Literature DB >> 28499817

Use of CellNetAnalyzer in biotechnology and metabolic engineering.

Axel von Kamp1, Sven Thiele1, Oliver Hädicke1, Steffen Klamt2.   

Abstract

Mathematical models of the cellular metabolism have become an essential tool for the optimization of biotechnological processes. They help to obtain a systemic understanding of the metabolic processes in the used microorganisms and to find suitable genetic modifications maximizing the production performance. In particular, methods of stoichiometric and constraint-based modeling are frequently used in the context of metabolic and bioprocess engineering. Since metabolic networks can be complex and comprise hundreds or even thousands of metabolites and reactions, dedicated software tools are required for an efficient analysis. One such software suite is CellNetAnalyzer, a MATLAB package providing, among others, various methods for analyzing stoichiometric and constraint-based metabolic models. CellNetAnalyzer can be used via command-line based operations or via a graphical user interface with embedded network visualizations. Herein we will present key functionalities of CellNetAnalyzer for applications in biotechnology and metabolic engineering and thereby review constraint-based modeling techniques such as metabolic flux analysis, flux balance analysis, flux variability analysis, metabolic pathway analysis (elementary flux modes) and methods for computational strain design.
Copyright © 2017 The Author(s). Published by Elsevier B.V. All rights reserved.

Keywords:  Elementary modes; Flux balance analysis; Metabolic flux analysis; Metabolic networks; Minimal cut sets; Strain optimization

Mesh:

Year:  2017        PMID: 28499817     DOI: 10.1016/j.jbiotec.2017.05.001

Source DB:  PubMed          Journal:  J Biotechnol        ISSN: 0168-1656            Impact factor:   3.307


  23 in total

1.  Automatic construction of metabolic models with enzyme constraints.

Authors:  Pavlos Stephanos Bekiaris; Steffen Klamt
Journal:  BMC Bioinformatics       Date:  2020-01-14       Impact factor: 3.169

2.  SteadyCellPhenotype: A web-based tool for the modeling of biological networks with ternary logic.

Authors:  Adam C Knapp; Luis Sordo Vieira; Reinhard Laubenbacher; Julia Chifman
Journal:  Bioinformatics       Date:  2022-02-18       Impact factor: 6.937

3.  Analyzing and Resolving Infeasibility in Flux Balance Analysis of Metabolic Networks.

Authors:  Steffen Klamt; Axel von Kamp
Journal:  Metabolites       Date:  2022-06-23

4.  Elucidating Plant-Microbe-Environment Interactions Through Omics-Enabled Metabolic Modelling Using Synthetic Communities.

Authors:  Ashley E Beck; Manuel Kleiner; Anna-Katharina Garrell
Journal:  Front Plant Sci       Date:  2022-06-20       Impact factor: 6.627

5.  Understanding FBA Solutions under Multiple Nutrient Limitations.

Authors:  Eunice van Pelt-KleinJan; Daan H de Groot; Bas Teusink
Journal:  Metabolites       Date:  2021-04-21

6.  A mathematical framework for yield (vs. rate) optimization in constraint-based modeling and applications in metabolic engineering.

Authors:  Steffen Klamt; Stefan Müller; Georg Regensburger; Jürgen Zanghellini
Journal:  Metab Eng       Date:  2018-02-07       Impact factor: 9.783

7.  An extended and generalized framework for the calculation of metabolic intervention strategies based on minimal cut sets.

Authors:  Philipp Schneider; Axel von Kamp; Steffen Klamt
Journal:  PLoS Comput Biol       Date:  2020-07-27       Impact factor: 4.475

8.  MoVE identifies metabolic valves to switch between phenotypic states.

Authors:  Naveen Venayak; Axel von Kamp; Steffen Klamt; Radhakrishnan Mahadevan
Journal:  Nat Commun       Date:  2018-12-14       Impact factor: 14.919

9.  Mapping the Physiological Response of Oenococcus oeni to Ethanol Stress Using an Extended Genome-Scale Metabolic Model.

Authors:  Angela Contreras; Magdalena Ribbeck; Guillermo D Gutiérrez; Pablo M Cañon; Sebastián N Mendoza; Eduardo Agosin
Journal:  Front Microbiol       Date:  2018-03-01       Impact factor: 5.640

10.  OptMDFpathway: Identification of metabolic pathways with maximal thermodynamic driving force and its application for analyzing the endogenous CO2 fixation potential of Escherichia coli.

Authors:  Oliver Hädicke; Axel von Kamp; Timur Aydogan; Steffen Klamt
Journal:  PLoS Comput Biol       Date:  2018-09-24       Impact factor: 4.475

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.