Special Issue "Metabolism and Systems Biology"

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A special issue of Metabolites (ISSN 2218-1989).

Deadline for manuscript submissions: closed (31 January 2015)

Special Issue Editors

Guest Editor
Dr. Christoph Kaleta

Research Group Theoretical Systems Biology, Friedrich-Schiller-Universität Jena, Raum 15N10, Leutragraben 1, D-07743 Jena, Germany
Website | E-Mail
Interests: analysis of metabolic and regulatory networks; system biology
Guest Editor
Dr. Ines Heiland

Arctic University of Norway, Department of Arctic and Marine Biology, Faculty of Biosciences, Fisheries and Economics, Naturfagbygget, Dramsvegen 201, 9037 Tromsø, Norway
Website | E-Mail
Interests: NAD-metabolism; tryptophan metabolism; circadian clocks; metabolic modelling; modelling of dynamic processes

Special Issue Information

Dear Colleagues,

Systems biological models of metabolism are widely used in biotechnology, pharmacy and medicine to optimise metabolite production, to predict drug targets or to understand metabolic discrepancies in diseases. Moreover, they have been shown to be key ingredients in the analysis of largescale data sets including transcriptomic, proteomic, fluxomic, metabolomic and genomic data sets. However, despite the success of these models, we are still very far from understanding the interaction between metabolism and signal transduction and models integrating both processes are still rare. This is in part due to the fact that different methods are used both in modelling as well as in experimental approaches targeting these pathways. We furthermore face the problem that data generation by far outpaces model construction efforts, which is especially true when it comes to kinetic models as the construction of these models is extremely time consuming. Thus, new approaches are required that combine the accuracy of kinetic models with semi automatic, less time consuming reconstruction methods as used for network based modeling approaches.

This special issue of Metabolites focuses on metabolic modelling approaches at the crossroad between kinetic and stiochiometric models as well as between signal transduction and metabolism. We furthermore welcome new approaches to improve the predictive capacity of genome scale metabolic models and approaches that integrate multiomics datasets.

Prof. Christoph Kaleta
Dr. Ines Heiland
Guest Editors

Submission

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. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as 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 refereed through a peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Metabolites is an international peer-reviewed Open Access quarterly 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 300 CHF (Swiss Francs). English correction and/or formatting fees of 250 CHF (Swiss Francs) will be charged in certain cases for those articles accepted for publication that require extensive additional formatting and/or English corrections.

Keywords

  • genome
  • scale metabolic models
  • reconstruction of metabolic networks
  • data integration into metabolic modeling
  • constraint
  • based modeling of metabolism
  • analysis of high
  • throughput datasets
  • transcriptomic data
  • proteomic data
  • fluxomic data
  • metabolomic data

Published Papers (19 papers)

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Research

Jump to: Review

Open AccessArticle Modeling and Simulation of Optimal Resource Management during the Diurnal Cycle in Emiliania huxleyi by Genome-Scale Reconstruction and an Extended Flux Balance Analysis Approach
Metabolites 2015, 5(4), 659-676; doi:10.3390/metabo5040659
Received: 13 June 2015 / Revised: 14 October 2015 / Accepted: 22 October 2015 / Published: 28 October 2015
Cited by 2 | PDF Full-text (1548 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The coccolithophorid unicellular alga Emiliania huxleyi is known to form large blooms, which have a strong effect on the marine carbon cycle. As a photosynthetic organism, it is subjected to a circadian rhythm due to the changing light conditions throughout the day. For
[...] Read more.
The coccolithophorid unicellular alga Emiliania huxleyi is known to form large blooms, which have a strong effect on the marine carbon cycle. As a photosynthetic organism, it is subjected to a circadian rhythm due to the changing light conditions throughout the day. For a better understanding of the metabolic processes under these periodically-changing environmental conditions, a genome-scale model based on a genome reconstruction of the E. huxleyi strain CCMP 1516 was created. It comprises 410 reactions and 363 metabolites. Biomass composition is variable based on the differentiation into functional biomass components and storage metabolites. The model is analyzed with a flux balance analysis approach called diurnal flux balance analysis (diuFBA) that was designed for organisms with a circadian rhythm. It allows storage metabolites to accumulate or be consumed over the diurnal cycle, while keeping the structure of a classical FBA problem. A feature of this approach is that the production and consumption of storage metabolites is not defined externally via the biomass composition, but the result of optimal resource management adapted to the diurnally-changing environmental conditions. The model in combination with this approach is able to simulate the variable biomass composition during the diurnal cycle in proximity to literature data. Full article
(This article belongs to the Special Issue Metabolism and Systems Biology)
Open AccessArticle Carotta: Revealing Hidden Confounder Markers in Metabolic Breath Profiles
Metabolites 2015, 5(2), 344-363; doi:10.3390/metabo5020344
Received: 13 March 2015 / Revised: 20 May 2015 / Accepted: 25 May 2015 / Published: 10 June 2015
Cited by 5 | PDF Full-text (8257 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Computational breath analysis is a growing research area aiming at identifying volatile organic compounds (VOCs) in human breath to assist medical diagnostics of the next generation. While inexpensive and non-invasive bioanalytical technologies for metabolite detection in exhaled air and bacterial/fungal vapor exist and
[...] Read more.
Computational breath analysis is a growing research area aiming at identifying volatile organic compounds (VOCs) in human breath to assist medical diagnostics of the next generation. While inexpensive and non-invasive bioanalytical technologies for metabolite detection in exhaled air and bacterial/fungal vapor exist and the first studies on the power of supervised machine learning methods for profiling of the resulting data were conducted, we lack methods to extract hidden data features emerging from confounding factors. Here, we present Carotta, a new cluster analysis framework dedicated to uncovering such hidden substructures by sophisticated unsupervised statistical learning methods. We study the power of transitivity clustering and hierarchical clustering to identify groups of VOCs with similar expression behavior over most patient breath samples and/or groups of patients with a similar VOC intensity pattern. This enables the discovery of dependencies between metabolites. On the one hand, this allows us to eliminate the effect of potential confounding factors hindering disease classification, such as smoking. On the other hand, we may also identify VOCs associated with disease subtypes or concomitant diseases. Carotta is an open source software with an intuitive graphical user interface promoting data handling, analysis and visualization. The back-end is designed to be modular, allowing for easy extensions with plugins in the future, such as new clustering methods and statistics. It does not require much prior knowledge or technical skills to operate. We demonstrate its power and applicability by means of one artificial dataset. We also apply Carotta exemplarily to a real-world example dataset on chronic obstructive pulmonary disease (COPD). While the artificial data are utilized as a proof of concept, we will demonstrate how Carotta finds candidate markers in our real dataset associated with confounders rather than the primary disease (COPD) and bronchial carcinoma (BC). Carotta is publicly available at http://carotta.compbio.sdu.dk [1]. Full article
(This article belongs to the Special Issue Metabolism and Systems Biology)
Open AccessArticle Footprints of Optimal Protein Assembly Strategies in the Operonic Structure of Prokaryotes
Metabolites 2015, 5(2), 252-269; doi:10.3390/metabo5020252
Received: 11 February 2015 / Revised: 27 March 2015 / Accepted: 24 April 2015 / Published: 28 April 2015
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Abstract
In this work, we investigate optimality principles behind synthesis strategies for protein complexes using a dynamic optimization approach. We show that the cellular capacity of protein synthesis has a strong influence on optimal synthesis strategies reaching from a simultaneous to a sequential synthesis
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In this work, we investigate optimality principles behind synthesis strategies for protein complexes using a dynamic optimization approach. We show that the cellular capacity of protein synthesis has a strong influence on optimal synthesis strategies reaching from a simultaneous to a sequential synthesis of the subunits of a protein complex. Sequential synthesis is preferred if protein synthesis is strongly limited, whereas a simultaneous synthesis is optimal in situations with a high protein synthesis capacity. We confirm the predictions of our optimization approach through the analysis of the operonic organization of protein complexes in several hundred prokaryotes. Thereby, we are able to show that cellular protein synthesis capacity is a driving force in the dissolution of operons comprising the subunits of a protein complex. Thus, we also provide a tested hypothesis explaining why the subunits of many prokaryotic protein complexes are distributed across several operons despite the presumably less precise co-regulation. Full article
(This article belongs to the Special Issue Metabolism and Systems Biology)
Open AccessArticle On Functional Module Detection in Metabolic Networks
Metabolites 2013, 3(3), 673-700; doi:10.3390/metabo3030673
Received: 31 May 2013 / Revised: 30 July 2013 / Accepted: 30 July 2013 / Published: 12 August 2013
Cited by 2 | PDF Full-text (579 KB) | HTML Full-text | XML Full-text
Abstract
Functional modules of metabolic networks are essential for understanding the metabolism of an organism as a whole. With the vast amount of experimental data and the construction of complex and large-scale, often genome-wide, models, the computer-aided identification of functional modules becomes more and
[...] Read more.
Functional modules of metabolic networks are essential for understanding the metabolism of an organism as a whole. With the vast amount of experimental data and the construction of complex and large-scale, often genome-wide, models, the computer-aided identification of functional modules becomes more and more important. Since steady states play a key role in biology, many methods have been developed in that context, for example, elementary flux modes, extreme pathways, transition invariants and place invariants. Metabolic networks can be studied also from the point of view of graph theory, and algorithms for graph decomposition have been applied for the identification of functional modules. A prominent and currently intensively discussed field of methods in graph theory addresses the Q-modularity. In this paper, we recall known concepts of module detection based on the steady-state assumption, focusing on transition-invariants (elementary modes) and their computation as minimal solutions of systems of Diophantine equations. We present the Fourier-Motzkin algorithm in detail. Afterwards, we introduce the Q-modularity as an example for a useful non-steady-state method and its application to metabolic networks. To illustrate and discuss the concepts of invariants and Q-modularity, we apply a part of the central carbon metabolism in potato tubers (Solanum tuberosum) as running example. The intention of the paper is to give a compact presentation of known steady-state concepts from a graph-theoretical viewpoint in the context of network decomposition and reduction and to introduce the application of Q-modularity to metabolic Petri net models. Full article
(This article belongs to the Special Issue Metabolism and Systems Biology)
Open AccessArticle Error Propagation Analysis for Quantitative Intracellular Metabolomics
Metabolites 2012, 2(4), 1012-1030; doi:10.3390/metabo2041012
Received: 14 August 2012 / Revised: 7 November 2012 / Accepted: 14 November 2012 / Published: 21 November 2012
Cited by 5 | PDF Full-text (1547 KB) | HTML Full-text | XML Full-text
Abstract
Model-based analyses have become an integral part of modern metabolic engineering and systems biology in order to gain knowledge about complex and not directly observable cellular processes. For quantitative analyses, not only experimental data, but also measurement errors, play a crucial role. The
[...] Read more.
Model-based analyses have become an integral part of modern metabolic engineering and systems biology in order to gain knowledge about complex and not directly observable cellular processes. For quantitative analyses, not only experimental data, but also measurement errors, play a crucial role. The total measurement error of any analytical protocol is the result of an accumulation of single errors introduced by several processing steps. Here, we present a framework for the quantification of intracellular metabolites, including error propagation during metabolome sample processing. Focusing on one specific protocol, we comprehensively investigate all currently known and accessible factors that ultimately impact the accuracy of intracellular metabolite concentration data. All intermediate steps are modeled, and their uncertainty with respect to the final concentration data is rigorously quantified. Finally, on the basis of a comprehensive metabolome dataset of Corynebacterium glutamicum, an integrated error propagation analysis for all parts of the model is conducted, and the most critical steps for intracellular metabolite quantification are detected. Full article
(This article belongs to the Special Issue Metabolism and Systems Biology)
Open AccessArticle Analysis and Design of Stimulus Response Curves of E. coli
Metabolites 2012, 2(4), 844-871; doi:10.3390/metabo2040844
Received: 17 October 2012 / Accepted: 29 October 2012 / Published: 12 November 2012
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Abstract
Metabolism and signalling are tightly coupled in bacteria. Combining several theoretical approaches, a core model is presented that describes transcriptional and allosteric control of glycolysis in Escherichia coli. Experimental data based on microarrays, signalling components and extracellular metabolites are used to estimate kinetic
[...] Read more.
Metabolism and signalling are tightly coupled in bacteria. Combining several theoretical approaches, a core model is presented that describes transcriptional and allosteric control of glycolysis in Escherichia coli. Experimental data based on microarrays, signalling components and extracellular metabolites are used to estimate kinetic parameters. A newly designed strain was used that adjusts the incoming glucose flux into the system and allows a kinetic analysis. Based on the results, prediction for intracelluar metabolite concentrations over a broad range of the growth rate could be performed and compared with data from literature. Full article
(This article belongs to the Special Issue Metabolism and Systems Biology)
Open AccessArticle Flux-P: Automating Metabolic Flux Analysis
Metabolites 2012, 2(4), 872-890; doi:10.3390/metabo2040872
Received: 4 September 2012 / Revised: 29 October 2012 / Accepted: 1 November 2012 / Published: 12 November 2012
Cited by 7 | PDF Full-text (989 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Quantitative knowledge of intracellular fluxes in metabolic networks is invaluable for inferring metabolic system behavior and the design principles of biological systems. However, intracellular reaction rates can not often be calculated directly but have to be estimated; for instance, via 13C-based metabolic
[...] Read more.
Quantitative knowledge of intracellular fluxes in metabolic networks is invaluable for inferring metabolic system behavior and the design principles of biological systems. However, intracellular reaction rates can not often be calculated directly but have to be estimated; for instance, via 13C-based metabolic flux analysis, a model-based interpretation of stable carbon isotope patterns in intermediates of metabolism. Existing software such as FiatFlux, OpenFLUX or 13CFLUX supports experts in this complex analysis, but requires several steps that have to be carried out manually, hence restricting the use of this software for data interpretation to a rather small number of experiments. In this paper, we present Flux-P as an approach to automate and standardize 13C-based metabolic flux analysis, using the Bio-jETI workflow framework. Exemplarily based on the FiatFlux software, it demonstrates how services can be created that carry out the different analysis steps autonomously and how these can subsequently be assembled into software workflows that perform automated, high-throughput intracellular flux analysis of high quality and reproducibility. Besides significant acceleration and standardization of the data analysis, the agile workflow-based realization supports flexible changes of the analysis workflows on the user level, making it easy to perform custom analyses. Full article
(This article belongs to the Special Issue Metabolism and Systems Biology)
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Open AccessArticle Determining Enzyme Kinetics for Systems Biology with Nuclear Magnetic Resonance Spectroscopy
Metabolites 2012, 2(4), 818-843; doi:10.3390/metabo2040818
Received: 20 August 2012 / Revised: 12 October 2012 / Accepted: 29 October 2012 / Published: 6 November 2012
Cited by 3 | PDF Full-text (3440 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Enzyme kinetics for systems biology should ideally yield information about the enzyme’s activity under in vivo conditions, including such reaction features as substrate cooperativity, reversibility and allostery, and be applicable to enzymatic reactions with multiple substrates. A large body of enzyme-kinetic data in
[...] Read more.
Enzyme kinetics for systems biology should ideally yield information about the enzyme’s activity under in vivo conditions, including such reaction features as substrate cooperativity, reversibility and allostery, and be applicable to enzymatic reactions with multiple substrates. A large body of enzyme-kinetic data in the literature is based on the uni-substrate Michaelis–Menten equation, which makes unnatural assumptions about enzymatic reactions (e.g., irreversibility), and its application in systems biology models is therefore limited. To overcome this limitation, we have utilised NMR time-course data in a combined theoretical and experimental approach to parameterize the generic reversible Hill equation, which is capable of describing enzymatic reactions in terms of all the properties mentioned above and has fewer parameters than detailed mechanistic kinetic equations; these parameters are moreover defined operationally. Traditionally, enzyme kinetic data have been obtained from initial-rate studies, often using assays coupled to NAD(P)H-producing or NAD(P)H-consuming reactions. However, these assays are very labour-intensive, especially for detailed characterisation of multi-substrate reactions. We here present a cost-effective and relatively rapid method for obtaining enzyme-kinetic parameters from metabolite time-course data generated using NMR spectroscopy. The method requires fewer runs than traditional initial-rate studies and yields more information per experiment, as whole time-courses are analyzed and used for parameter fitting. Additionally, this approach allows real-time simultaneous quantification of all metabolites present in the assay system (including products and allosteric modifiers), which demonstrates the superiority of NMR over traditional spectrophotometric coupled enzyme assays. The methodology presented is applied to the elucidation of kinetic parameters for two coupled glycolytic enzymes from Escherichia coli (phosphoglucose isomerase and phosphofructokinase). 31P-NMR time-course data were collected by incubating cell extracts with substrates, products and modifiers at different initial concentrations. NMR kinetic data were subsequently processed using a custom software module written in the Python programming language, and globally fitted to appropriately modified Hill equations. Full article
(This article belongs to the Special Issue Metabolism and Systems Biology)
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Open AccessArticle Characterization of the Interaction Between the Small Regulatory Peptide SgrT and the EIICBGlc of the Glucose-Phosphotransferase System of E. coli K-12
Metabolites 2012, 2(4), 756-774; doi:10.3390/metabo2040756
Received: 1 August 2012 / Revised: 29 August 2012 / Accepted: 10 October 2012 / Published: 16 October 2012
Cited by 4 | PDF Full-text (425 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Escherichia coli is a widely used microorganism in biotechnological processes. An obvious goal for current scientific and technical research in this field is the search for new tools to optimize productivity. Usually glucose is the preferred carbon source in biotechnological applications. In E.
[...] Read more.
Escherichia coli is a widely used microorganism in biotechnological processes. An obvious goal for current scientific and technical research in this field is the search for new tools to optimize productivity. Usually glucose is the preferred carbon source in biotechnological applications. In E. coli, glucose is taken up by the phosphoenolpyruvate-dependent glucose phosphotransferase system (PTS). The regulation of the ptsG gene for the glucose transporter is very complex and involves several regulatory proteins. Recently, a novel posttranscriptional regulation system has been identified which consists of a small regulatory RNA SgrS and a small regulatory polypeptide called SgrT. During the accumulation of glucose-6-phosphate or fructose-6-phosphate, SgrS is involved in downregulation of ptsG mRNA stability, whereas SgrT inhibits glucose transport activity by a yet unknown mechanism. The function of SgrS has been studied intensively. In contrast, the knowledge about the function of SgrT is still limited. Therefore, in this paper, we focused our interest on the regulation of glucose transport activity by SgrT. We identified the SgrT target sequence within the glucose transporter and characterized the interaction in great detail. Finally, we suggest a novel experimental approach to regulate artificially carbohydrate uptake in E. coli to minimize metabolic overflow in biotechnological applications. Full article
(This article belongs to the Special Issue Metabolism and Systems Biology)
Open AccessArticle Differentiating Hepatocellular Carcinoma from Hepatitis C Using Metabolite Profiling
Metabolites 2012, 2(4), 701-716; doi:10.3390/metabo2040701
Received: 1 August 2012 / Revised: 12 September 2012 / Accepted: 25 September 2012 / Published: 10 October 2012
Cited by 7 | PDF Full-text (455 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Hepatocellular carcinoma (HCC) accounts for most liver cancer cases worldwide. Contraction of the hepatitis C virus (HCV) is considered a major risk factor for liver cancer. In order to identify the risk of cancer, metabolic profiling of serum samples from patients with HCC
[...] Read more.
Hepatocellular carcinoma (HCC) accounts for most liver cancer cases worldwide. Contraction of the hepatitis C virus (HCV) is considered a major risk factor for liver cancer. In order to identify the risk of cancer, metabolic profiling of serum samples from patients with HCC (n=40) and HCV (n=22) was performed by 1H nuclear magnetic resonance spectroscopy. Multivariate statistical analysis showed a distinct separation of the two patient cohorts, indicating a distinct metabolic difference between HCC and HCV patient groups based on signals from lipids and other individual metabolites. Univariate analysis showed that three metabolites (choline, valine and creatinine) were significantly altered in HCC. A PLS-DA model based on these three metabolites showed a sensitivity of 80%, specificity of 71% and an area under the receiver operating curve of 0.83, outperforming the clinical marker alpha-fetoprotein (AFP). The robustness of the model was tested using Monte-Carlo cross validation (MCCV). This study showed that metabolite profiling could provide an alternative approach for HCC screening in HCV patients, many of whom have high risk for developing liver cancer. Full article
(This article belongs to the Special Issue Metabolism and Systems Biology)
Open AccessArticle Construction of a Genome-Scale Kinetic Model of Mycobacterium Tuberculosis Using Generic Rate Equations
Metabolites 2012, 2(3), 382-397; doi:10.3390/metabo2030382
Received: 29 March 2012 / Revised: 7 June 2012 / Accepted: 25 June 2012 / Published: 3 July 2012
Cited by 1 | PDF Full-text (500 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The study of biological systems at the genome scale helps us understand fundamental biological processes that govern the activity of living organisms and regulate their interactions with the environment. Genome-scale metabolic models are usually analysed using constraint-based methods, since detailed rate equations and
[...] Read more.
The study of biological systems at the genome scale helps us understand fundamental biological processes that govern the activity of living organisms and regulate their interactions with the environment. Genome-scale metabolic models are usually analysed using constraint-based methods, since detailed rate equations and kinetic parameters are often missing. However, constraint-based analysis is limited in capturing the dynamics of cellular processes. In this paper, we present an approach to build a genome-scale kinetic model of Mycobacterium tuberculosis metabolism using generic rate equations. M. tuberculosis causes tuberculosis which remains one of the largest killer infectious diseases. Using a genetic algorithm, we estimated kinetic parameters for a genome-scale metabolic model of M. tuberculosis based on flux distributions derived from Flux Balance Analysis. Our results show that an excellent agreement with flux values is obtained under several growth conditions, although kinetic parameters may vary in different conditions. Parameter variability analysis indicates that a high degree of redundancy remains present in model parameters, which suggests that the integration of other types of high-throughput datasets will enable the development of better constrained models accounting for a variety of in vivo phenotypes. Full article
(This article belongs to the Special Issue Metabolism and Systems Biology)
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Review

Jump to: Research

Open AccessReview Metabolism at Evolutionary Optimal States
Metabolites 2015, 5(2), 311-343; doi:10.3390/metabo5020311
Received: 3 March 2015 / Revised: 20 May 2015 / Accepted: 25 May 2015 / Published: 2 June 2015
PDF Full-text (1707 KB) | HTML Full-text | XML Full-text
Abstract
Metabolism is generally required for cellular maintenance and for the generation of offspring under conditions that support growth. The rates, yields (efficiencies), adaptation time and robustness of metabolism are therefore key determinants of cellular fitness. For biotechnological applications and our understanding of the
[...] Read more.
Metabolism is generally required for cellular maintenance and for the generation of offspring under conditions that support growth. The rates, yields (efficiencies), adaptation time and robustness of metabolism are therefore key determinants of cellular fitness. For biotechnological applications and our understanding of the evolution of metabolism, it is necessary to figure out how the functional system properties of metabolism can be optimized, via adjustments of the kinetics and expression of enzymes, and by rewiring metabolism. The trade-offs that can occur during such optimizations then indicate fundamental limits to evolutionary innovations and bioengineering. In this paper, we review several theoretical and experimental findings about mechanisms for metabolic optimization. Full article
(This article belongs to the Special Issue Metabolism and Systems Biology)
Open AccessReview Mathematical Modelling of Metabolic Regulation in Aging
Metabolites 2015, 5(2), 232-251; doi:10.3390/metabo5020232
Received: 3 November 2014 / Revised: 24 March 2015 / Accepted: 25 March 2015 / Published: 27 April 2015
Cited by 4 | PDF Full-text (695 KB) | HTML Full-text | XML Full-text
Abstract
The underlying cellular mechanisms that characterize aging are complex and multifaceted. However, it is emerging that aging could be regulated by two distinct metabolic hubs. These hubs are the pathway defined by the mammalian target of rapamycin (mTOR) and that defined by the
[...] Read more.
The underlying cellular mechanisms that characterize aging are complex and multifaceted. However, it is emerging that aging could be regulated by two distinct metabolic hubs. These hubs are the pathway defined by the mammalian target of rapamycin (mTOR) and that defined by the NAD+-dependent deacetylase enzyme, SIRT1. Recent experimental evidence suggests that there is crosstalk between these two important pathways; however, the mechanisms underpinning their interaction(s) remains poorly understood. In this review, we propose using computational modelling in tandem with experimentation to delineate the mechanism(s). We briefly discuss the main modelling frameworks that could be used to disentangle this relationship and present a reduced reaction pathway that could be modelled. We conclude by outlining the limitations of computational modelling and by discussing opportunities for future progress in this area. Full article
(This article belongs to the Special Issue Metabolism and Systems Biology)
Open AccessReview Metabolic Adaptation and Protein Complexes in Prokaryotes
Metabolites 2012, 2(4), 940-958; doi:10.3390/metabo2040940
Received: 20 October 2012 / Revised: 10 November 2012 / Accepted: 12 November 2012 / Published: 16 November 2012
Cited by 2 | PDF Full-text (741 KB) | HTML Full-text | XML Full-text
Abstract
Protein complexes are classified and have been charted in several large-scale screening studies in prokaryotes. These complexes are organized in a factory-like fashion to optimize protein production and metabolism. Central components are conserved between different prokaryotes; major complexes involve carbohydrate, amino acid, fatty
[...] Read more.
Protein complexes are classified and have been charted in several large-scale screening studies in prokaryotes. These complexes are organized in a factory-like fashion to optimize protein production and metabolism. Central components are conserved between different prokaryotes; major complexes involve carbohydrate, amino acid, fatty acid and nucleotide metabolism. Metabolic adaptation changes protein complexes according to environmental conditions. Protein modification depends on specific modifying enzymes. Proteins such as trigger enzymes display condition-dependent adaptation to different functions by participating in several complexes. Several bacterial pathogens adapt rapidly to intracellular survival with concomitant changes in protein complexes in central metabolism and optimize utilization of their favorite available nutrient source. Regulation optimizes protein costs. Master regulators lead to up- and downregulation in specific subnetworks and all involved complexes. Long protein half-life and low level expression detaches protein levels from gene expression levels. However, under optimal growth conditions, metabolite fluxes through central carbohydrate pathways correlate well with gene expression. In a system-wide view, major metabolic changes lead to rapid adaptation of complexes and feedback or feedforward regulation. Finally, prokaryotic enzyme complexes are involved in crowding and substrate channeling. This depends on detailed structural interactions and is verified for specific effects by experiments and simulations. Full article
(This article belongs to the Special Issue Metabolism and Systems Biology)
Open AccessReview Computational Methods for Metabolomic Data Analysis of Ion Mobility Spectrometry Data—Reviewing the State of the Art
Metabolites 2012, 2(4), 733-755; doi:10.3390/metabo2040733
Received: 8 August 2012 / Revised: 24 September 2012 / Accepted: 25 September 2012 / Published: 16 October 2012
Cited by 8 | PDF Full-text (2822 KB) | HTML Full-text | XML Full-text
Abstract
Ion mobility spectrometry combined with multi-capillary columns (MCC/IMS) is a well known technology for detecting volatile organic compounds (VOCs). We may utilize MCC/IMS for scanning human exhaled air, bacterial colonies or cell lines, for example. Thereby we gain information about the human health
[...] Read more.
Ion mobility spectrometry combined with multi-capillary columns (MCC/IMS) is a well known technology for detecting volatile organic compounds (VOCs). We may utilize MCC/IMS for scanning human exhaled air, bacterial colonies or cell lines, for example. Thereby we gain information about the human health status or infection threats. We may further study the metabolic response of living cells to external perturbations. The instrument is comparably cheap, robust and easy to use in every day practice. However, the potential of the MCC/IMS methodology depends on the successful application of computational approaches for analyzing the huge amount of emerging data sets. Here, we will review the state of the art and highlight existing challenges. First, we address methods for raw data handling, data storage and visualization. Afterwards we will introduce de-noising, peak picking and other pre-processing approaches. We will discuss statistical methods for analyzing correlations between peaks and diseases or medical treatment. Finally, we study up-to-date machine learning techniques for identifying robust biomarker molecules that allow classifying patients into healthy and diseased groups. We conclude that MCC/IMS coupled with sophisticated computational methods has the potential to successfully address a broad range of biomedical questions. While we can solve most of the data pre-processing steps satisfactorily, some computational challenges with statistical learning and model validation remain. Full article
(This article belongs to the Special Issue Metabolism and Systems Biology)
Open AccessReview What mRNA Abundances Can Tell us about Metabolism
Metabolites 2012, 2(3), 614-631; doi:10.3390/metabo2030614
Received: 1 August 2012 / Revised: 24 August 2012 / Accepted: 4 September 2012 / Published: 12 September 2012
Cited by 10 | PDF Full-text (165 KB) | HTML Full-text | XML Full-text
Abstract
Inferring decreased or increased metabolic functions from transcript profiles is at first sight a bold and speculative attempt because of the functional layers in between: proteins, enzymatic activities, and reaction fluxes. However, the growing interest in this field can easily be explained by
[...] Read more.
Inferring decreased or increased metabolic functions from transcript profiles is at first sight a bold and speculative attempt because of the functional layers in between: proteins, enzymatic activities, and reaction fluxes. However, the growing interest in this field can easily be explained by two facts: the high quality of genome-scale metabolic network reconstructions and the highly developed technology to obtain genome-covering RNA profiles. Here, an overview of important algorithmic approaches is given by means of criteria by which published procedures can be classified. The frontiers of the methods are sketched and critical voices are being heard. Finally, an outlook for the prospects of the field is given. Full article
(This article belongs to the Special Issue Metabolism and Systems Biology)
Open AccessReview Minimal Cut Sets and the Use of Failure Modes in Metabolic Networks
Metabolites 2012, 2(3), 567-595; doi:10.3390/metabo2030567
Received: 12 July 2012 / Revised: 25 August 2012 / Accepted: 29 August 2012 / Published: 11 September 2012
Cited by 1 | PDF Full-text (417 KB) | HTML Full-text | XML Full-text
Abstract
A minimal cut set is a minimal set of reactions whose inactivation would guarantee a failure in a certain network function or functions. Minimal cut sets (MCSs) were initially developed from the metabolic pathway analysis method (MPA) of elementary modes (EMs); they provide
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A minimal cut set is a minimal set of reactions whose inactivation would guarantee a failure in a certain network function or functions. Minimal cut sets (MCSs) were initially developed from the metabolic pathway analysis method (MPA) of elementary modes (EMs); they provide a way of identifying target genes for eliminating a certain objective function from a holistic perspective that takes into account the structure of the whole metabolic network. The concept of MCSs is fairly new and still being explored and developed; the initial concept has developed into a generalized form and its similarity to other network characterizations are discussed. MCSs can be used in conjunction with other constraints-based methods to get a better understanding of the capability of metabolic networks and the interrelationship between metabolites and enzymes/genes. The concept could play an important role in systems biology by contributing to fields such as metabolic and genetic engineering where it could assist in finding ways of producing industrially relevant compounds from renewable resources, not only for economical, but also for sustainability, reasons. Full article
(This article belongs to the Special Issue Metabolism and Systems Biology)
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Open AccessReview Mathematical Modeling of Plant Metabolism―From Reconstruction to Prediction
Metabolites 2012, 2(3), 553-566; doi:10.3390/metabo2030553
Received: 7 July 2012 / Revised: 22 August 2012 / Accepted: 28 August 2012 / Published: 6 September 2012
Cited by 6 | PDF Full-text (231 KB) | HTML Full-text | XML Full-text
Abstract
Due to their sessile lifestyle, plants are exposed to a large set of environmental cues. In order to cope with changes in environmental conditions a multitude of complex strategies to regulate metabolism has evolved. The complexity is mainly attributed to interlaced regulatory circuits
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Due to their sessile lifestyle, plants are exposed to a large set of environmental cues. In order to cope with changes in environmental conditions a multitude of complex strategies to regulate metabolism has evolved. The complexity is mainly attributed to interlaced regulatory circuits between genes, proteins and metabolites and a high degree of cellular compartmentalization. The genetic model plant Arabidopsis thaliana was intensely studied to characterize adaptive traits to a changing environment. The availability of genetically distinct natural populations has made it an attractive system to study plant-environment interactions. The impact on metabolism caused by changing environmental conditions can be estimated by mathematical approaches and deepens the understanding of complex biological systems. In combination with experimental high-throughput technologies this provides a promising platform to develop in silico models which are not only able to reproduce but also to predict metabolic phenotypes and to allow for the interpretation of plant physiological mechanisms leading to successful adaptation to a changing environment. Here, we provide an overview of mathematical approaches to analyze plant metabolism, with experimental procedures being used to validate their output, and we discuss them in the context of establishing a comprehensive understanding of plant-environment interactions. Full article
(This article belongs to the Special Issue Metabolism and Systems Biology)
Open AccessReview Optimality Principles in the Regulation of Metabolic Networks
Metabolites 2012, 2(3), 529-552; doi:10.3390/metabo2030529
Received: 20 July 2012 / Revised: 15 August 2012 / Accepted: 17 August 2012 / Published: 29 August 2012
Cited by 5 | PDF Full-text (2889 KB) | HTML Full-text | XML Full-text
Abstract
One of the challenging tasks in systems biology is to understand how molecular networks give rise to emergent functionality and whether universal design principles apply to molecular networks. To achieve this, the biophysical, evolutionary and physiological constraints that act on those networks need
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One of the challenging tasks in systems biology is to understand how molecular networks give rise to emergent functionality and whether universal design principles apply to molecular networks. To achieve this, the biophysical, evolutionary and physiological constraints that act on those networks need to be identified in addition to the characterisation of the molecular components and interactions. Then, the cellular “task” of the network—its function—should be identified. A network contributes to organismal fitness through its function. The premise is that the same functions are often implemented in different organisms by the same type of network; hence, the concept of design principles. In biology, due to the strong forces of selective pressure and natural selection, network functions can often be understood as the outcome of fitness optimisation. The hypothesis of fitness optimisation to understand the design of a network has proven to be a powerful strategy. Here, we outline the use of several optimisation principles applied to biological networks, with an emphasis on metabolic regulatory networks. We discuss the different objective functions and constraints that are considered and the kind of understanding that they provide. Full article
(This article belongs to the Special Issue Metabolism and Systems Biology)

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