Special Issue "Metabolic Network Models"

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

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.

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• metabolic network
• dynamic model
• multi-scale
• multi-resolution
• model reduction
• transcriptional regulation
• allosteric regulation
• metabolomics data
• parameter estimation
• network flexibility
• robust design

Feature Paper:

Type of Paper: Review
Title: Hepatic Metabolic Models: Computational and Experimental Aspects
Authors: Mehmet A. Orman ¹, John Mattick ¹, Ioannis P. Androulakis ^1,2, Francois Berthiaume ², Marianthi G. Ierapetritou ^1,*
1 Department of Chemical and Biochemical Engineering, Rutgers, the State University of New Jersey, Piscataway, NJ, 08854, USA
² Department of Biomedical Engineering, Rutgers, the State University of New Jersey, Piscataway, NJ, 08854, USA; E-Mail: marianth@soemail.rutgers.edu
Abstract: The liver regulating the metabolic activity of the body 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 vitro experiments to elucidate toxic effects of drugs and metabolic changes caused by the 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 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 profoundly.
Keywords: hepatocytes, perfused livers, metabolic flux analysis, flux balance analysis, metabolic pathway analysis.

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. Yarmush^1,2 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