke ◽

Joel S. Bader

Keyword(s):

Large Scale ◽

Supplementary Information ◽

Market Place ◽

Design Standards ◽

Reusable Components ◽

Assembly Design ◽

Amazon Web Services ◽

Dna Elements ◽

Combinatorial Assembly ◽

The Cost

Abstract Summary: Combinatorial assembly of DNA elements is an efficient method for building large-scale synthetic pathways from standardized, reusable components. These methods are particularly useful because they enable assembly of multiple DNA fragments in one reaction, at the cost of requiring that each fragment satisfies design constraints. We developed BioPartsBuilder as a biologist-friendly web tool to design biological parts that are compatible with DNA combinatorial assembly methods, such as Golden Gate and related methods. It retrieves biological sequences, enforces compliance with assembly design standards and provides a fabrication plan for each fragment. Availability and implementation: BioPartsBuilder is accessible at http://public.biopartsbuilder.org and an Amazon Web Services image is available from the AWS Market Place (AMI ID: ami-508acf38). Source code is released under the MIT license, and available for download at https://github.com/baderzone/biopartsbuilder. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

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