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SUMMARY:Johannes G. Rebelein (Max Planck Institute for Terrestrial Microbi
ology)
DTSTART:20251110T160000Z
DTEND:20251110T163000Z
DTSTAMP:20260314T085953Z
UID:MicroBiotechDBTL/1
DESCRIPTION:Title: Decoding and Taming Nitrogenases for CO2 Conversion\nby Johan
nes G. Rebelein (Max Planck Institute for Terrestrial Microbiology) as par
t of Seminar on Microbial Biotechnology: Developing the Conceptual Framewo
rk of the DBTL Cycle\n\n\nAbstract\nNitrogenases are the only known enzyme
s that catalyze the reduction of molecular nitrogen (N$_2$) to ammonia (NH
$_3$)\, and with this driving the global nitrogen cycle. Besides the conve
rsion of N$_2$\, we recently showed that nitrogenases convert carbon dioxi
de (CO$_2$) to carbon monoxide\, formate and hydrocarbons(1-5)\, suggestin
g CO$_2$ to be a competitor of N$_2$.\n\nWe have investigated the competin
g reduction of CO$_2$ and N$_2$ by the molybdenum (Mo)- and iron (Fe)-nitr
ogenase(5). We find the Fe-nitrogenase almost three-fold more efficient in
CO$_2$ reduction than the Mo-isoform. The same effects translate in vivo\
, where Rhodobacter capsulatus strains relying on the Fe-nitrogenase reduc
e CO$_2$ physiologically to formate and methane\, highlighting the potenti
al of Fe-nitrogenases for the biotechnological conversion of CO$_2$ into v
alue-added compounds.\n\nFurthermore\, we use structural approaches (cryo-
EM(6) and X-ray crystallography(7)) to obtain new insights of the nitrogen
ase mechanism to engineer nitrogenases towards CO$_2$ reduction. We were j
ust able to change the product profile towards hydrocarbons and increase t
he selectivity for the reduction of CO$_2$ in the presence of N$_2$.$\\\\$
\n\n1. J. G. Rebelein\, Y. Hu\, M. W. Ribbe\, Angew. Chem. Int. Ed. 5
3\, 11543-11546 (2014).$\\\\$\n2. J. G. Rebelein\, Y. Hu\, M. W. Ribb
e\, ChemBioChem 16\, 1993-1996 (2015).$\\\\$\n3. J. G. Rebelein\, M.
T. Stiebritz\, C. C. Lee\, Y. Hu\, Nat. Chem. Biol. 13\, 147-149 (2017).$\
\\\$\n4. N. N. Oehlmann\, J. G. Rebelein\, ChemBioChem 23\, e20210045
3 (2022).$\\\\$\n5. N. N. Oehlmann et al.\, Sci. Adv. 10\, eado7729 (
2024).$\\\\$\n6. F. V. Schmidt et al.\, Nat. Struct. Mol. Biol. 31\,
150-158 (2024).$\\\\$\n7. H. Addison et al.\, Chemistry 31\, e2025008
44 (2025).\n\nJohannes Rebelein is a leading researcher in the field of me
talloenzymes and nitrogenases. He is currently an independent Emmy Noether
Research Group Leader at the Max Planck Institute for Terrestrial Microbi
ology in Marburg\, Germany\, and a Junior Group Leader at the LOEWE Center
for Synthetic Microbiology at Philipps-University of Marburg.\n\nDr. Rebe
lein has published 24 peer-reviewed articles on metalloenzymes with a focu
s on the conversion of small carbon compounds by nitrogenase proteins and
the development of artificial metalloenzymes. With his expertise in bioche
mistry\, synthetic microbiology\, and enzyme engineering\, Dr. Rebelein is
committed to advancing our understanding of complex biological systems an
d developing new solutions for the conversion of greenhouse gases to bulk
chemicals.\n\nDr. Rebelein received his Ph.D. in Biological Sciences from
the University of California\, Irvine\, under the supervision of Prof. Mar
kus W. Ribbe. He also holds an M.Sc. in Biotechnology with Honors from TU
Braunschweig and a B.Sc. in Biotechnology from the same institution.\n\nDr
. Rebelein has received several prestigious awards and fellowships\, inclu
ding the European Molecular Biology Organization (EMBO) Young Investigator
award\, the VAAM (Association for General and Applied Microbiology Resear
ch Award.\n
LOCATION:https://researchseminars.org/talk/MicroBiotechDBTL/1/
END:VEVENT
BEGIN:VEVENT
SUMMARY:David Henriques (Institute of Marine Research IIM (CSIC))
DTSTART:20251208T160000Z
DTEND:20251208T163000Z
DTSTAMP:20260314T085953Z
UID:MicroBiotechDBTL/2
DESCRIPTION:Title: Learning from Broken Models: How Contrasting Equations with Multi
-Omic Data Reveals New Biological Insights\nby David Henriques (Instit
ute of Marine Research IIM (CSIC)) as part of Seminar on Microbial Biotech
nology: Developing the Conceptual Framework of the DBTL Cycle\n\n\nAbstrac
t\nIntegrating multi-omic datasets remains a major challenge in systems bi
ology\, because information is often split across metabolic\, regulatory\,
and physiological layers. Saccharomyces cerevisiae\, one of the most thor
oughly characterised eukaryotes\, offers an ideal framework to tackle this
problem thanks to its exceptionally rich metabolic\, signalling\, and gen
e-expression datasets. Yet\, even with this depth of knowledge\, building
a coherent picture of yeast physiology still leaves several mechanistic ga
ps open.\n\nMy recent work combines dynamic modelling\, genome-scale metab
olic models (GSMMs)\, proteomics\, and physiological and signalling measur
ements to explore these gaps in a more systematic way. Despite many years
of careful curation\, GSMMs still carry numerous implicit assumptions—of
ten inherited from earlier versions—that only become apparent when they
are written down explicitly and confronted with multi-omic data. In practi
ce\, this comparison often exposes contradictions that point to missing re
actions\, incorrect or overly rigid constraints\, or simply aspects of phy
siology that the model has no way of capturing.\n\nComparing a model (or h
ypothesis) with data is\, in principle\, one of the foundations of the sci
entific method. What makes systems biology different is the scale and comp
lexity on both sides: the models encode thousands of assumptions\, and the
biological systems they describe operate through nonlinear behaviours\, l
ayered regulation\, and strong context dependence. This means that even we
ll-established models can fail in surprising and informative ways when tes
ted across different omic layers.\n\nIn this talk\, I will show several ex
amples where the tension between equations and experimental measurements l
ed to insights that neither approach could have produced alone. The broade
r point is that models are useful not only for prediction\, but also as st
ructured ways of organising assumptions—assumptions that become scientif
ically productive when they break. This interplay between models and data
continues to sharpen our understanding of yeast physiology and\, ultimatel
y\, supports the development of more robust tools for bioengineering and b
ioprocess design.\n\nDavid Henriques is an emerging principal investigator
at the IIM-CSIC. He holds a PhD in Computational Systems Biology from the
University of Minho\, an MSc in Bioinformatics\, and a BSc in Electronics
and Informatics Engineering. His research path began at EMBL-EBI\, progre
ssed through the MSCA ITN Cellular Homeostasis programme\, and has include
d scientific stays and postdoctoral work in Edinburgh\, RWTH Aachen and IT
QB NOVA. His work has evolved from modelling cell signalling to the revers
e engineering of dynamical biological systems and scientific machine learn
ing\, leading to several methodological developments at the interface of m
achine learning and systems biology.\n\nDavid’s independent research act
ivity consolidated during his recent postdoctoral stage at IIM-CSIC\, wher
e he led the development of a multiscale modelling line combining genome-s
cale models with mechanistic descriptions of metabolism. He is now part of
the AcuaBiotec group\, building experimental capacity and developing a re
search programme focused on engineering microbes for marine biotechnologic
al applications\, supported by multi-scale modelling and mini-bioreactor s
ystems.\n
LOCATION:https://researchseminars.org/talk/MicroBiotechDBTL/2/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Postponed
DTSTART:20260112T160000Z
DTEND:20260112T163000Z
DTSTAMP:20260314T085953Z
UID:MicroBiotechDBTL/3
DESCRIPTION:by Postponed as part of Seminar on Microbial Biotechnology: De
veloping the Conceptual Framework of the DBTL Cycle\n\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/MicroBiotechDBTL/3/
END:VEVENT
BEGIN:VEVENT
SUMMARY:TBA
DTSTART:20260209T150000Z
DTEND:20260209T153000Z
DTSTAMP:20260314T085953Z
UID:MicroBiotechDBTL/4
DESCRIPTION:by TBA as part of Seminar on Microbial Biotechnology: Developi
ng the Conceptual Framework of the DBTL Cycle\n\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/MicroBiotechDBTL/4/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Axel von Kamp (Max Planck Institute for Dynamics of Complex Techni
cal Systems)
DTSTART:20260309T150000Z
DTEND:20260309T153000Z
DTSTAMP:20260314T085953Z
UID:MicroBiotechDBTL/5
DESCRIPTION:Title: CNApy: An integrated visual environment for metabolic modeling\nby Axel von Kamp (Max Planck Institute for Dynamics of Complex Technica
l Systems) as part of Seminar on Microbial Biotechnology: Developing the C
onceptual Framework of the DBTL Cycle\n\n\nAbstract\nCNApy [1] is an open-
source cross-platform desktop application written in Python\, which offers
a state-of-the-art graphical front-end for the intuitive analysis of meta
bolic networks with Constraint-Based Reconstruction and Analysis (COBRA) m
ethods. While the basic look-and-feel of CNApy is similar to the user inte
rface of our MATLAB toolbox CellNetAnalyzer [2]\, it provides various enha
nced features by using components of the powerful Qt library. CNApy suppor
ts a number of standard and advanced COBRA techniques and further function
alities can be easily embedded in its GUI facilitating its modular extensi
on.\nSince its first public release in 2021\, CNApy has been continuously
updated. Highlights include the integration of interactively editable Esch
er maps [3]\, GUI interfaces for the powerful metabolic engineering packag
e StrainDesign [4]\, as well as thermodynamic analyses such as TFBA [5] an
d OptMDFpathway [6]. CNApy is available at https://github.com/cnapy-org/CN
Apy.\n\nReferences:\n\n[1] Thiele\, von Kamp\, Bekiaris\, Schneider & Klam
t (2022). CNApy: a CellNetAnalyzer GUI in Python for analyzing and designi
ng metabolic networks. Bioinformatics\, 38(5)\, 1467-1469.\n\n[2] Klamt\,
Saez-Rodriguez\, & Gilles (2007). Structural and functional analysis of ce
llular networks with CellNetAnalyzer. BMC systems biology\, 1(1)\, 2.\n\n[
3] King\, Dräger\, Ebrahim\, Sonnenschein\, Lewis & Palsson (2015). Esche
r: a web application for building\, sharing\, and embedding data-rich visu
alizations of biological pathways. PLoS computational biology\, 11(8)\, e1
004321.\n\n[4] Schneider\, Bekiaris\, von Kamp & Klamt (2022). StrainDesig
n: a comprehensive Python package for computational design of metabolic ne
tworks. Bioinformatics\, 38(21)\, 4981-4983.\n\n[5] Soh & Hatzimanikatis (
2014). Constraining the flux space using thermodynamics and integration of
metabolomics data. In Metabolic flux analysis: methods and protocols (pp.
49-63). New York\, NY: Springer New York.\n\n[6] Hädicke\, von Kamp\, Ay
dogan & Klamt (2018). OptMDFpathway: Identification of metabolic pathways
with maximal thermodynamic driving force and its application for analyzing
the endogenous CO2 fixation potential of Escherichia coli. PLoS computati
onal biology\, 14(9)\, e1006492.\n
LOCATION:https://researchseminars.org/talk/MicroBiotechDBTL/5/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Jasper Koehorst
DTSTART:20260413T140000Z
DTEND:20260413T143000Z
DTSTAMP:20260314T085953Z
UID:MicroBiotechDBTL/6
DESCRIPTION:Title: TBC\nby Jasper Koehorst as part of Seminar on Microbial Biote
chnology: Developing the Conceptual Framework of the DBTL Cycle\n\nAbstrac
t: TBA\n
LOCATION:https://researchseminars.org/talk/MicroBiotechDBTL/6/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Ljubisa Miskovic (Swiss Federal Institute of Technology (EPFL))
DTSTART:20260511T140000Z
DTEND:20260511T143000Z
DTSTAMP:20260314T085953Z
UID:MicroBiotechDBTL/7
DESCRIPTION:Title: Rational strain design with minimal phenotype perturbation\nb
y Ljubisa Miskovic (Swiss Federal Institute of Technology (EPFL)) as part
of Seminar on Microbial Biotechnology: Developing the Conceptual Framework
of the DBTL Cycle\n\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/MicroBiotechDBTL/7/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Brett Metcalfe
DTSTART:20260608T140000Z
DTEND:20260608T143000Z
DTSTAMP:20260314T085953Z
UID:MicroBiotechDBTL/8
DESCRIPTION:Title: TBC\nby Brett Metcalfe as part of Seminar on Microbial Biotec
hnology: Developing the Conceptual Framework of the DBTL Cycle\n\nAbstract
: TBA\n
LOCATION:https://researchseminars.org/talk/MicroBiotechDBTL/8/
END:VEVENT
BEGIN:VEVENT
SUMMARY:TBA
DTSTART:20260713T140000Z
DTEND:20260713T143000Z
DTSTAMP:20260314T085953Z
UID:MicroBiotechDBTL/9
DESCRIPTION:by TBA as part of Seminar on Microbial Biotechnology: Developi
ng the Conceptual Framework of the DBTL Cycle\n\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/MicroBiotechDBTL/9/
END:VEVENT
BEGIN:VEVENT
SUMMARY:TBA
DTSTART:20260810T140000Z
DTEND:20260810T143000Z
DTSTAMP:20260314T085953Z
UID:MicroBiotechDBTL/10
DESCRIPTION:by TBA as part of Seminar on Microbial Biotechnology: Developi
ng the Conceptual Framework of the DBTL Cycle\n\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/MicroBiotechDBTL/10/
END:VEVENT
BEGIN:VEVENT
SUMMARY:TBA
DTSTART:20260914T140000Z
DTEND:20260914T143000Z
DTSTAMP:20260314T085953Z
UID:MicroBiotechDBTL/11
DESCRIPTION:by TBA as part of Seminar on Microbial Biotechnology: Developi
ng the Conceptual Framework of the DBTL Cycle\n\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/MicroBiotechDBTL/11/
END:VEVENT
BEGIN:VEVENT
SUMMARY:TBA
DTSTART:20261012T140000Z
DTEND:20261012T143000Z
DTSTAMP:20260314T085953Z
UID:MicroBiotechDBTL/12
DESCRIPTION:by TBA as part of Seminar on Microbial Biotechnology: Developi
ng the Conceptual Framework of the DBTL Cycle\n\nAbstract: TBA\n
LOCATION:https://researchseminars.org/talk/MicroBiotechDBTL/12/
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