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SUMMARY:Johannes G. Rebelein (Max Planck Institute for Terrestrial Microbi
ology)
DTSTART:20251110T160000Z
DTEND:20251110T163000Z
DTSTAMP:20260404T110657Z
UID:MicroBiotechDBTL/1
DESCRIPTION:Title: Decoding and Taming Nitrogenases for CO2 Conversion\nb
y Johannes G. Rebelein (Max Planck Institute for Terrestrial Microbiology)
as part of Seminar on Microbial Biotechnology: Developing the Conceptual
Framework of the DBTL Cycle\n\n\nAbstract\nNitrogenases are the only known
enzymes that catalyze the reduction of molecular nitrogen (N$_2$) to ammo
nia (NH$_3$)\, and with this driving the global nitrogen cycle. Besides th
e conversion of N$_2$\, we recently showed that nitrogenases convert carbo
n dioxide (CO$_2$) to carbon monoxide\, formate and hydrocarbons(1-5)\, su
ggesting CO$_2$ to be a competitor of N$_2$.\n\nWe have investigated the c
ompeting reduction of CO$_2$ and N$_2$ by the molybdenum (Mo)- and iron (F
e)-nitrogenase(5). We find the Fe-nitrogenase almost three-fold more effic
ient in CO$_2$ reduction than the Mo-isoform. The same effects translate i
n vivo\, where Rhodobacter capsulatus strains relying on the Fe-nitrogenas
e reduce CO$_2$ physiologically to formate and methane\, highlighting the
potential of Fe-nitrogenases for the biotechnological conversion of CO$_2$
into value-added compounds.\n\nFurthermore\, we use structural approaches
(cryo-EM(6) and X-ray crystallography(7)) to obtain new insights of the n
itrogenase mechanism to engineer nitrogenases towards CO$_2$ reduction. We
were just able to change the product profile towards hydrocarbons and inc
rease the 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. 53\, 11543-11546 (2014).$\\\\$\n2. J. G. Rebelein\, Y. Hu\, M.
W. Ribbe\, ChemBioChem 16\, 1993-1996 (2015).$\\\\$\n3. J. G. Rebelei
n\, M. T. Stiebritz\, C. C. Lee\, Y. Hu\, Nat. Chem. Biol. 13\, 147-149 (2
017).$\\\\$\n4. N. N. Oehlmann\, J. G. Rebelein\, ChemBioChem 23\, e2
02100453 (2022).$\\\\$\n5. N. N. Oehlmann et al.\, Sci. Adv. 10\, ead
o7729 (2024).$\\\\$\n6. F. V. Schmidt et al.\, Nat. Struct. Mol. Biol
. 31\, 150-158 (2024).$\\\\$\n7. H. Addison et al.\, Chemistry 31\, e
202500844 (2025).\n\nJohannes Rebelein is a leading researcher in the fiel
d of metalloenzymes and nitrogenases. He is currently an independent Emmy
Noether Research Group Leader at the Max Planck Institute for Terrestrial
Microbiology in Marburg\, Germany\, and a Junior Group Leader at the LOEWE
Center for Synthetic Microbiology at Philipps-University of Marburg.\n\nD
r. Rebelein has published 24 peer-reviewed articles on metalloenzymes with
a focus on the conversion of small carbon compounds by nitrogenase protei
ns and the development of artificial metalloenzymes. With his expertise in
biochemistry\, synthetic microbiology\, and enzyme engineering\, Dr. Rebe
lein is committed to advancing our understanding of complex biological sys
tems and developing new solutions for the conversion of greenhouse gases t
o bulk chemicals.\n\nDr. Rebelein received his Ph.D. in Biological Science
s from the University of California\, Irvine\, under the supervision of Pr
of. Markus W. Ribbe. He also holds an M.Sc. in Biotechnology with Honors f
rom TU Braunschweig and a B.Sc. in Biotechnology from the same institution
.\n\nDr. Rebelein has received several prestigious awards and fellowships\
, including the European Molecular Biology Organization (EMBO) Young Inves
tigator award\, the VAAM (Association for General and Applied Microbiology
Research Award.\n
LOCATION:https://stable.researchseminars.org/talk/MicroBiotechDBTL/1/
END:VEVENT
BEGIN:VEVENT
SUMMARY:David Henriques (Institute of Marine Research IIM (CSIC))
DTSTART:20251208T160000Z
DTEND:20251208T163000Z
DTSTAMP:20260404T110657Z
UID:MicroBiotechDBTL/2
DESCRIPTION:Title: Learning from Broken Models: How Contrasting Equations wit
h Multi-Omic Data Reveals New Biological Insights\nby David Henriques
(Institute of Marine Research IIM (CSIC)) as part of Seminar on Microbial
Biotechnology: Developing the Conceptual Framework of the DBTL Cycle\n\n\n
Abstract\nIntegrating multi-omic datasets remains a major challenge in sys
tems biology\, because information is often split across metabolic\, regul
atory\, and physiological layers. Saccharomyces cerevisiae\, one of the mo
st thoroughly characterised eukaryotes\, offers an ideal framework to tack
le this problem thanks to its exceptionally rich metabolic\, signalling\,
and gene-expression datasets. Yet\, even with this depth of knowledge\, bu
ilding a coherent picture of yeast physiology still leaves several mechani
stic gaps open.\n\nMy recent work combines dynamic modelling\, genome-scal
e metabolic models (GSMMs)\, proteomics\, and physiological and signalling
measurements to explore these gaps in a more systematic way. Despite many
years of careful curation\, GSMMs still carry numerous implicit assumptio
ns—often inherited from earlier versions—that only become apparent whe
n they are written down explicitly and confronted with multi-omic data. In
practice\, this comparison often exposes contradictions that point to mis
sing reactions\, incorrect or overly rigid constraints\, or simply aspects
of physiology that the model has no way of capturing.\n\nComparing a mode
l (or hypothesis) with data is\, in principle\, one of the foundations of
the scientific method. What makes systems biology different is the scale a
nd complexity on both sides: the models encode thousands of assumptions\,
and the biological systems they describe operate through nonlinear behavio
urs\, layered regulation\, and strong context dependence. This means that
even well-established models can fail in surprising and informative ways w
hen tested across different omic layers.\n\nIn this talk\, I will show sev
eral examples where the tension between equations and experimental measure
ments led to insights that neither approach could have produced alone. The
broader point is that models are useful not only for prediction\, but als
o as structured ways of organising assumptions—assumptions that become s
cientifically productive when they break. This interplay between models an
d data continues to sharpen our understanding of yeast physiology and\, ul
timately\, supports the development of more robust tools for bioengineerin
g and bioprocess design.\n\nDavid Henriques is an emerging principal inves
tigator at the IIM-CSIC. He holds a PhD in Computational Systems Biology f
rom the University of Minho\, an MSc in Bioinformatics\, and a BSc in Elec
tronics and Informatics Engineering. His research path began at EMBL-EBI\,
progressed through the MSCA ITN Cellular Homeostasis programme\, and has
included scientific stays and postdoctoral work in Edinburgh\, RWTH Aachen
and ITQB NOVA. His work has evolved from modelling cell signalling to the
reverse engineering of dynamical biological systems and scientific machin
e learning\, leading to several methodological developments at the interfa
ce of machine learning and systems biology.\n\nDavid’s independent resea
rch activity consolidated during his recent postdoctoral stage at IIM-CSIC
\, where he led the development of a multiscale modelling line combining g
enome-scale models with mechanistic descriptions of metabolism. He is now
part of the AcuaBiotec group\, building experimental capacity and developi
ng a research programme focused on engineering microbes for marine biotech
nological applications\, supported by multi-scale modelling and mini-biore
actor systems.\n
LOCATION:https://stable.researchseminars.org/talk/MicroBiotechDBTL/2/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Postponed
DTSTART:20260112T160000Z
DTEND:20260112T163000Z
DTSTAMP:20260404T110657Z
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://stable.researchseminars.org/talk/MicroBiotechDBTL/3/
END:VEVENT
BEGIN:VEVENT
SUMMARY:TBA
DTSTART:20260209T150000Z
DTEND:20260209T153000Z
DTSTAMP:20260404T110657Z
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://stable.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:20260404T110657Z
UID:MicroBiotechDBTL/5
DESCRIPTION:Title: CNApy: An integrated visual environment for metabolic mode
ling\nby Axel von Kamp (Max Planck Institute for Dynamics of Complex T
echnical Systems) as part of Seminar on Microbial Biotechnology: Developin
g the Conceptual Framework of the DBTL Cycle\n\n\nAbstract\nCNApy [1] is a
n open-source cross-platform desktop application written in Python\, which
offers a state-of-the-art graphical front-end for the intuitive analysis
of metabolic networks with Constraint-Based Reconstruction and Analysis (C
OBRA) methods. While the basic look-and-feel of CNApy is similar to the us
er interface of our MATLAB toolbox CellNetAnalyzer [2]\, it provides vario
us enhanced features by using components of the powerful Qt library. CNApy
supports a number of standard and advanced COBRA techniques and further f
unctionalities can be easily embedded in its GUI facilitating its modular
extension.\nSince its first public release in 2021\, CNApy has been contin
uously updated. Highlights include the integration of interactively editab
le Escher maps [3]\, GUI interfaces for the powerful metabolic engineering
package StrainDesign [4]\, as well as thermodynamic analyses such as TFBA
[5] and OptMDFpathway [6]. CNApy is available at https://github.com/cnapy
-org/CNApy.\n\nReferences:\n\n[1] Thiele\, von Kamp\, Bekiaris\, Schneider
& Klamt (2022). CNApy: a CellNetAnalyzer GUI in Python for analyzing and
designing metabolic networks. Bioinformatics\, 38(5)\, 1467-1469.\n\n[2] K
lamt\, Saez-Rodriguez\, & Gilles (2007). Structural and functional analysi
s of cellular networks with CellNetAnalyzer. BMC systems biology\, 1(1)\,
2.\n\n[3] King\, Dräger\, Ebrahim\, Sonnenschein\, Lewis & Palsson (2015)
. Escher: a web application for building\, sharing\, and embedding data-ri
ch visualizations of biological pathways. PLoS computational biology\, 11(
8)\, e1004321.\n\n[4] Schneider\, Bekiaris\, von Kamp & Klamt (2022). Stra
inDesign: a comprehensive Python package for computational design of metab
olic networks. Bioinformatics\, 38(21)\, 4981-4983.\n\n[5] Soh & Hatzimani
katis (2014). Constraining the flux space using thermodynamics and integra
tion of metabolomics data. In Metabolic flux analysis: methods and protoco
ls (pp. 49-63). New York\, NY: Springer New York.\n\n[6] Hädicke\, von Ka
mp\, Aydogan & Klamt (2018). OptMDFpathway: Identification of metabolic pa
thways with maximal thermodynamic driving force and its application for an
alyzing the endogenous CO2 fixation potential of Escherichia coli. PLoS co
mputational biology\, 14(9)\, e1006492.\n
LOCATION:https://stable.researchseminars.org/talk/MicroBiotechDBTL/5/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Jasper Koehorst
DTSTART:20260413T140000Z
DTEND:20260413T143000Z
DTSTAMP:20260404T110657Z
UID:MicroBiotechDBTL/6
DESCRIPTION:Title: TBC\nby Jasper Koehorst as part of Seminar on Microbia
l Biotechnology: Developing the Conceptual Framework of the DBTL Cycle\n\n
Abstract: TBA\n
LOCATION:https://stable.researchseminars.org/talk/MicroBiotechDBTL/6/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Ljubisa Miskovic (Swiss Federal Institute of Technology (EPFL))
DTSTART:20260511T140000Z
DTEND:20260511T143000Z
DTSTAMP:20260404T110657Z
UID:MicroBiotechDBTL/7
DESCRIPTION:Title: From Nonlinear Metabolic Dynamics to Experimentally Valida
ted Strain Design\nby Ljubisa Miskovic (Swiss Federal Institute of Tec
hnology (EPFL)) as part of Seminar on Microbial Biotechnology: Developing
the Conceptual Framework of the DBTL Cycle\n\n\nAbstract\nPredictive metab
olic engineering requires models that capture the nonlinear and time-depen
dent behavior of cellular metabolism\, particularly under genetic and envi
ronmental perturbations. While constraint-based approaches provide scalabl
e analyses\, they lack the ability to describe dynamic responses and regul
atory effects\, limiting their predictive power for strain design.\n\nHere
\, we present a kinetic-model-driven framework for rational metabolic engi
neering that integrates large-scale model construction\, uncertainty quant
ification\, and control-based design. Using ensembles of kinetic models co
nsistent with thermodynamic\, physiological\, and omics constraints\, we s
ystematically screen for models that reproduce experimentally observed dyn
amic behavior and exhibit robustness to perturbations. These models are th
en used in conjunction with Network Response Analysis to generate combinat
orial intervention strategies under phenotypic constraints.\n\nWe demonstr
ate this approach across multiple case studies\, including the design of s
trains for anthranilate and p-coumaric acid production. Nonlinear dynamic
simulations reveal that candidate designs must be evaluated beyond local a
pproximations to ensure robustness and feasibility. Experimental implement
ation of selected designs confirms the predictive capability of the framew
ork\, with the majority of proposed interventions leading to improved prod
uction while preserving growth.\n\nTogether\, these results establish kine
tic modeling as a practical and predictive platform for strain design\, en
abling the identification of robust engineering strategies that are consis
tent across uncertainty and validated in real biological systems.\n
LOCATION:https://stable.researchseminars.org/talk/MicroBiotechDBTL/7/
END:VEVENT
BEGIN:VEVENT
SUMMARY:Brett Metcalfe
DTSTART:20260608T140000Z
DTEND:20260608T143000Z
DTSTAMP:20260404T110657Z
UID:MicroBiotechDBTL/8
DESCRIPTION:Title: TBC\nby Brett Metcalfe as part of Seminar on Microbial
Biotechnology: Developing the Conceptual Framework of the DBTL Cycle\n\nA
bstract: TBA\n
LOCATION:https://stable.researchseminars.org/talk/MicroBiotechDBTL/8/
END:VEVENT
BEGIN:VEVENT
SUMMARY:TBA
DTSTART:20260713T140000Z
DTEND:20260713T143000Z
DTSTAMP:20260404T110657Z
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://stable.researchseminars.org/talk/MicroBiotechDBTL/9/
END:VEVENT
BEGIN:VEVENT
SUMMARY:TBA
DTSTART:20260810T140000Z
DTEND:20260810T143000Z
DTSTAMP:20260404T110657Z
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://stable.researchseminars.org/talk/MicroBiotechDBTL/10/
END:VEVENT
BEGIN:VEVENT
SUMMARY:TBA
DTSTART:20260914T140000Z
DTEND:20260914T143000Z
DTSTAMP:20260404T110657Z
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://stable.researchseminars.org/talk/MicroBiotechDBTL/11/
END:VEVENT
BEGIN:VEVENT
SUMMARY:TBA
DTSTART:20261012T140000Z
DTEND:20261012T143000Z
DTSTAMP:20260404T110657Z
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://stable.researchseminars.org/talk/MicroBiotechDBTL/12/
END:VEVENT
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