Special Issue "Metabolic Network Models"
Quicklinks
A special issue of Metabolites (ISSN 2218-1989).
Deadline for manuscript submissions: closed (31 August 2012)
Special Issue Editor
Guest Editor
Dr. Kyongbum Lee
Department of Chemical and Biological Engineering, Tufts University, Room 142, 4 Colby Street, Medford, MA 02155, USA
Website: http://engineering.tufts.edu/chbe/tme/default.asp
E-Mail: kyongbum.lee@tufts.edu
Interests: adipose tissue metabolism; liver drug transformation; dynamic models of metabolic networks; targeted metabolomics
Special Issue Information
Dear Colleagues,
Network models have been instrumental in advancing quantitative knowledge of cellular metabolism by characterizing the systems-level features and properties that arise from the biochemical interactions between metabolites, enzymes and regulatory molecules. Network models are now widely used in both basic and applied studies, ranging from investigations on the evolutionary origins of hierarchical modularity in metabolism to design of synthetic pathways for the overproduction of commercially useful molecules. By exploiting parallel advances in genomics, proteomics and bioinformatics, significant progress has been achieved in modeling metabolic networks, especially in the reconstruction and characterization of whole cell metabolic networks.
Many challenges remain, however, in developing dynamic models capable of predicting the response of cellular metabolism to environmental perturbations or genetic modifications. Given the size and complexity of metabolic networks, new modeling approaches are needed to incorporate existing and new knowledge on regulation, account for uncertainty, and systematically construct an identifiable model whose parameters can be robustly estimated from data. Therefore, this special issue of Metabolites will be dedicated for publishing current advances on dynamic metabolic network models, multi-scale and multi-resolution models, transcriptional and allosteric regulation, integration with signaling and other biochemical networks, parameter estimation from metabolomics data, and modeling of noise and uncertainty.
Prof. Kyongbum Lee
Guest Editor
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
- metabolic network
- dynamic model
- multi-scale
- multi-resolution
- model reduction
- transcriptional regulation
- allosteric regulation
- metabolomics data
- parameter estimation
- network flexibility
- robust design
Published Papers (10 papers)
|
Received: 30 December 2011; in revised form: 8 February 2012 / Accepted: 10 February 2012 / Published: 27 February 2012
Show/Hide Abstract
| Download PDF Full-text (360 KB) | Download XML Full-text
Abstract: Heat is one of the most fundamental and ancient environmental stresses, and response mechanisms are found in prokaryotes and shared among most eukaryotes. In the budding yeast Saccharomyces cerevisiae, the heat stress response involves coordinated changes at all biological levels, from gene expression to protein and metabolite abundances, and to temporary adjustments in physiology. Due to its integrative multi-level-multi-scale nature, heat adaptation constitutes a complex dynamic process, which has forced most experimental and modeling analyses in the past to focus on just one or a few of its aspects. Here we review the basic components of the heat stress response in yeast and outline what has been done, and what needs to be done, to merge the available information into computational structures that permit comprehensive diagnostics, interrogation, and interpretation. We illustrate the process in particular with the coordination of two metabolic responses, namely the dramatic accumulation of the protective disaccharide trehalose and the substantial change in the profile of sphingolipids, which in turn affect gene expression. The proposed methods primarily use differential equations in the canonical modeling framework of Biochemical Systems Theory (BST), which permits the relatively easy construction of coarse, initial models even in systems that are incompletely characterized.
|
|
Received: 30 January 2012; in revised form: 18 February 2012 / Accepted: 27 February 2012 / Published: 2 March 2012
Show/Hide Abstract
| Download PDF Full-text (371 KB) | Download XML Full-text
Abstract: Metabolism is crucial to cell growth and proliferation. Deficiency or alterations in metabolic functions are known to be involved in many human diseases. Therefore, understanding the human metabolic system is important for the study and treatment of complex diseases. Current reconstructions of the global human metabolic network provide a computational platform to integrate genome-scale information on metabolism. The platform enables a systematic study of the regulation and is applicable to a wide variety of cases, wherein one could rely on in silico perturbations to predict novel targets, interpret systemic effects, and identify alterations in the metabolic states to better understand the genotype-phenotype relationships. In this review, we describe the reconstruction of the human metabolic network, introduce the constraint based modeling approach to analyze metabolic networks, and discuss systems biology applications to study human physiology and pathology. We highlight the challenges and opportunities in network reconstruction and systems modeling of the human metabolic system.
|
|
Received: 15 February 2012; in revised form: 5 March 2012 / Accepted: 6 March 2012 / Published: 14 March 2012
Show/Hide Abstract
| Download PDF Full-text (247 KB) | Download XML Full-text
Abstract: The liver has many complex physiological functions, including lipid, protein and carbohydrate metabolism, as well as bile and urea production. It detoxifies toxic substances and medicinal products. It also plays a key role in the onset and maintenance of abnormal metabolic patterns associated with various disease states, such as burns, infections and major traumas. Liver cells have been commonly used in in vitro experiments to elucidate the toxic effects of drugs and metabolic changes caused by aberrant metabolic conditions, and to improve the functions of existing systems, such as bioartificial liver. More recently, isolated liver perfusion systems have been increasingly used to characterize intrinsic metabolic changes in the liver caused by various perturbations, including systemic injury, hepatotoxin exposure and warm ischemia. Metabolic engineering tools have been widely applied to these systems to identify metabolic flux distributions using metabolic flux analysis or flux balance analysis and to characterize the topology of the networks using metabolic pathway analysis. In this context, hepatic metabolic models, together with experimental methodologies where hepatocytes or perfused livers are mainly investigated, are described in detail in this review. The challenges and opportunities are also discussed extensively.
|
|
Received: 24 May 2012; in revised form: 26 June 2012 / Accepted: 9 July 2012 / Published: 12 July 2012
Show/Hide Abstract
| Download PDF Full-text (428 KB) | Download XML Full-text
Abstract: Formation and adaptation of metabolic networks has been a long-standing question in biology. With recent developments in biotechnology and bioinformatics, the understanding of metabolism is progressively becoming clearer from a network perspective. This review introduces the comprehensive metabolic world that has been revealed by a wide range of data analyses and theoretical studies; in particular, it illustrates the role of evolutionary events, such as gene duplication and horizontal gene transfer, and environmental factors, such as nutrient availability and growth conditions, in evolution of the metabolic network. Furthermore, the mathematical models for the formation and adaptation of metabolic networks have also been described, according to the current understanding from a perspective of metabolic networks. These recent findings are helpful in not only understanding the formation of metabolic networks and their adaptation, but also metabolic engineering.
|
|
Received: 1 August 2012; in revised form: 28 August 2012 / Accepted: 12 September 2012 / Published: 24 September 2012
Show/Hide Abstract
| Download PDF Full-text (5037 KB) | Download XML Full-text | 
Abstract: Metabolism has frequently been analyzed from a network perspective. A major question is how network properties correlate with biological features like growth rates, flux patterns and enzyme essentiality. Using methods from graph theory as well as established topological categories of metabolic systems, we analyze the essentiality of metabolic reactions depending on the growth medium and identify the topological footprint of these reactions. We find that the typical topological context of a medium-dependent essential reaction is systematically different from that of a globally essential reaction. In particular, we observe systematic differences in the distribution of medium-dependent essential reactions across three-node subgraphs (the network motif signature of medium-dependent essential reactions) compared to globally essential or globally redundant reactions. In this way, we provide evidence that the analysis of metabolic systems on the few-node subgraph scale is meaningful for explaining dynamic patterns. This topological characterization of medium-dependent essentiality provides a better understanding of the interplay between reaction deletions and environmental conditions.
 |
|
Received: 20 July 2012; in revised form: 7 September 2012 / Accepted: 24 September 2012 / Published: 8 October 2012
Show/Hide Abstract
| Download PDF Full-text (364 KB) | Download XML Full-text
Abstract: ATP delivery and its usage are achieved by cycling of respective intermediates through interconnected coupled reactions. At steady state, cycling between coupled reactions always occurs at zero resistance of the whole cycle without dissipation of free energy. The cross-bridge cycle can also be described by a system of coupled reactions: one energising reaction, which energises myosin heads by coupled ATP splitting, and one de-energising reaction, which transduces free energy from myosin heads to coupled actin movement. The whole cycle of myosin heads via cross-bridge formation and dissociation proceeds at zero resistance. Dissipation of free energy from coupled reactions occurs whenever the input potential overcomes the counteracting output potential. In addition, dissipation is produced by uncoupling. This is brought about by a load dependent shortening of the cross-bridge stroke to zero, which allows isometric force generation without mechanical power output. The occurrence of maximal efficiency is caused by uncoupling. Under coupled conditions, Hill’s equation (velocity as a function of load) is fulfilled. In addition, force and shortening velocity both depend on [Ca2+]. Muscular fatigue is triggered when ATP consumption overcomes ATP delivery. As a result, the substrate of the cycle, [MgATP2−], is reduced. This leads to a switch off of cycling and ATP consumption, so that a recovery of [ATP] is possible. In this way a potentially harmful, persistent low energy state of the cell can be avoided.
|
|
Received: 14 September 2012; in revised form: 2 November 2012 / Accepted: 5 November 2012 / Published: 12 November 2012
Show/Hide Abstract
| Download PDF Full-text (558 KB) | Download XML Full-text |
Abstract: Kinetic modeling of metabolic pathways has important applications in metabolic engineering, but significant challenges still remain. The difficulties faced vary from finding best-fit parameters in a highly multidimensional search space to incomplete parameter identifiability. To meet some of these challenges, an ensemble modeling method is developed for characterizing a subset of kinetic parameters that give statistically equivalent goodness-of-fit to time series concentration data. The method is based on the incremental identification approach, where the parameter estimation is done in a step-wise manner. Numerical efficacy is achieved by reducing the dimensionality of parameter space and using efficient random parameter exploration algorithms. The shift toward using model ensembles, instead of the traditional “best-fit” models, is necessary to directly account for model uncertainty during the application of such models. The performance of the ensemble modeling approach has been demonstrated in the modeling of a generic branched pathway and the trehalose pathway in Saccharomyces cerevisiae using generalized mass action (GMA) kinetics.
 |
|
Received: 29 August 2012; in revised form: 18 October 2012 / Accepted: 7 November 2012 / Published: 21 November 2012
Show/Hide Abstract
| Download PDF Full-text (1195 KB) | Download XML Full-text |
Abstract: TGFb signaling plays a major role in the reorganization of liver tissue upon injury and is an important driver of chronic liver disease. This is achieved by a deep impact on a cohort of cellular functions. To comprehensively assess the full range of affected metabolic functions, transcript changes of cultured mouse hepatocytes were analyzed with a novel method (ModeScore), which predicts the activity of metabolic functions by scoring transcript expression changes with 987 reference flux distributions, which yielded the following hypotheses. TGFb multiplies down-regulation of most metabolic functions occurring in culture stressed controls. This is especially pronounced for tyrosine degradation, urea synthesis, glucuronization capacity, and cholesterol synthesis. Ethanol degradation and creatine synthesis are down-regulated only in TGFb treated hepatocytes, but not in the control. Among the few TGFb dependently up-regulated functions, synthesis of various collagens is most pronounced. Further interesting findings include: down-regulation of glucose export is postponed by TGFb, TGFb up-regulates the synthesis capacity of ketone bodies only as an early response, TGFb suppresses the strong up-regulation of Vanin, and TGFb induces re-formation of ceramides and sphingomyelin.
 |
Eve Syrkin Wurtele, Joe Chappell, A. Daniel Jones, Mary Dawn Celiz, Nick Ransom, Manhoi Hur, Ludmila Rizshsky, Matthew Crispin, Philip Dixon, Jia Liu, Mark P.Widrlechner and Basil J. Nikolau
Received: 18 September 2012; in revised form: 30 October 2012 / Accepted: 31 October 2012 / Published: 21 November 2012
Show/Hide Abstract
| Download PDF Full-text (1583 KB) | Download XML Full-text
Abstract: Specialized compounds from photosynthetic organisms serve as rich resources for drug development. From aspirin to atropine, plant-derived natural products have had a profound impact on human health. Technological advances provide new opportunities to access these natural products in a metabolic context. Here, we describe a database and platform for storing, visualizing and statistically analyzing metabolomics data from fourteen medicinal plant species. The metabolomes and associated transcriptomes (RNAseq) for each plant species, gathered from up to twenty tissue/organ samples that have experienced varied growth conditions and developmental histories, were analyzed in parallel. Three case studies illustrate different ways that the data can be integrally used to generate testable hypotheses concerning the biochemistry, phylogeny and natural product diversity of medicinal plants. Deep metabolomics analysis of Camptotheca acuminata exemplifies how such data can be used to inform metabolic understanding of natural product chemical diversity and begin to formulate hypotheses about their biogenesis. Metabolomics data from Prunella vulgaris, a species that contains a wide range of antioxidant, antiviral, tumoricidal and anti-inflammatory constituents, provide a case study of obtaining biosystematic and developmental fingerprint information from metabolite accumulation data in a little studied species. Digitalis purpurea, well known as a source of cardiac glycosides, is used to illustrate how integrating metabolomics and transcriptomics data can lead to identification of candidate genes encoding biosynthetic enzymes in the cardiac glycoside pathway. Medicinal Plant Metabolomics Resource (MPM) [1] provides a framework for generating experimentally testable hypotheses about the metabolic networks that lead to the generation of specialized compounds, identifying genes that control their biosynthesis and establishing a basis for modeling metabolism in less studied species. The database is publicly available and can be used by researchers in medicine and plant biology.
|
|
Received: 8 October 2012; in revised form: 18 December 2012 / Accepted: 31 December 2012 / Published: 8 January 2013
Show/Hide Abstract
| Download PDF Full-text (904 KB) | Download XML Full-text |
Abstract: Plant diseases caused by pathogenic bacteria or fungi cause major economic damage every year and destroy crop yields that could feed millions of people. Only by a thorough understanding of the interaction between plants and phytopathogens can we hope to develop strategies to avoid or treat the outbreak of large-scale crop pests. Here, we studied the interaction of plant-pathogen pairs at the metabolic level. We selected five plant-pathogen pairs, for which both genomes were fully sequenced, and constructed the corresponding genome-scale metabolic networks. We present theoretical investigations of the metabolic interactions and quantify the positive and negative effects a network has on the other when combined into a single plant-pathogen pair network. Merged networks were examined for both the native plant-pathogen pairs as well as all other combinations. Our calculations indicate that the presence of the parasite metabolic networks reduce the ability of the plants to synthesize key biomass precursors. While the producibility of some precursors is reduced in all investigated pairs, others are only impaired in specific plant-pathogen pairs. Interestingly, we found that the specific effects on the host’s metabolism are largely dictated by the pathogen and not by the host plant. We provide graphical network maps for the native plant-pathogen pairs to allow for an interactive interrogation. By exemplifying a systematic reconstruction of metabolic network pairs for five pathogen-host pairs and by outlining various theoretical approaches to study the interaction of plants and phytopathogens on a biochemical level, we demonstrate the potential of investigating pathogen-host interactions from the perspective of interacting metabolic networks that will contribute to furthering our understanding of mechanisms underlying a successful invasion and subsequent establishment of a parasite into a plant host.
 |
Planned Papers
The below list represents only planned manuscripts. Some of these
manuscripts have not been received by the Editorial Office yet. Papers
submitted to MDPI journals are subject to peer-review.
Type of Paper: Article
Title: Development of Metabolic Indicators of Hypermetabolism: VLDL and Acetoacetate Are Highly Correlated to Severity of Burn Injury in Rats
Authors: Maria-Louisa Izamis1, Korkut Uygun1, Nripen S. Sharma2 Basak Uygun1, Martin L. Yarmush1,2 and Francois Berthiaume2
Affiliations: 1 Center for Engineering in Medicine, Massachusetts General Hospital, Harvard Medical School, and the Shriners Hospitals for Children, Boston, MA, USA; E-Mail: uygun.korkut@mgh.harvard.edu
2 Department of Biomedical Engineering, Rutgers University, Piscataway, NJ, USA
Abstract: Hypermetabolism is a significant sequela to severe trauma such as burns, as well as critical illnesses such as cancer. It persists in parallel to, or beyond, the original pathology for many months as an often-fatal comorbidity. Currently, diagnosis is based solely on clinical observations of increased energy expenditure, severe muscle wasting and progressive organ dysfunction. In a rat model of burn injury-induced hypermetabolism, we utilized data mining approaches to identify the metabolic variables that strongly correlate to the level of hypermetabolism. A clustering based algorithm to identify the minimum number of variables necessary was introduced, and applied to develop a regression model of burn injury level. As a result, a neural network model which employs VLDL and acetoacetate levels was demonstrated to predict the level of hypermetabolism with 76% accuracy in the rat model. The physiological importance of the identified variables in the context of hypermetabolism, and necessary steps in extension of this preliminary model to a clinically utilizable index of hypermetabolism are outlined.
Keywords: Hypermetabolism, metabonomics, flux analysis, index of hypermetabolism
Last update: 12 October 2012
