Special Issue "Dynamic Approaches to Metabolic Modeling and Metabolic Engineering"

A special issue of Processes (ISSN 2227-9717).

Deadline for manuscript submissions: closed (30 June 2014)

Special Issue Editor

Guest Editor
Prof. Dr. Doraiswami Ramkrishna

Forney Hall of Chemical Engineering, 480 Stadium Mall Drive, Purdue University, West Lafayette, IN 47907, USA
Website | E-Mail
Fax: +1 765 494 0805
Interests: metabolic modeling; metabolic engineering; particulate processes; population balances

Special Issue Information

Dear Colleagues,

Processes (ISSN 2227-9717), an international open access journal on processes in chemistry, biochemistry, biology, and related engineering research fields, is published by MDPI online quarterly. For more information, please refer to Processes' Aims and Scope at: http://www.mdpi.com/journal/processes/about. This Special Issue of Processes is expected to gather contributions in the general area of Dynamic Approaches to Metabolic Modeling and Metabolic Engineering.

Overall, this Special Issue is dedicated to applying mathematical models towards the translation of fundamental theory to design, optimize and control engineering systems. Specifically, it aims to promote the use of dynamic modeling from metabolic networks to metabolic engineering, then further to enable the design of organisms to maximize productivity.

While successful case studies will be of special interest, we also welcome papers that deliberate on the methodology, irrespective of whether the stipulated engineering goals have been reached successfully.

Prof. Dr. Doraiswami Ramkrishna
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 papers will be 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. Processes 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 350 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.


  • metabolic networks
  • metabolic Engineering
  • genetic Engineering
  • mutation
  • gene knockout
  • gene insertion
  • product yield and productivity

Published Papers (1 paper)

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Open AccessArticle Dynamic Modeling of Cell-Free Biochemical Networks Using Effective Kinetic Models
Processes 2015, 3(1), 138-160; doi:10.3390/pr3010138
Received: 8 September 2014 / Revised: 16 February 2015 / Accepted: 17 February 2015 / Published: 3 March 2015
Cited by 2 | PDF Full-text (3563 KB) | HTML Full-text | XML Full-text
Cell-free systems offer many advantages for the study, manipulation and modeling of metabolism compared to in vivo processes. Many of the challenges confronting genome-scale kinetic modeling can potentially be overcome in a cell-free system. For example, there is no complex transcriptional regulation to
[...] Read more.
Cell-free systems offer many advantages for the study, manipulation and modeling of metabolism compared to in vivo processes. Many of the challenges confronting genome-scale kinetic modeling can potentially be overcome in a cell-free system. For example, there is no complex transcriptional regulation to consider, transient metabolic measurements are easier to obtain, and we no longer have to consider cell growth. Thus, cell-free operation holds several significant advantages for model development, identification and validation. Theoretically, genome-scale cell-free kinetic models may be possible for industrially important organisms, such as E. coli, if a simple, tractable framework for integrating allosteric regulation with enzyme kinetics can be formulated. Toward this unmet need, we present an effective biochemical network modeling framework for building dynamic cell-free metabolic models. The key innovation of our approach is the integration of simple effective rules encoding complex allosteric regulation with traditional kinetic pathway modeling. We tested our approach by modeling the time evolution of several hypothetical cell-free metabolic networks. We found that simple effective rules, when integrated with traditional enzyme kinetic expressions, captured complex allosteric patterns such as ultrasensitivity or non-competitive inhibition in the absence of mechanistic information. Second, when integrated into network models, these rules captured classic regulatory patterns such as product-induced feedback inhibition. Lastly, we showed, at least for the network architectures considered here, that we could simultaneously estimate kinetic parameters and allosteric connectivity from synthetic data starting from an unbiased collection of possible allosteric structures using particle swarm optimization. However, when starting with an initial population that was heavily enriched with incorrect structures, our particle swarm approach could converge to an incorrect structure. While only an initial proof-of-concept, the framework presented here could be an important first step toward genome-scale cell-free kinetic modeling of the biosynthetic capacity of industrially important organisms. Full article
(This article belongs to the Special Issue Dynamic Approaches to Metabolic Modeling and Metabolic Engineering)

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: Unstructured Modeling of a Synthetic Microbial Consortium for Consolidated Production of Ethanol
Authors: Timothy J. Hanly and Michael A. Henson
Affiliation: Department of Chemical Engineering, University of Massachusetts, Amherst, MA 01003-3110, USA; E-Mail: henson@ecs.umass.edu
Abstract: The conversion of lignocellulosic biomass to liquid fuels such as ethanol is required for the commercialization of second generation biofuels. Reducing operating costs by combining the saccharification and fermentation steps of this process into one reactor has long been a goal of biofuels research. A defined mixed culture of specialized microbes that exploits the native capabilities of each member species is a promising alternative to use an omnipotent, engineered microbe. We explored such a synthetic consortium that couples the high cellulolytic activity of the filamentous fungus Trichoderma reesei with the ability of the yeasts Saccharomyces cerevisiae and Pichia stipitis to ferment hexose and pentose sugars to ethanol. Consortium stability was demonstrated by culturing the three microbes on a mixture of cellulose and xylan. As a first step towards understanding and manipulating this consortium, we developed a simple dynamic model with unstructured descriptions of enzyme synthesis, cellulose and hemicellulose degradation, sugar uptake, cell growth, and ethanol production. The batch culture model contained 10 ordinary differential equations with parameters obtained from the literature and experiment to the extent possible. The dynamic model was used to predict initial concentration of each cell type that maximized ethanol productivity subject to a constraint on the total inoculum concentration. The simulated ratio of cellulose to hemicellulose in the feedstock was varied to determine the effects on the optimal inoculum and ethanol productivity. A sensitivity analysis of model parameters identified several promising experimental targets for improvement of ethanol production through metabolic engineering.

Type of Paper: Review
Mathematical Modeling of Microbial Community Dynamics: Theory and Application
Hyun-Seob Song, William R. Cannon, Alexander S. Beliaev, Allan E. Konopka
Biological Sciences Division, Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, WA 99352, USA; E-mail: hyunseob.song@pnnl.gov
Microorganisms in nature form diverse communities that undergo dynamics in structure due both to the interactions between members and in response to external environmental changes. As a complex adaptive system, microbial communities exhibit properties that are uncaptured by characterizing individual microbes in isolation. Predictive mathematical models can be useful in understanding the dynamics of microbial communities. In this article, we give an overview of different potential modeling frameworks and particularly highlight multiscale approaches. We also briefly discuss how they have been applied in a wide range of settings. Therefore, this review may help to choose the most suitable framework that meets the key needs of modeling a microbial community.

Type of Paper: Article
Title: Dynamic Modeling of Metabolic Pathways in Microbial Degradation of Coal to Methane
Authors: Chris Micale, Aditya Srihari, Matt Wiatrowski, Nathaniel Grande, Juan-Lucio Vega and Prasad Dhurjati
Affiliation: University of Delaware
Abstract: We are currently working on using microorganisms as "microbial miners" to convert coal below ground to methane. Coal that is at depths that cannot be mined by humans represents more than 90% of the global fossil fuel energy reserves according to a US DOE estimate. Our objective is to use a consortium of microorganisms to convert the coal below ground to methane. This makes coal a "clean energy" source as many of the environmental impacts of burning coal above ground are eliminated below ground and one obtains methane as a much cleaner fuel. The research challenge is in understanding the elaborate metabolic processes involved in breaking coal down to methane. We have examined the microorganisms involved in the metabolic breakdown of coal and also built dynamic mathematical models of the metabolic alternatives. The manuscripts will present the results of these research activities at Delaware.

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