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Metabolites
Special Issues
Metabolism and Systems Biology Volume 2
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Metabolism and Systems Biology Volume 2

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

Deadline for manuscript submissions: closed (30 June 2018) | Viewed by 12920

Special Issue Editors

Prof. Dr. Frank J. Bruggeman

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Guest Editor
Systems Bioinformatics, VU University, Amsterdam, The Netherlands
Interests: systems biology; computational biology; biophysics
Prof. Dr. Radhakrishnan Mahadevan

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Guest Editor
Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON M5S 3E5, Canada
Interests: systems biology; metabolic engineering; human metabolism; metabolic modeling
Prof. Dr. Steffen Waldherr

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Guest Editor
Department of Chemical Engineering, Bio- and Chemical Systems Technology, Reactor Engineering, and Safety, KU Leuven, B-3001 Leuven, Belgium
Interests: modelling of biological systems; robustness analysis of biological networks; modelling and analysis of heterogeneous cell populations; dynamic optimization of metabolic networks

Special Issue Information

Dear Colleagues,

The computational systems biology of cellular metabolism has grown into a rich field. Models are based on genome-scale stoichiometric descriptions of metabolic networks, but recent efforts are moving away from only modeling the metabolic network. Metabolism and expression (ME) models take, for instance, the synthesis reactions of all individual macromolecular components of the cell into account, such as of each mRNA and protein, leading to enormous models with much greater predictive accuracy. Other models consider reduced descriptions of metabolism and cellular growth to understand how particular constraints, such as limited biosynthetic machinery and protein-accessible volumes, influence cellular growth and how cells would be predicted to change their protein expression across conditions if they are maximizing their growth rate. Novel methods for optimal metabolic regulation are also being pioneered; in order to figure out how protein expression can be optimized in time, when particular cellular objective are optimized, such as total biomass formation within some time window or period of particular environmental conditions, such as circadian rhythm.

To help the biologists evaluate the specific characteristics of these modeling approaches, we think that it is helpful for the field to have a Special Issue of Metabolites dedicated to novel computational and theoretical methods in the systems biology of metabolism.

Topics that we deem relevant for this review collections are:

  1. Principles and illustrations of ME models;
  2. Physicochemical constraints and optimization of whole cell models;
  3. Optimality and metabolic regulation;
  4. Novel theoretical results related to metabolic regulation.

Prof. Dr. Frank J. Bruggeman
Prof. Dr. Steffen Waldherr
Prof. Dr. Radhakrishnan Mahadevan
Guest Editors

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. Metabolites 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.

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

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25 pages, 14836 KiB  
Article
Genetic Optimization Algorithm for Metabolic Engineering Revisited
by Tobias B. Alter, Lars M. Blank and Birgitta E. Ebert
Metabolites 2018, 8(2), 33; https://doi.org/10.3390/metabo8020033 - 16 May 2018
Cited by 14 | Viewed by 5663
Abstract
To date, several independent methods and algorithms exist for exploiting constraint-based stoichiometric models to find metabolic engineering strategies that optimize microbial production performance. Optimization procedures based on metaheuristics facilitate a straightforward adaption and expansion of engineering objectives, as well as fitness functions, while [...] Read more.
To date, several independent methods and algorithms exist for exploiting constraint-based stoichiometric models to find metabolic engineering strategies that optimize microbial production performance. Optimization procedures based on metaheuristics facilitate a straightforward adaption and expansion of engineering objectives, as well as fitness functions, while being particularly suited for solving problems of high complexity. With the increasing interest in multi-scale models and a need for solving advanced engineering problems, we strive to advance genetic algorithms, which stand out due to their intuitive optimization principles and the proven usefulness in this field of research. A drawback of genetic algorithms is that premature convergence to sub-optimal solutions easily occurs if the optimization parameters are not adapted to the specific problem. Here, we conducted comprehensive parameter sensitivity analyses to study their impact on finding optimal strain designs. We further demonstrate the capability of genetic algorithms to simultaneously handle (i) multiple, non-linear engineering objectives; (ii) the identification of gene target-sets according to logical gene-protein-reaction associations; (iii) minimization of the number of network perturbations; and (iv) the insertion of non-native reactions, while employing genome-scale metabolic models. This framework adds a level of sophistication in terms of strain design robustness, which is exemplarily tested on succinate overproduction in Escherichia coli. Full article
(This article belongs to the Special Issue Metabolism and Systems Biology Volume 2)
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848 KiB  
Article
A Protocol for Generating and Exchanging (Genome-Scale) Metabolic Resource Allocation Models
by Alexandra-M. Reimers, Henning Lindhorst and Steffen Waldherr
Metabolites 2017, 7(3), 47; https://doi.org/10.3390/metabo7030047 - 6 Sep 2017
Cited by 18 | Viewed by 6531
Abstract
In this article, we present a protocol for generating a complete (genome-scale) metabolic resource allocation model, as well as a proposal for how to represent such models in the systems biology markup language (SBML). Such models are used to investigate enzyme levels and [...] Read more.
In this article, we present a protocol for generating a complete (genome-scale) metabolic resource allocation model, as well as a proposal for how to represent such models in the systems biology markup language (SBML). Such models are used to investigate enzyme levels and achievable growth rates in large-scale metabolic networks. Although the idea of metabolic resource allocation studies has been present in the field of systems biology for some years, no guidelines for generating such a model have been published up to now. This paper presents step-by-step instructions for building a (dynamic) resource allocation model, starting with prerequisites such as a genome-scale metabolic reconstruction, through building protein and noncatalytic biomass synthesis reactions and assigning turnover rates for each reaction. In addition, we explain how one can use SBML level 3 in combination with the flux balance constraints and our resource allocation modeling annotation to represent such models. Full article
(This article belongs to the Special Issue Metabolism and Systems Biology Volume 2)
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