Special Issue "Applying Systems Biotechnology Tools to Study Cell Metabolism"

A special issue of Bioengineering (ISSN 2306-5354).

Deadline for manuscript submissions: closed (31 October 2017).

Special Issue Editor

Dr. Xueyang Feng

Guest Editor
Department of Biological Systems Engineering, Virginia Polytechnic Institute and State University, USA
Interests: multi-omics; metabolic flux analysis; genome editing; metabolic engineering; synthetic biology; systems biology

Special Issue Information

Dear Colleagues,

Cell metabolism is a complex system composed of thousands of cell components, including genes, proteins, and metabolites. In the past two decades, systems biotechnologies, such as multi-omics analysis, have seen increasing applications and successes in uncovering novel metabolic pathways, discovering regulatory network of cell metabolism, and predicting cell performance. Instead of focusing on a few biological targets, systems biotechnologies study the cell metabolism as a whole and provide holistic views of the entire biological system. With recent advances of analytical and computational approaches, we envision that numerous novel discoveries could be provided and revolutionize our understanding of cell metabolism.
The current Special Issue emphasizes development and application of state-of-the-art systems biology technologies to decode cell metabolism. It will also feature the industrial applications of systems biotechnologies for improving cell performance.
We look forward to receiving your contributions to this exciting Special Issue.

Prof. Xueyang Feng, Ph.D.
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. Bioengineering 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 1000 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.

Keywords

  • Omics
  • Metabolic flux analysis
  • Genome-scale modeling
  • Signaling
  • Computational biology
  • Pathway analysis
  • Analytical biochemistry
  • Macromolecular modeling

Published Papers (2 papers)

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Research

Open AccessArticle
Assessing and Resolving Model Misspecifications in Metabolic Flux Analysis
Bioengineering 2017, 4(2), 48; https://doi.org/10.3390/bioengineering4020048 - 24 May 2017
Cited by 1
Abstract
Metabolic flux analysis (MFA) is an indispensable tool in metabolic engineering. The simplest variant of MFA relies on an overdetermined stoichiometric model of the cell’s metabolism under the pseudo-steady state assumption to evaluate the intracellular flux distribution. Despite its long history, the issue [...] Read more.
Metabolic flux analysis (MFA) is an indispensable tool in metabolic engineering. The simplest variant of MFA relies on an overdetermined stoichiometric model of the cell’s metabolism under the pseudo-steady state assumption to evaluate the intracellular flux distribution. Despite its long history, the issue of model error in overdetermined MFA, particularly misspecifications of the stoichiometric matrix, has not received much attention. We evaluated the performance of statistical tests from linear least square regressions, namely Ramsey’s Regression Equation Specification Error Test (RESET), the F-test, and the Lagrange multiplier test, in detecting model misspecifications in the overdetermined MFA, particularly missing reactions. We further proposed an iterative procedure using the F-test to correct such an issue. Using Chinese hamster ovary and random metabolic networks, we demonstrated that: (1) a statistically significant regression does not guarantee high accuracy of the flux estimates; (2) the removal of a reaction with a low flux magnitude can cause disproportionately large biases in the flux estimates; (3) the F-test could efficiently detect missing reactions; and (4) the proposed iterative procedure could robustly resolve the omission of reactions. Our work demonstrated that statistical analysis and tests could be used to systematically assess, detect, and resolve model misspecifications in the overdetermined MFA. Full article
(This article belongs to the Special Issue Applying Systems Biotechnology Tools to Study Cell Metabolism)
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Open AccessArticle
Macroscopic Dynamic Modeling of Sequential Batch Cultures of Hybridoma Cells: An Experimental Validation
Bioengineering 2017, 4(1), 17; https://doi.org/10.3390/bioengineering4010017 - 23 Feb 2017
Cited by 5
Abstract
Hybridoma cells are commonly grown for the production of monoclonal antibodies (MAb). For monitoring and control purposes of the bioreactors, dynamic models of the cultures are required. However these models are difficult to infer from the usually limited amount of available experimental data [...] Read more.
Hybridoma cells are commonly grown for the production of monoclonal antibodies (MAb). For monitoring and control purposes of the bioreactors, dynamic models of the cultures are required. However these models are difficult to infer from the usually limited amount of available experimental data and do not focus on target protein production optimization. This paper explores an experimental case study where hybridoma cells are grown in a sequential batch reactor. The simplest macroscopic reaction scheme translating the data is first derived using a maximum likelihood principal component analysis. Subsequently, nonlinear least-squares estimation is used to determine the kinetic laws. The resulting dynamic model reproduces quite satisfactorily the experimental data, as evidenced in direct and cross-validation tests. Furthermore, model predictions can also be used to predict optimal medium renewal time and composition. Full article
(This article belongs to the Special Issue Applying Systems Biotechnology Tools to Study Cell Metabolism)
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