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Kulyashov, M.A.; Kolmykov, S.K.; Khlebodarova, T.M.; Akberdin, I.R. State-of the-Art Constraint-Based Modeling of Microbial Metabolism. Encyclopedia. Available online: https://encyclopedia.pub/entry/53209 (accessed on 03 May 2024).
Kulyashov MA, Kolmykov SK, Khlebodarova TM, Akberdin IR. State-of the-Art Constraint-Based Modeling of Microbial Metabolism. Encyclopedia. Available at: https://encyclopedia.pub/entry/53209. Accessed May 03, 2024.
Kulyashov, Mikhail A., Semyon K. Kolmykov, Tamara M. Khlebodarova, Ilya R. Akberdin. "State-of the-Art Constraint-Based Modeling of Microbial Metabolism" Encyclopedia, https://encyclopedia.pub/entry/53209 (accessed May 03, 2024).
Kulyashov, M.A., Kolmykov, S.K., Khlebodarova, T.M., & Akberdin, I.R. (2023, December 28). State-of the-Art Constraint-Based Modeling of Microbial Metabolism. In Encyclopedia. https://encyclopedia.pub/entry/53209
Kulyashov, Mikhail A., et al. "State-of the-Art Constraint-Based Modeling of Microbial Metabolism." Encyclopedia. Web. 28 December, 2023.
State-of the-Art Constraint-Based Modeling of Microbial Metabolism
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This entry is adapted from the peer-reviewed paper 10.3390/microorganisms11122987

Methanotrophy is the ability of an organism to capture and utilize the greenhouse gas, methane, as a source of energy-rich carbon. Over the years, significant progress has been made in understanding of mechanisms for methane utilization, mostly in bacterial systems, including the key metabolic pathways, regulation and the impact of various factors (iron, copper, calcium, lanthanum, and tungsten) on cell growth and methane bioconversion. The implementation of -omics approaches provided vast amount of heterogeneous data that require the adaptation or development of computational tools for a system-wide interrogative analysis of methanotrophy. The genome-scale mathematical modeling of its metabolism has been envisioned as one of the most productive strategies for the integration of muti-scale data to better understand methane metabolism and enable its biotechnological implementation. 

genome-scale metabolic modeling constraint-based modeling context-specific modeling pipeline tool transcriptomics methanotrophy

1. The Stages of Metabolic Model Reconstruction

The development of a GSM model of any metabolic process involves several fundamental steps (Figure 1). One of the first steps is the reconstruction of the metabolic network, which is performed based on the annotation data of the sequenced genome of an organism of interest and includes information about the genes and proteins/enzymes encoded by them, biochemical reactions of the analyzed metabolic pathway and metabolites [1][2]. The sources of this information are databases and web-portals which will be discussed below.
Microorganisms 11 02987 g001
Figure 1. Development stages of a genome-scale metabolic model of any metabolic process (created with BioRender.com). A key step in constraint-based modeling is the construction of the GSM model which is represented at the pyramid’s base. This part of the pyramid briefly illustrates the main approaches (bottom-up: from in vitro data via enzymatic reactions to a metabolic map. Top-down: from omics data to a metabolic map) for GSM models reconstruction. The next block demonstrates an equally important stage—the modification and expansion/reduction of the original GSM model. The block preceding the vertex reflects the model simulation and further visualization of the obtained in silico results using metabolic maps. At the top of the pyramid is a relatively new stage that provides a significant refinement of the model’s predictions through the integration of omics data into the original GSM model for the reconstruction of context-specific models (CS models). Tools developed using the Python programming language are highlighted in pink, while software packages written in MATLAB are highlighted in blue.
The functionality of this network is confirmed at the next stage of model reconstruction by additional information from published data and experiments conducted for model organisms and/or closely related species [3][4].
The third step adds species-specific physiological, biochemical, physical and phenotypic characteristics of the network components including the thermodynamic and kinetic parameters of reactions and the metabolites available in publications or databases.
The resulting metabolic map allows one to mathematically link enzymatic reactions and the metabolites participating in them as substrates or products in a certain stoichiometry using a stoichiometric S matrix.
Generally, for GSM models validation it is assumed that the biological system is in a quasi-equilibrium state, i.e., the concentrations of metabolites do not change in the system, and therefore the right-hand sides of the system of differential equations describing the change in the concentration of metabolites can be equated to zero. Thus, a system of linear algebraic equations is obtained. In other words, the assumption that the metabolic system has reached a quasi-equilibrium implies that the sum of all reaction fluxes in which a certain metabolite is synthesized is equal to the sum of enzymatic reaction fluxes in which this metabolite is consumed.
These constraints on flux balances are mathematically formulated as 𝑆∗𝑉=0, where S is the stoichiometric matrix and V is the vector of reaction rates (fluxes) of the studied metabolic system. As a result, this mathematical expression was performed under quasi-equilibrium conditions and additionally introduced constraints on the rates of intracellular reversible and irreversible reactions (on the lower (LB) and upper boundaries (UB), respectively), as well as on the reactions of transport exchange between the compartments of the model, which enables one to conduct a flux balance analysis (FBA) using linear programming methods at the last stage of model development. FBA addresses one of the optimization problems [1][2][5]. For example, optimizing the production of cellular biomass or one of the targeted, biotechnologically important products under wild-type phenotype conditions and/or under various genetic modifications (knockouts, increased expression of a gene encoding a particular enzyme) [6][7][8].
A GSM model constructed in this way requires further refinement based on available experimental data for a more adequate description of the metabolism of the object under study. It will ultimately provide more relevant and accurate predictions of phenotypic changes in the growth of the bacterium under certain conditions of the culturing or as a result of genetic modifications employing in silico experiments [6][9][10][11][12].

2. Databases of the Microorganisms’ Genomes

Numerous databases and web-portals resources have been developed and are available for the initial metabolic pathway reconstruction, including BioCyc [13], KEGG [14], GenBank [15], Ensembl Bacteria [16], PATRIC [17], MicroScope [18] and IMG/M [19].
BioCyc (https://biocyc.org/) is a web-portal of prokaryote genomes that integrates sequenced genomes with expert-processed an information from published data as well as an information imported from other biological databases. The BioCyc collection consists of over 20,040 pathway/genome databases (PGDBs) [13], each containing the complete genome and putative metabolic network of a single organism, which is predicted by the Pathway Tools software and comprises metabolites, enzymatic reactions and metabolic pathways [20]. BioCyc provides extensive search and visualization tools, as well as toolkits for omics data analysis, comparative genomic analysis, metabolic pathways search, and metabolic model generation. BioCyc expert analytical information includes experimental data on gene functions, kinetic parameters of enzymatic reactions, enzyme activators and inhibitors. The database also contains textual mini-reviews authored by expert curators that summarize information on enzymes and pathways with corresponding references [13][21]. The main drawbacks of this resource now are its limited use without a paid subscription.
KEGG (Kyoto Encyclopedia of Genes and Genomes, https://www.genome.jp/kegg/) is manually curated resource represented by a set of databases and associated bioinformatics software for analyzing and modeling the functional behavior of a cell or higher-order organism based on information about its genome. KEGG includes both data relevant for biomedical research (e.g., KEGG DISEASE and KEGG DRUG) and tools for the analysis of bulk molecular data [14][22][23]. Of particular note are the KEGG PATHWAY metabolic maps, which is a powerful tool in the reconstruction of GSM models enabling the analysis of metabolic pathways for a selected organism.
UniProt (https://www.uniprot.org/, [24]), Brenda (https://www.brenda-enzymes.info/index.php, [25]) and Sabio-RK (http://sabio.h-its.org/, [26]) are also very useful and widely cited resources for biochemical data and enzymes annotation that are essential for proper metabolic pathway reconstruction.
Whereas Genbank (https://www.ncbi.nlm.nih.gov/genbank/) is an annotated collection of publicly available nucleotide sequences for more than 500 000 formally described species [27], Ensembl Bacteria (https://bacteria.ensembl.org/index.html)—a portal containing specifically bacterial and archaea genomes as well as a collection of data on genes and the proteins they encode [28]. Ensembl has BLAST and an algorithm based on hidden Markov models as a tool to seek protein motifs. Pan-taxonomic comparison tools are available for key microbial species. The current version of the portal also presents genome annotation capabilities, includes transcriptome data, and supports comparative analysis [16]. However, this resource lacks tools for reconstructing and analyzing metabolic pathways.
PATRIC (http://www.patricbrc.org) is designed to support biomedical research aimed at studying bacterial infectious diseases through the integration of pathogen information using available data and tools for analysis. Integrated data covers genomics, transcriptomics, protein-protein interactions, 3D protein structures and metadata from various organisms. PATRIC provides genome assembly and annotation as well as RNA-seq data analysis [17][29][30].

3. GSM Models for C1-Utilizing Bacteria

The databases described above serve as the basis for construction of GSM models for prokaryotes, including models for unique groups of microorganisms such as methanotrophs and methylotrophs. These are bacteria and archaea that utilize C1-containing hydrocarbons as their sole carbon sources for growth. The developed models are employed to study the metabolic capabilities of diverse strains of C1-utilizing bacteria, including growth on methane and/or methanol using various metabolic pathways. These models also can be used to predict more efficient ways for the production of target value-added compounds and to study the peculiarities of their metabolism under different cultivation conditions (see reviews: [31][32][33][34]).
The iMb5G model for M. buryatense 5G, the first published GSM model of methanotrophic bacteria, was used to interrogate the feasibility of three possible modes of methane oxidation (redox-arm, the direct coupling mode and uphill electron transfer) and to test the efficiency of carbon conversion via different C1 utilization pathways including variants of ribulose monophosphate pathway and the serine cycle. The extended version, iMb5GB1 was applied to explore the ability of the methanotrophic strain to be a fatty acid producer [35][36].
The iIA407 model for a closely-related strain, M. alcaliphilum 20ZR, was constructed based on genomic, enzymatic and transcriptomic data and refined using published 13C-carbon-labeling [37] and original continuous cell culture parameters, which enabled the uncovering of the reversibility of the phosphoketolase reaction leading to the carbon flux from acetyl-CoA to xulylose-5-phosphate and highly branched TCA cycle [10]. Furthermore, a slightly extended version of the model, iIA409, was also applied to investigate the mechanisms of improved growth vs. carbon conversion for the strain growth in different media contents [38].
The iMcBath and iMC535 models for Methylococcus capsulatus (Bath), which is an obligate methanotroph, were also built to study the pathways and mechanisms of the methane utilization in order to estimate methanotroph’s biotechnological potential and pave the way for rational strain design [12][39].
A series of GSM models was developed for several representatives of type II (alpha-proteobacteria) methanotrophs that use the serine cycle for carbon assimilation [40][41][42]. The built GSM models enabled the exploration of distinct features of the metabolism in Methylocystis species and Methylocella silvestris (redox-arm mechanisms as a general feature of type II methanotrophs, growth on C1 and C2 compounds, influence of the nitrogen source and mechanisms of the poly-3-hydroxybutyrate (PHB) accumulation). The metabolic models provide an effective in silico basis for the development of metabolic engineering platforms for these particular strains.
The iJV806 model describing the metabolism of Methylomicrobium album BG8, another representative of obligate aerobic gammaproteobacterial methanotrophs was recently reconstructed to study the metabolic states of the strain under growth on methane or methanol promoting biomass production and excretion of carbon dioxide and organic acids. The last ones can be considered valuable compounds for proposing the biotechnological potential of M. album BG8 [43].

4. Web Resources and Tools for Automatic Reconstruction of GSM Models

Currently, there is a fairly large number of web resources and programs for the automatic reconstruction of GSM models (see review: [44]). 

4.1. Web Resources

One of the most popular web-based resources for reconstructing and analyzing GSM models is Kbase [4], which not only offers automatic model reconstruction, but also provides modules for sequencing data processing. Kbase contains more than 160 applications, including the analysis of user data from raw short reads to fully assembled and annotated genomes, followed by the ability to analyze transcriptome data and develop metabolic models. The set of tools implemented in Kbase makes it possible to build a complete pipeline for the reconstruction and analysis of a GSM model. Moreover, Kbase gives an opportunity for model visualization as with the Kbase tools, which presents the model as a connected graph of reactions and metabolites. In addition, the distribution of fluxes can be visualized via Escher metabolic maps [45]. Kbase also enables users to integrate their original code into data analysis and also allows the addition of external applications.
ModelSEED is a web resource linked to Kbase that supports the creation of GSM models not only for microorganisms, but also for plants. It allows the use of a linked RAST profile (http://rast.nmpdr.org) [46] with user-annotated genomes or using existed annotations from the PATRIC database (http://www.patricbrc.org). Users can also choose their own FASTA annotation file. This version of the resource provides synonyms for reactions and metabolites in other databases, and supports the gap-filling algorithm, with the option to use a reaction file from the user [47]. A visualization of models via the Escher web tool has recently become available in ModelSEED according to the official website: https://modelseed.org/. The updated version of the reconstruction pipeline, ModelSEED v2 (MS2), has been released with improved representation of energy metabolism [48].
FAME (flux analysis and modeling environment) is also a web-based tool for the development of GSM models. It can be employed for generation, editing, running and analysis/visualization tasks in a single program [49]. The main distinguishing FAME feature is that analysis results can be visualized on the generally accepted KEGG metabolic map. But this is also its essential limitation, since models cannot be created for microorganisms that are not in KEGG. It should be noted that the web service is not available at this moment (verified on 27 July 2023).
MicrobesFlux is another web resource for GSM models reconstruction, which enables model building based on information about reactions and metabolites from the KEGG database, similar to FAME [50]. The source code is currently in the public domain, but the resource itself is not available.

4.2. GUI-Based Desktop Programs

One of the most popular programs for reconstructing GSM models is Pathway Tools (Ptools), which supports the construction and maintenance of databases specific to the organism under study (PGDB), and also has the ability to work in a web application, although its functionality is partially limited there [20]. Its features interactively explore, visualize and edit various components of the reconstructed model, such as genes, operons, enzymes (including transporter proteins), metabolites, reactions and metabolic pathways; analyze omics data taking into account the reconstructed metabolic map; and even develop microbial community models.
Other existing programs are also in this area of interest. For example, the Merlin program (https://www.merlin-sysbio.org/) has a user-friendly GUI and allows semi-automatic model reconstruction and its editing based on the KEGG database data [51]. Furthermore, the BiGG database was recently added to the tool as a source for model reconstruction [52]. Additionally, Merlin provides the opportunity to visualize the model using the Escher program, unlike other GUI-based tools. The last, but very essential advantage of the Merlin is a high-quality tutorial for working with the program.
MetaDraft 0.9.2 is a full-featured platform with a graphical interface for genome-scale metabolic model reconstruction. It utilizes a constantly updated, user-expandable database of template models [53]. GEMSiRV is a GUI platform [54] that also uses templates from already existing mathematical models for model reconstruction. This program provides model editing and visualization using built-in tools.

4.3. Packages and Command Line Programs

This group of resources includes a number of programs, which are presented below.
ScrumPy (https://mudshark.brookes.ac.uk/ScrumPy) is one of the first Python-based flexible packages for the reconstruction and analysis of metabolic models. A GSM model is directly constructed from the BioCyc Pathway Genome Database. Moreover, the tool has a modular model definition language that enables the tracking of changes during the model development process and the definition of metabolic subsystems separately [55].
The AuReMe (http://aureme.genouest.org/) program enables the reconstruction of genome-scale models based on information from the MetaCyc, BiGG and KEGG databases [56]. Its key feature is distribution through the Docker container, which eliminates compatibility issues between different program components.
gapseq (https://github.com/jotech/gapseq) is a tool for metabolic pathways prediction and automatical reconstruction of bacterial metabolic models using a curated reaction database and a novel gap-filling algorithm [57]. The program is written in the R language and distributed through the R package repository, Cran.
A number of programs allow the automatic reconstruction of models using MATLAB. Examples of such tools are AutoKEGGRec [58] and RAVEN 2.0 [59]. The advantage of these programs is the compatibility with COBRA Toolbox 3, which gives the possibility to perform reconstruction and further analyze the model within the same project. AutoKEGGRec is a simple program for the automatic reconstruction of organisms based on KEGG data, which supports the reconstruction of models for several organisms represented in the KEGG database at once. It should be noted that this program has not been updated for more than 5 years. RAVEN v2, unlike AutoKEGGRec, allows the reconstruction of models not only on the basis of the KEGG database, but also using MetaCyc database and templates of existing models.
There are also programs written in Python such as CarveMe [60], moped [61], Reconstructor [62], Bactabolize [63] and AuCoMe [64] that provide the possibility of reconstructing the model using their own resources.
CarveMe (https://github.com/cdanielmachado/carveme) uses expert-curated GSM models from the BiGG database [52] to reconstruct models as initial templates. To improve the quality of reconstructed models, the program also has its own gapfilling algorithm based on the bottom-up approach. A limitation in working with CarveMe is the need to use commercial solvers such as MBI CPLEX and Gurobi.
moped (https://gitlab.com/qtb-hhu/moped) is a Python-based package which provides an opportunity for GSM model reconstruction from a genome sequence or by importing data from SBML [65] file or the MetaCyc or BioCyC databases as a PGDB flat file using the BLAST algorithm. Unlike most other tools, it uses a topological gap-filling algorithm [66], which is a crucial step at the process of GSM models reconstruction. Moreover, it includes a list of methods for FBA, topological model analysis and also moped model objects that are easily converted into Cobrapy model objects that simplify the integration with a large number of python-based tools for model simulation and analysis [61].
Bactabolize (https://github.com/kelwyres/Bactabolize) is a new command line program that also employs the BiGG database to reconstruct GSM models [63]. This tool was validated in the modeling a pathogenic strain of Klebsiella pneumoniae and demonstrated better model reconstruction compared to CarveMe. Bactabolize is distributed through the conda environment, which allows, as in the case of the Docker container, the elimination of the potential problems of program version incompatibility. By using the Cobrapy Toolkit, the reconstructed models are compatible with this package. Bactabolize also provides the ability to simulate the model in order to analyze the effect of single mutations on cell growth and to predict the substrates required for cell growth.

2.5. Web-Resources and Tools for Analysis of GSM Models

COBRA Toolbox (COnstraint-Based Reconstruction and Analysis) is one of the most widely used tools for handling GSM models. It includes methods of reconstruction and modeling, topological analysis, network visualization, as well as network integration of metabolic, transcriptomic, proteomic and thermodynamic data [67]. It contains a set of software available for use in the MATLAB program.
COBRApy is a software package for modeling represented by COBRA methods and written in the Python programming language. Inheriting the many strengths of the Python language, COBRApy provides the core capabilities of COBRA modeling and has a dedicated module for interfacing with the COBRA Toolbox [68]. COBRApy makes it possible to integrate models with databases and other data sources and does not require commercial software such as MATLAB.
OptFlux is an open-source software written in the Java programming language. Opflux is the first tool that to enable optimization problems aimed at identifying target genes and/or reactions for metabolic engineering using evolutionary algorithms or the previously proposed OptKnock algorithm [69]. Due to their availability, it has become possible to use stoichiometric metabolic models for a variety of tasks, including modeling the organism’s phenotype using methods of flux balance analysis, minimizing metabolic adjustment and its on/off regulation. One of the advantages of OptFlux is the presence of a GUI interface, which considerably simplifies the user’s operation with a mathematical model, unlike COBRA Toolbox and COBRApy, which require programming skills [70].
MEWpy is a software package for exploring the different classes of constraint-based models, including metabolic, enzymatic and regulatory models. MEWpy is written in the Python programming language by the developers of Optflux and allows the use of different toolkits, such as GECKO [71] and OptRAM [72], to predict the phenotype of a microorganism and optimize its growth. The advantage of MEWpy is the ability to work with GSM models derived from COBRApy, which simplifies the process of optimization and modification of the mathematical model [73].
MOST (Metabolic Optimization and Simulation Tool) is a software written in the Java programming language, which, like Optflux, has a user-friendly GUI interface. Its distinctive feature is the presence of a proprietary GDBB algorithm for searching for gene knockouts to optimize the target product yield, as well as the E-Flux2 and SPOT algorithms [74] to integrate transcriptomic data into a metabolic model with an easy-to-use interface with functions editing like Excel. MOST has a reaction editor with a built-in check for changes to prevent syntax errors when editing reaction equations, as well as the ability to analyze the flux balance, their variability and visualize the resulting calculations on a custom metabolic map represented as a graph of metabolic reactions [75]. The drawback of MOST is the lack of updates and development of the original version of the product.
In silico discovery is a commercial software designed for the graphically oriented reconstruction of constrained-based mathematical models, as well as their modification and calculation. The program has a user-friendly interface, an extensive set of tools for model reconstruction by integrating data from different databases and visual control over all integrated reactions, and tools for model tuning and searching for problems associated with the reconstruction, including various cycles, unused (dead-end) metabolites and reactions. There are algorithms for model calculation and optimization, taking into account its kinetic parameters, which makes it possible to expand the constrained-based model into a dynamic one. The main disadvantage of the resource is its unavailability for academic use and, more importantly, its own modeling format, which differs from the widely used SBML (systems biology markup language) [65], which creates the problem of using existing models and independent evaluation of modeling results.
CAVE is a web service for integrated calculation, visualization, research and correction of metabolic pathways [76], which can analyze and visualize them for a large number of genome-scale metabolic models using its own graph tool, similar to the analogous tool for visualization in MOST and OptFlux. It has a user-friendly interface that allows editing model responses and the environment for growth when optimizing the model and the cloud server, on which the calculations take place, making it easy to use without the need to install any software or have your own computational capacity. The alternative web application for computation and interactive visualization of fluxes distribution predicted by FBA of GSM models, Fluxer, implemented in Python provides different ways for metabolic network representations based on spanning trees, k-shortest paths and complete graphs [77]. Developers of the tool are planning to significantly improve the application capabilities to customize FBA calculations and graph layouts beyond the used methods.
Cameo is a constraint-based modeling software package written in the Python programming language [78], based on the previously described COBRApy package, with a slightly different syntax. The package contains integrated OptKnock and OptGene modules, described in the MEWpy library and the OptFlux program, that solves problems of biotechnological engineering, but it lacks evolutionary functions and tasks related to co-optimization functions. It is possible to visualize the model on metabolic maps by integrating the Escherpy library into Cameo, as well as a large set of tools for model analysis and visualization.
ReFramed is a constraint-based modeling software package, also written in the Python, which is the refactored version of the previous Frame package. It is based, as in the case of Cameo, on COBRApy and the Escherpy visualization package. Initially, only commercial solvers such as Gurobi and MBI CPLEX (under an academic license) were available for model optimization, but now an Optlang module is available that allows one to connect other solvers of his/her choice. There are tools for model analysis and the ability to extend the model by integrating transcriptomic data with modules for reconstructing context-specific models, such as GIMME and E-Flux (see description below). There is also a module to optimize the analysis of SteadyCom community models [79].
Thus, advances in computational methods and continuously developing tools for FBA led to the GSM modeling approach that has become a guiding tool for cell factory design. The diversity of tools and web sources for the analysis of GSM models provides a broad feasibility for development of different metabolic engineering strategies depending on biotechnological requirements and tasks. However, GSM models analyzed in the vast majority of described software are still far from the real metabolic state of cells because the stoichiometric dependencies alone cannot comprehensively reflect the relationships between different metabolic fluxes considering a certain environmental condition that can stimulate a particular regulatory mechanism on transcription or translation levels. To overcome the limitation, a number of methods implemented in computational tools have been proposed that take into account the specificity or context of the cellular state on the different hierarchical levels via the integration of datasets generated by multiomics measurement techniques.

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Subjects: Microbiology; Mathematical & Computational Biology
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Mikhail A. Kulyashov
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Semyon K. Kolmykov
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Tamara M. Khlebodarova
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Ilya R. Akberdin
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Update Date: 03 Jan 2024
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