Christian Jungreuthmayer

Austrian Centre of Industrial Biotechnology (Austria) — Austria

Biochemistry, Genetics and Molecular Biology · Life Sciences

20h-index1.6kcitations67works0.02yr avg
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Research Topics

Microbial Metabolic Engineering and Bioproduction(25), Biofuel production and bioconversion(16), Gene Regulatory Network Analysis(13), Enzyme Catalysis and Immobilization(6), Microfluidic and Capillary Electrophoresis Applications(5)

Publications67 total

Combinatorial in Vitro and in Silico Approach To Describe Shear-Force Dependent Uptake of Nanoparticles in Microfluidic Vascular Models
Analytical Chemistry·2018· 22 citedOpen Access
Designing Optimized Production Hosts by Metabolic Modeling
Methods in molecular biology·2017· 1 cited
From elementary flux modes to elementary flux vectors: Metabolic pathway analysis with arbitrary linear flux constraints
PLoS Computational Biology·2017· 75 citedOpen Access
Optimal knockout strategies in genome-scale metabolic networks using particle swarm optimization
BMC Bioinformatics·2017· 16 citedOpen Access
Microfluidic Migration and Wound Healing Assay Based on Mechanically Induced Injuries of Defined and Highly Reproducible Areas
Analytical Chemistry·2017· 67 cited
Toward Genome‐Scale Metabolic Pathway Analysis
Industrial Biotechnology·2016· 7 cited
Comparison of EFM calculation with and without taking into account gene regulatory information.
Figshare·2016Open Access
Comparison of the number of intermediate EFMs as a function of the iteration step for simulations with gene regulatory information (rule GR1, GR2, GR3, and GR4) and without gene regulatory information.
Figshare·2016Open Access
Comparison of runs with (green) and without (red) resorting the reaction order for a case with four rules (GR1, GR2, GR3, and GR4).
Figshare·2016Open Access
Number of modes as a function of all possible combinations of the four iteration phase rules.
Figshare·2016Open Access
Runtime speedup (ratio of execution time without resorting to execution time with resorting reactions) as a function of used gene rules.
Figshare·2016Open Access
Kernel matrix <i>K</i> of the extended stoichiometric matrix shown in S2 Table.
Figshare·2016Open Access
Initial mode matrix <i>R</i> for EFM calculation.
Figshare·2016Open Access
Example network consisting of 12 metabolites (rectangles), 11 reactions (diamonds), and a gene regulatory network: <i>R</i>7<i>r</i> = NOT(<i>R</i>9).
Figshare·2016Open Access
Numerical representation of all EFMs of the example network shown in Fig 1 calculated with ordinary EFMtool (left) and regEFMtool (right) using the gene regulatory rule <i>R</i>7<i>r</i> = NOT(<i>R</i>9).
Figshare·2016Open Access
The four Boolean rules, <i>X</i> = NOT(1<i>R</i>_<i>GLCt</i>1), used by the introduced elimination algorithm to exclude biologically infeasible EFMs during the iteration phase.
Figshare·2016Open Access
Effect of re-arranging the order of reactions for the simple example shown in S7 Table because of a gene rule that involves reaction <i>R</i>1 and <i>R</i>5.
Figshare·2016Open Access
Key properties of reactions participating in iteration phase rules regarding the re-ordering approach.
Figshare·2016Open Access
Mode matrix <i>R</i> after the first iteration step (left) converting reaction <i>R2</i> from numerical to binary form and after the last iteration step (right) for an ordinary EFM analysis.
Figshare·2016Open Access
Trend analysis of performance parameters of pre-packed columns for protein chromatography over a time span of ten years
Journal of Chromatography A·2016· 15 citedOpen Access
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Frequent Co-authors

Jürgen Zanghellini(38), David E. Ruckerbauer(11), Michael Hanscho(10), Matthias P. Gerstl(10), Peter Ertl(7), Fergal J. O’Brien(5), Eyal David(5), Platzer Matthias(5), Amir A. Al‐Munajjed(4), Gerald M. Birnbaumer(4), Govind Nair(4), Michael J. Jaasma(3), Daniel J. Kelly(3), Steffen Klamt(3), Stefan C. Müller(3), Lukas Richter(3), Schuller Winfried-Paul(3), Claudia Kappen(3), Michael Salbaum J(3), Thomas Bäuml(2)