Submit to Microorganisms Review for Microorganisms Propose a Special Issue

Journal Menu

Journal Browser

► Journal Browser

Genome-Scale Modeling of Microorganisms in the Real World

Special Issue Editors
Special Issue Information
Published Papers

A special issue of Microorganisms (ISSN 2076-2607). This special issue belongs to the section "Systems Microbiology".

Deadline for manuscript submissions: closed (30 June 2020) | Viewed by 36846

Share This Special Issue

Special Issue Editor

Dr. Nicolai S. Panikov

E-Mail Website
Guest Editor

Department of Chemistry and Chemical Biology, College of Sciences, Northeastern University, Boston, MA 02115, USA
Interests: microbial ecology; biotechnology; growth kinetics and stoichiometry; fermentation; mathematical models of microbial growth; genome-scale metabolic reconstructions; history of microbiology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Presently, NCBI lists nearly 150,000 completed whole-genome projects! With 1,278 sequenced fungal species, 1,422 archaeal, and 21,380 bacterial species, we can state that most of the medically, industrially, and environmentally significant microorganisms have been already sequenced. Genomic data are used in a wide range of fundamental studies as well as diverse medical and industrial applications. However, the practical implementations of genomic data are well below the expected potential. To this gap, we rely on systems biology bottom-up approaches, such as genome-scale models (GEM). Typically GEMs are cellular metabolic network reconstructions converted into a mathematical format and lend themselves to computational treatment. The first GEM was created for viruses (Edwards and Palsson, 1999), but now GEMs have been applied to diverse forms of microbial life and even natural and artificial bacterial communities including human microbiome. GEMs vary from relatively simple and robust FBA (flux balance analysis) to highly sophisticated models simulating gene expression patterns coupled with metabolic networks or even whole-cell dynamic simulation of bacteria with small genomes.

Most efforts are focused on the development of computational tools, which are applied to simplified microbiological data, such as the homogeneous microbial growth of a few model organisms under the simplest possible cultivation conditions (nutrients excess, constant maximum growth rate without any inhibitory or perturbation effects). In real life, environmental factors vary, affecting gene expression, posttranslational modification, and other processes; biosynthesis of the most valuable target products (e.g. antibiotics) occurs during idiophase when growth rate declines, and natural microbial populations in soils, water or human gut only rarely display intensive growth. This Special Issue of the journal Microorganisms aims to bring GEMs closer to real life and the practical challenges of fundamental and applied microbiology. We encourage the submission of papers that address relevant topics, such as the following:

Development of robust GEM simulating microbial metabolism and growth under non-optimal conditions (nutrient limited, intoxicated, under various stress conditions) to dissect molecular mechanisms of stress-response
Experimental validation of GEMs under conditions beyond short-term exponential growth phase (lag-, stationary phase)
Simulation of microbial biosynthesis of secondary metabolites including antibiotics that are not proportional to cell growth
Accounts of changeable cell composition as dependent on environmental factors
GEMs for difficult to culture microorganisms and enigmatic physiological phenomena (substrate-accelerated death, VBNC, phage-induced lysis, drug-resistance)
GEMs of microbial communities taking into account interactions between individual populations

Experimental studies, critical reviews, and ‘current opinion’ comments are all welcome. We especially encourage constructive and productive reports on failures associated with the application of GEMs, such as false negative or false positive predictions contradicting experimental observations that could provide a great opportunity for ‘unplanned’ biological discoveries.

Dr. Nicolai S. Panikov
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Microorganisms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (9 papers)

Download All Papers

Research

Jump to: Review, Other

18 pages, 2553 KiB

Open AccessArticle

Genome-Scale Metabolic Modeling Reveals Metabolic Alterations of Multidrug-Resistant Acinetobacter baumannii in a Murine Bloodstream Infection Model

by Jinxin Zhao, Yan Zhu, Jiru Han, Yu-Wei Lin, Michael Aichem, Jiping Wang, Ke Chen, Tony Velkov, Falk Schreiber and Jian Li

Microorganisms 2020, 8(11), 1793; https://doi.org/10.3390/microorganisms8111793 - 16 Nov 2020

Cited by 9 | Viewed by 3040

Abstract

Multidrug-resistant (MDR) Acinetobacter baumannii is a critical threat to human health globally. We constructed a genome-scale metabolic model iAB5075 for the hypervirulent, MDR A. baumannii strain AB5075. Predictions of nutrient utilization and gene essentiality were validated using Biolog assay and a transposon mutant library. In vivo transcriptomics data were integrated with iAB5075 to elucidate bacterial metabolic responses to the host environment. iAB5075 contains 1530 metabolites, 2229 reactions, and 1015 genes, and demonstrated high accuracies in predicting nutrient utilization and gene essentiality. At 4 h post-infection, a total of 146 metabolic fluxes were increased and 52 were decreased compared to 2 h post-infection; these included enhanced fluxes through peptidoglycan and lipopolysaccharide biosynthesis, tricarboxylic cycle, gluconeogenesis, nucleotide and fatty acid biosynthesis, and altered fluxes in amino acid metabolism. These flux changes indicate that the induced central metabolism, energy production, and cell membrane biogenesis played key roles in establishing and enhancing A. baumannii bloodstream infection. This study is the first to employ genome-scale metabolic modeling to investigate A. baumannii infection in vivo. Our findings provide important mechanistic insights into the adaption of A. baumannii to the host environment and thus will contribute to the development of new therapeutic agents against this problematic pathogen. Full article

(This article belongs to the Special Issue Genome-Scale Modeling of Microorganisms in the Real World)

► Show Figures

Figure 1

20 pages, 5359 KiB

Open AccessArticle

A Genome-Scale Metabolic Model of Thalassiosira pseudonana CCMP 1335 for a Systems-Level Understanding of Its Metabolism and Biotechnological Potential

by Ahmad Ahmad, Archana Tiwari and Shireesh Srivastava

Microorganisms 2020, 8(9), 1396; https://doi.org/10.3390/microorganisms8091396 - 11 Sep 2020

Cited by 7 | Viewed by 3533

Abstract

Thalassiosira pseudonana is a transformable and biotechnologically promising model diatom with an ability to synthesise nutraceuticals such as fucoxanthin and store a significant amount of polyglucans and lipids including omega-3 fatty acids. While it was the first diatom to be sequenced, a systems-level analysis of its metabolism has not been done yet. This work presents first comprehensive, compartmentalized, and functional genome-scale metabolic model of the marine diatom Thalassiosira pseudonana CCMP 1335, which we have termed iThaps987. The model includes 987 genes, 2477 reactions, and 2456 metabolites. Comparison with the model of another diatom Phaeodactylum tricornutum revealed presence of 183 unique enzymes (belonging primarily to amino acid, carbohydrate, and lipid metabolism) in iThaps987. Model simulations showed a typical C3-type photosynthetic carbon fixation and suggested a preference of violaxanthin–diadinoxanthin pathway over violaxanthin–neoxanthin pathway for the production of fucoxanthin. Linear electron flow was found be active and cyclic electron flow was inactive under normal phototrophic conditions (unlike green algae and plants), validating the model predictions with previous reports. Investigation of the model for the potential of Thalassiosira pseudonana CCMP 1335 to produce other industrially useful compounds suggest iso-butanol as a foreign compound that can be synthesized by a single-gene addition. This work provides novel insights about the metabolism and potential of the organism and will be helpful to further investigate its metabolism and devise metabolic engineering strategies for the production of various compounds. Full article

(This article belongs to the Special Issue Genome-Scale Modeling of Microorganisms in the Real World)

► Show Figures

Figure 1

13 pages, 1088 KiB

Open AccessArticle

A Genome-Scale Metabolic Model of 2,3-Butanediol Production by Thermophilic Bacteria Geobacillus icigianus

by Mikhail Kulyashov, Sergey E. Peltek and Ilya R. Akberdin

Microorganisms 2020, 8(7), 1002; https://doi.org/10.3390/microorganisms8071002 - 04 Jul 2020

Cited by 9 | Viewed by 3288

Abstract

The thermophilic strain of the genus Geobacillus, Geobacillus icigianus is a promising bacterial chassis for a wide range of biotechnological applications. In this study, we explored the metabolic potential of Geobacillus icigianus for the production of 2,3-butanediol (2,3-BTD), one of the cost-effective commodity chemicals. Here we present a genome-scale metabolic model iMK1321 for Geobacillus icigianus constructed using an auto-generating pipeline with consequent thorough manual curation. The model contains 1321 genes and includes 1676 reactions and 1589 metabolites, representing the most-complete and publicly available model of the genus Geobacillus. The developed model provides new insights into thermophilic bacterial metabolism and highlights new strategies for biotechnological applications of the strain. Our analysis suggests that Geobacillus icigianus has a potential for 2,3-butanediol production from a variety of utilized carbon sources, including glycerine, a common byproduct of biofuel production. We identified a set of solutions for enhancing 2,3-BTD production, including cultivation under anaerobic or microaerophilic conditions and decreasing the TCA flux to succinate via reducing citrate synthase activity. Both in silico predicted metabolic alternatives have been previously experimentally verified for closely related strains including the genus Bacillus. Full article

(This article belongs to the Special Issue Genome-Scale Modeling of Microorganisms in the Real World)

► Show Figures

Figure 1

12 pages, 1448 KiB

Open AccessArticle

Genome-Scale Metabolic Network Reconstruction and In Silico Analysis of Hexanoic acid Producing Megasphaera elsdenii

by Na-Rae Lee, Choong Hwan Lee, Dong-Yup Lee and Jin-Byung Park

Microorganisms 2020, 8(4), 539; https://doi.org/10.3390/microorganisms8040539 - 09 Apr 2020

Cited by 15 | Viewed by 3977

Abstract

Hexanoic acid and its derivatives have been recently recognized as value-added materials and can be synthesized by several microbes. Of them, Megasphaera elsdenii has been considered as an interesting hexanoic acid producer because of its capability to utilize a variety of carbons sources. However, the cellular metabolism and physiology of M. elsdenii still remain uncharacterized. Therefore, in order to better understand hexanoic acid synthetic metabolism in M. elsdenii, we newly reconstructed its genome-scale metabolic model, iME375, which accounts for 375 genes, 521 reactions, and 443 metabolites. A constraint-based analysis was then employed to evaluate cell growth under various conditions. Subsequently, a flux ratio analysis was conducted to understand the mechanism of bifurcated hexanoic acid synthetic pathways, including the typical fatty acid synthetic pathway via acetyl-CoA and the TCA cycle in a counterclockwise direction through succinate. The resultant metabolic states showed that the highest hexanoic acid production could be achieved when the balanced fractional contribution via acetyl-CoA and succinate in reductive TCA cycle was formed in various cell growth rates. The highest hexanoic acid production was maintained in the most perturbed flux ratio, as phosphoenolpyruvate carboxykinase (pck) enables the bifurcated pathway to form consistent fluxes. Finally, organic acid consuming simulations suggested that succinate can increase both biomass formation and hexanoic acid production. Full article

(This article belongs to the Special Issue Genome-Scale Modeling of Microorganisms in the Real World)

► Show Figures

Figure 1

19 pages, 4737 KiB

Open AccessArticle

Genome-Scale Metabolic Model Reconstruction and in Silico Investigations of Methane Metabolism in Methylosinus trichosporium OB3b

by Sanzhar Naizabekov and Eun Yeol Lee

Microorganisms 2020, 8(3), 437; https://doi.org/10.3390/microorganisms8030437 - 20 Mar 2020

Cited by 19 | Viewed by 5129

Abstract

Methylosinus trichosporium OB3b is an obligate aerobic methane-utilizing alpha-proteobacterium. Since its isolation, M. trichosporium OB3b has been established as a model organism to study methane metabolism in type II methanotrophs. M. trichosporium OB3b utilizes soluble and particulate methane monooxygenase (sMMO and pMMO respectively) for methane oxidation. While the source of electrons is known for sMMO, there is less consensus regarding electron donor to pMMO. To investigate this and other questions regarding methane metabolism, the genome-scale metabolic model for M. trichosporium OB3b (model ID: iMsOB3b) was reconstructed. The model accurately predicted oxygen: methane molar uptake ratios and specific growth rates on nitrate-supplemented medium with methane as carbon and energy source. The redox-arm mechanism which links methane oxidation with complex I of electron transport chain has been found to be the most optimal mode of electron transfer. The model was also qualitatively validated on ammonium-supplemented medium indicating its potential to accurately predict methane metabolism in different environmental conditions. Finally, in silico investigations regarding flux distribution in central carbon metabolism of M. trichosporium OB3b were performed. Overall, iMsOB3b can be used as an organism-specific knowledgebase and a platform for hypothesis-driven theoretical investigations of methane metabolism. Full article

(This article belongs to the Special Issue Genome-Scale Modeling of Microorganisms in the Real World)

► Show Figures

Figure 1

Review

Jump to: Research, Other

48 pages, 128617 KiB

Open AccessReview

Genome-Scale Reconstruction of Microbial Dynamic Phenotype: Successes and Challenges

by Nicolai S. Panikov

Microorganisms 2021, 9(11), 2352; https://doi.org/10.3390/microorganisms9112352 - 14 Nov 2021

Cited by 6 | Viewed by 2601

Abstract

This review is a part of the SI ‘Genome-Scale Modeling of Microorganisms in the Real World’. The goal of GEM is the accurate prediction of the phenotype from its respective genotype under specified environmental conditions. This review focuses on the dynamic phenotype; prediction of the real-life behaviors of microorganisms, such as cell proliferation, dormancy, and mortality; balanced and unbalanced growth; steady-state and transient processes; primary and secondary metabolism; stress responses; etc. Constraint-based metabolic reconstructions were successfully started two decades ago as FBA, followed by more advanced models, but this review starts from the earlier nongenomic predecessors to show that some GEMs inherited the outdated biokinetic frameworks compromising their performances. The most essential deficiencies are: (i) an inadequate account of environmental conditions, such as various degrees of nutrients limitation and other factors shaping phenotypes; (ii) a failure to simulate the adaptive changes of MMCC (MacroMolecular Cell Composition) in response to the fluctuating environment; (iii) the misinterpretation of the SGR (Specific Growth Rate) as either a fixed constant parameter of the model or independent factor affecting the conditional expression of macromolecules; (iv) neglecting stress resistance as an important objective function; and (v) inefficient experimental verification of GEM against simple growth (constant MMCC and SGR) data. Finally, we propose several ways to improve GEMs, such as replacing the outdated Monod equation with the SCM (Synthetic Chemostat Model) that establishes the quantitative relationships between primary and secondary metabolism, growth rate and stress resistance, process kinetics, and cell composition. Full article

(This article belongs to the Special Issue Genome-Scale Modeling of Microorganisms in the Real World)

► Show Figures

Figure 1

18 pages, 1432 KiB

Open AccessReview

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

by Daniel Craig Zielinski, Arjun Patel and Bernhard O. Palsson

Microorganisms 2020, 8(12), 2050; https://doi.org/10.3390/microorganisms8122050 - 21 Dec 2020

Cited by 10 | Viewed by 4190

Abstract

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. Full article

(This article belongs to the Special Issue Genome-Scale Modeling of Microorganisms in the Real World)

► Show Figures

Figure 1

28 pages, 2099 KiB

Open AccessReview

Computational Modeling of the Human Microbiome

by Shomeek Chowdhury and Stephen S. Fong

Microorganisms 2020, 8(2), 197; https://doi.org/10.3390/microorganisms8020197 - 31 Jan 2020

Cited by 21 | Viewed by 8629

Abstract

The impact of microorganisms on human health has long been acknowledged and studied, but recent advances in research methodologies have enabled a new systems-level perspective on the collections of microorganisms associated with humans, the human microbiome. Large-scale collaborative efforts such as the NIH Human Microbiome Project have sought to kick-start research on the human microbiome by providing foundational information on microbial composition based upon specific sites across the human body. Here, we focus on the four main anatomical sites of the human microbiome: gut, oral, skin, and vaginal, and provide information on site-specific background, experimental data, and computational modeling. Each of the site-specific microbiomes has unique organisms and phenomena associated with them; there are also high-level commonalities. By providing an overview of different human microbiome sites, we hope to provide a perspective where detailed, site-specific research is needed to understand causal phenomena that impact human health, but there is equally a need for more generalized methodology improvements that would benefit all human microbiome research. Full article

(This article belongs to the Special Issue Genome-Scale Modeling of Microorganisms in the Real World)

► Show Figures