Publications
2025
Smith,
E. N., van Aalst, M., Weber, A. P. M., Ebenhöh,
O., & Heinemann, M. (2025). Alternatives to
photorespiration: A system-level analysis reveals mechanisms of
enhanced plant productivity. Science
Advances, 11(13), Article eadt9287. https://doi.org/10.1126/sciadv.adt9287
Satanowski,
A., Marchal, D. G., Perret, A., Petit, J.-L., Bouzon, M.,
Döring, V., Dubois, I., He, H., Smith, E. N.,
Pellouin, V., Petri, H. M., Rainaldi, V., Nattermann, M., Burgener,
S., Paczia, N., Zarzycki, J., Heinemann, M., Bar-Even,
A., & Erb, T. J. (2025). Design and implementation of
aerobic and ambient CO2-reduction as an entry-point for enhanced
carbon fixation. Nature
Communications, 16(1), Article 3134. https://doi.org/10.1038/s41467-025-57549-4
Li,
X., de Assis Souza, R., & Heinemann, M.
(2025). The rate of glucose metabolism sets the cell
morphology across yeast strains and species.
Current Biology, 35(4), 788-798. https://doi.org/10.1016/j.cub.2024.12.039
van
Oppen, Y. B., & Milias-Argeitis, A. (2025).
Gradient matching accelerates mixed-effects inference for
biochemical networks. Bioinformatics (Oxford,
England), Article btaf154. Advance online publication.
https://doi.org/10.1093/bioinformatics/btaf154
2024
Losa,
J., & Heinemann, M. (2024).
Contribution of different macromolecules to the diffusion of
a 40 nm particle in Escherichia coli. Biophysical
Journal, 123(10), 1211-1221. https://doi.org/10.1016/j.bpj.2024.03.040
Terpstra,
H. M., Gómez-Sánchez, R.,
Veldsink, A. C., Otto, T. A., Veenhoff,
L. M., & Heinemann, M. (2024).
PunctaFinder: An algorithm for automated spot detection in
fluorescence microscopy images. Molecular Biology
of the Cell, 35(12), Article 35:mr9. https://doi.org/10.1091/mbc.E24-06-0254
Freese,
T., Elzinga, N., Heinemann,
M., Lerch, M. M., & Feringa, B.
L. (2024). The relevance of sustainable laboratory
practices. RSC Sustainability,
2(5), 1300-1336. Article d4su00056k. https://doi.org/10.1039/D4SU00056K
Liu,
Y., Liu, C., Tang, S., Xiao, H., Wu, X., Peng, Y., Wang, X., Que,
L., Di, Z., Zhou, D., & Heinemann, M. (2024).
The "weaken-fill-repair" model for cell budding: Linking cell
wall biosynthesis with mechanics.
Iscience, 27(10), Article 110981. https://doi.org/10.1016/j.isci.2024.110981
Galenkamp,
N. S., Zernia, S., Van Oppen, Y.
B., van den Noort, M., Argeitis, A.
M., & Maglia, G. (2024). Allostery
can convert binding free energies into concerted domain motions in
enzymes. Nature Communications,
15(1), Article 10109. https://doi.org/10.1038/s41467-024-54421-9
Milias
Argeitis, A., & Kruitbosch, H. (2024).
Transfer Learning from Synthetic Data for Cell Segmentation
and Tracking. In J. Zhou, H. Peng, & M. Rapsomaniki
(Eds.), Frontiers in Bioimage Informatics Methodology
(Series on Language Processing, Pattern Recognition, and
Intelligent Systems; Vol. 8). World Scientific Publishing.
Bergsma,
T., Steen, A., Kamenz, J.
L., Otto, T., Gallardo, P.,
& Veenhoff, L. M. (2025). Imaging-Based
Quantitative Assessment of Biomolecular Condensates in vitro and in
Cells. The Journal of Biological
Chemistry, 301(2), Article 108130. https://doi.org/10.1016/j.jbc.2024.108130
Boland,
A., & Kamenz, J. (2024). Racing through C.
elegans mitosis using cyclin B3. The Journal of
Cell Biology, 223(11), Article e202410007. https://doi.org/10.1083/jcb.202410007
2023
Smith,
E. N., van Aalst, M., Tosens, T., Niinemets, Ü., Stich,
B., Morosinotto, T., Alboresi, A., Erb, T., Gómez-Coronado, P.
A., Tolleter, D., Finazzi, G., Curien, G., Heinemann,
M., Ebenhöh, O., Hibberd, J. M., Schlüter, U.,
Sun, T., & Weber, A. P. M. (2023). Improving
photosynthetic efficiency toward food security: Strategies,
advances, and perspectives. Molecular
Plant, 16(10), 1547-1563. https://doi.org/10.1016/j.molp.2023.08.017
Li,
X., & Heinemann, M. (2023).
Quantifying intracellular glucose levels when yeast is grown
in glucose media. Scientific Reports,
13(1), Article 17066. https://doi.org/10.1038/s41598-023-43602-z
Takhaveev,
V., Özsezen, S., Smith, E.
N., Zylstra, A., Chaillet, M. L., Chen,
H., Papagiannakis, A., Milias-Argeitis,
A., & Heinemann, M. (2023). Temporal
segregation of biosynthetic processes is responsible for metabolic
oscillations during the budding yeast cell cycle.
Nature Metabolism, 5(2), 294-313. https://doi.org/10.1038/s42255-023-00741-x
Prins,
F. L., Tomanin, D., Kamenz, J.,
& Azzopardi, G. (2023). Biometric Recognition of
African Clawed Frogs. In N. Tsapatsoulis, E. Kyriacou, A.
Lanitis, Z. Theodosiou, M. Pattichis, C. Pattichis, C. Kyrkou,
& A. Panayides (Eds.), Computer Analysis of Images and
Patterns - 20th International Conference, CAIP 2023, Proceedings:
20th International Conference, CAIP 2023 Limassol, Cyprus,
September 25–28, 2023 Proceedings, Part II (pp.
151-161). (Lecture Notes in Computer Science (including subseries
Lecture Notes in Artificial Intelligence and Lecture Notes in
Bioinformatics); Vol. 14185 LNCS). Springer. https://doi.org/10.1007/978-3-031-44240-7_15
2022
Zylstra,
A., & Heinemann, M. (2022). Metabolic
dynamics during the cell cycle. Current Opinion in
Systems Biology, 30, Article 100415. https://doi.org/10.1016/j.coisb.2022.100415
Van
den Bergh, B., Schramke, H., Michiels, J. E., Kimkes, T. E. P.,
Radzikowski, J. L., Schimpf, J., Vedelaar, S. R.,
Burschel, S., Dewachter, L., Lončar, N., Schmidt,
A., Meijer, T., Fauvart, M., Friedrich, T., Michiels, J.,
& Heinemann, M. (2022). Mutations in respiratory
complex I promote antibiotic persistence through alterations in
intracellular acidity and protein synthesis.
Nature Communications, 13(1),
Article 546. https://doi.org/10.1038/s41467-022-28141-x
Losa,
J., Leupold, S., Alonso-Martinez, D., Vainikka,
P., Thallmair, S., Tych, K.
M., Marrink, S. J., & Heinemann,
M. (2022). Perspective: A stirring role for metabolism
in cells. Molecular Systems Biology,
18(4), Article e10822. https://doi.org/10.15252/msb.202110822
Litsios,
A., Goswami, P., Terpstra, H. M., Coffin, C.,
Vuillemenot, L.-A., Rovetta, M., Ghazal,
G., Guerra, P., Buczak, K., Schmidt, A., Tollis, S.,
Tyers, M., Royer, C. A., Milias-Argeitis, A.,
& Heinemann, M. (2022). The timing of Start is
determined primarily by increased synthesis of the Cln3 activator
rather than dilution of the Whi5 inhibitor.
Molecular Biology of the Cell,
33(5), Article rp2. https://doi.org/10.1091/mbc.E21-07-0349
Novarina,
D., Koutsoumpa, A., &
Milias-Argeitis, A. (2022). A user-friendly and
streamlined protocol for CRISPR/Cas9 genome editing in budding
yeast. STAR protocols, 3(2),
Article 101358. https://doi.org/10.1016/j.xpro.2022.101358
Kurdyaeva,
T., & Milias-Argeitis, A. (2022).
Propagation of initial condition uncertainty for linear
dynamical systems: Beyond the Gaussian assumption. In
2022 European Control Conference, ECC 2022 (pp.
1391-1396). Institute of Electrical and Electronics Engineers Inc..
https://doi.org/10.23919/ECC55457.2022.9838074
Guerra,
P., Vuillemenot, L.-A. P. E., van Oppen,
Y. B., Been, M., & Milias-Argeitis, A.
(2022). TORC1 and PKA activity towards ribosome biogenesis
oscillates in synchrony with the budding yeast cell cycle.
Journal of Cell Science, 135(18),
Article 260378. https://doi.org/10.1242/jcs.260378
2021
Sellner,
B., Prakapaitė, R., van Berkum, M., Heinemann,
M., Harms, A., & Jenal, U. (2021). A New Sugar for
an Old Phage: a c-di-GMP-Dependent Polysaccharide Pathway
Sensitizes Escherichia coli for Bacteriophage Infection.
mBio, 12(6), Article e03246-21. https://doi.org/10.1128/mbio.03246-21
Vedelaar,
S. R., Radzikowski, J. L., & Heinemann, M.
(2021). A Robust Method for Generating, Quantifying, and
Testing Large Numbers of Escherichia coli Persisters.
Methods in molecular biology (Clifton, N.J.),
2357, 41-62. https://doi.org/10.1007/978-1-0716-1621-5_3
Ortega,
A. D., Takhaveev, V., Vedelaar, S. R.,
Long, Y., Mestre-Farràs, N., Incarnato, D.,
Ersoy, F., Olsen, L. F., Mayer, G., & Heinemann,
M. (2021). A synthetic RNA-based biosensor for
fructose-1,6-bisphosphate that reports glycolytic flux.
Cell Chemical Biology, 28(11),
1554-1568.e8. Article j.chembiol.2021.04.006. https://doi.org/10.1016/j.chembiol.2021.04.006
Alseekh,
S., Aharoni, A., Brotman, Y., Contrepois, K., D'Auria, J., Ewald,
J., C Ewald, J., Fraser, P. D., Giavalisco, P., Hall, R. D.,
Heinemann, M., Link, H., Luo, J., Neumann, S., Nielsen, J.,
Perez de Souza, L., Saito, K., Sauer, U., Schroeder, F. C., ...
Fernie, A. R. (2021). Mass spectrometry-based metabolomics: A
guide for annotation, quantification and best reporting
practices. Nature Methods,
18(7), 747-756. https://doi.org/10.1038/s41592-021-01197-1
Yang,
X., Heinemann, M., Howard, J., Huber, G., Iyer-Biswas,
S., Le Treut, G., Lynch, M., Montooth, K. L., Needleman, D. J.,
Pigolotti, S., Rodenfels, J., Ronceray, P., Shankar, S., Tavassoly,
I., Thutupalli, S., Titov, D. V., Wang, J., & Foster, P. J.
(2021). Physical bioenergetics: Energy fluxes, budgets, and
constraints in cells. Proceedings of the National
Academy of Sciences of the United States of America,
118(26), Article e2026786118. https://doi.org/10.1073/pnas.2026786118
Kruitbosch,
H., Mzayek, Y., Omlor, S., Guerra,
P., & Milias-Argeitis, A. (2022). A
convolutional neural network for segmentation of yeast cells
without manual training annotations.
Bioinformatics (Oxford, England),
38(5), 1427-1433. Article btab835. https://doi.org/10.1093/bioinformatics/btab835
Guerra,
P., Vuillemenot, L.-A., Rae, B.,
Ladyhina, V., & Milias-Argeitis, A. (2022).
Systematic In Vivo Characterization of Fluorescent Protein
Maturation in Budding Yeast. ACS Synthetic
Biology, 11(3), 1129-1141. Article
acssynbio.1c00387. https://doi.org/10.1021/acssynbio.1c00387
Kurdyaeva,
T., & Milias Argeitis, A. (2021).
Moment-based uncertainty propagation for deterministic
biochemical network models with rational reaction rates. In
Proceedings of the European Control Conference 2021 (pp.
878-883). EUCA. https://doi.org/10.23919/ECC54610.2021.9654833
Kurdyaeva,
T., & Milias-Argeitis, A. (2021).
Uncertainty propagation for deterministic models of
biochemical networks using moment equations and the extended Kalman
filter. Journal of the Royal Society
Interface, 18(181), Article 20210331. https://doi.org/10.1098/rsif.2021.0331
Novarina,
D., Guerra, P., & Milias-Argeitis,
A. (2021). Vacuolar Localization via the N-terminal
Domain of Sch9 is Required for TORC1-dependent Phosphorylation and
Downstream Signal Transduction. Journal of
Molecular Biology, 433(24), Article 167326. https://doi.org/10.1016/j.jmb.2021.167326
Kamenz,
J., Qiao, R., Yang, Q., & Ferrell, J. E. (2021).
Real-Time Monitoring of APC /C-Mediated Substrate Degradation
Using Xenopus laevis Egg Extracts. In Methods in
Molecular Biology (pp. 29-38). (Methods in Molecular Biology;
Vol. 2329). Humana Press. https://doi.org/10.1007/978-1-0716-1538-6_3
2020
Heinemann,
M., Basan, M., & Sauer, U. (2020). Implications of
initial physiological conditions for bacterial adaptation to
changing environments. Molecular Systems
Biology, 16(9), Article e9965. https://doi.org/10.15252/msb.20209965
Milias
Argeitis, A., & Kurdyaeva, T. (2020).
Derivation of moment equations for a nonlinear gene
expression model with initial condition and parameter
uncertainty.
Kamenz,
J., Gelens, L., & Ferrell, J. E. (2021). Bistable,
Biphasic Regulation of PP2A-B55 Accounts for the Dynamics of
Mitotic Substrate Phosphorylation. Current
Biology, 31(4), 794-808. https://doi.org/10.1016/j.cub.2020.11.058
Lockhead,
S., Moskaleva, A., Kamenz, J., Chen, Y., Kang, M.,
Reddy, A. R., Santos, S. D. M., & Ferrell, J. E. (2020).
The Apparent Requirement for Protein Synthesis during G2
Phase Is due to Checkpoint Activation. Cell
reports, 32(2), Article 107901. https://doi.org/10.1016/j.celrep.2020.107901
2019
Kimkes,
T. E. P., & Heinemann, M. (2020). How
bacteria recognise and respond to surface contact.
FEMS Microbiology Reviews, 44(1),
106-122. https://doi.org/10.1093/femsre/fuz029
Niebel,
B., Leupold, S., & Heinemann, M. (2019). An
upper limit on Gibbs energy dissipation governs cellular
metabolism. Nature Metabolism,
1, 125-131. https://doi.org/10.1038/s42255-018-0006-7
Balaban,
N. Q., Helaine, S., Lewis, K., Ackermann, M., Aldridge, B.,
Andersson, D. I., Brynildsen, M. P., Bumann, D., Camilli, A.,
Collins, J. J., Dehio, C., Fortune, S., Ghigo, J.-M., Hardt, W.-D.,
Harms, A., Heinemann, M., Hung, D. T., Jenal, U.,
Levin, B. R., ... Zinkernagel, A. (2019). Definitions and
guidelines for research on antibiotic persistence.
Nature Reviews Microbiology, 17(7),
441-448. https://doi.org/10.1038/s41579-019-0196-3
Litsios,
A., Huberts, D. H. E. W., Terpstra, H. M.,
Guerra, P., Schmidt, A., Buczak, K., Papagiannakis,
A., Rovetta, M., Hekelaar, J., Hubmann,
G., Exterkate, M., Milias-Argeitis,
A., & Heinemann, M. (2019).
Differential scaling between G1 protein production and cell
size dynamics promotes commitment to the cell division cycle in
budding yeast. Nature Cell Biology,
21(11), 1382-1392. https://doi.org/10.1038/s41556-019-0413-3
Ozsezen,
S., Papagiannakis, A., Chen, H., Niebel,
B., Milias-Argeitis, A., & Heinemann,
M. (2019). Inference of the High-Level Interaction
Topology between the Metabolic and Cell-Cycle Oscillators from
Single-Cell Dynamics. Cell systems,
9(4), 354-365. https://doi.org/10.1016/j.cels.2019.09.003
Zhang,
Z., Kimkes, T. E. P., & Heinemann, M. (2019).
Manipulating rod-shaped bacteria with optical
tweezers. Scientific Reports,
9(1), Article 19086. https://doi.org/10.1038/s41598-019-55657-y
Monteiro,
F., Hubmann, G., Takhaveev, V., Vedelaar, S.
R., Norder, J., Hekelaar, J., Saldida,
J., Litsios, A., Wijma, H. J., Schmidt,
A., & Heinemann, M. (2019). Measuring
glycolytic flux in single yeast cells with an orthogonal synthetic
biosensor. Molecular Systems Biology,
15(12), Article e9071. https://doi.org/10.15252/msb.20199071
Balaban,
N. Q., Helaine, S., Lewis, K., Ackermann, M., Aldridge, B.,
Andersson, D. I., Brynildsen, M. P., Bumann, D., Camilli, A.,
Collins, J. J., Dehio, C., Fortune, S., Ghigo, J.-M., Hardt, W.-D.,
Harms, A., Heinemann, M., Hung, D. T., Jenal, U.,
Levin, B. R., ... Zinkernagel, A. (2019). Publisher
Correction: Definitions and guidelines for research on antibiotic
persistence. Nature Reviews
Microbiology, 17(7), 460-460. https://doi.org/10.1038/s41579-019-0207-4
Leupold,
S., Hubmann, G., Litsios, A., Meinema, A. C., Takhaveev,
V., Papagiannakis, A., Niebel, B., Janssens, G., Siegel,
D., & Heinemann, M. (2019). Saccharomyces
cerevisiae goes through distinct metabolic phases during its
replicative lifespan. eLife,
8, Article e41046. https://doi.org/10.7554/eLife.41046
Yang,
Y.-S., Kato, M., Wu, X., Litsios, A., Sutter, B. M., Wang, Y., Hsu,
C.-H., Wood, N. E., Lemoff, A., Mirzaei, H., Heinemann,
M., & Tu, B. P. (2019). Yeast Ataxin-2 Forms an
Intracellular Condensate Required for the Inhibition of TORC1
Signaling during Respiratory Growth.
Cell, 177(3), 697-710. https://doi.org/10.1016/j.cell.2019.02.043
Kurdyaeva,
T., & Milias-Argeitis, A. (2019).
Efficient global sensitivity analysis of biochemical networks
using Gaussian process regression. In 2018 IEEE
Conference on Decision and Control, CDC 2018 (pp. 2673-2678).
Article 8618902 (Proceedings of the IEEE Conference on Decision and
Control). Institute of Electrical and Electronics Engineers Inc..
https://doi.org/10.1109/CDC.2018.8618902
2018
von
Borzyskowski, L. S., Carrillo, M., Leupold, S., Glatter, T.,
Kiefer, P., Weishaupt, R., Heinemann, M., & Erb,
T. J. (2018). An engineered Calvin-Benson-Bassham cycle for
carbon dioxide fixation in Methylobacterium extorquens AM1.
Metabolic Engineering, 47, 423-433.
https://doi.org/10.1016/j.ymben.2018.04.003
Bley
Folly, B., Ortega, A. D., Hubmann, G., Bonsing-Vedelaar,
S., Wijma, H. J., van der Meulen,
P., Milias-Argeitis, A., & Heinemann,
M. (2018). Assessment of the interaction between the
flux-signaling metabolite fructose-1,6-bisphosphate and the
bacterial transcription factors CggR and Cra.
Molecular Microbiology, 109(3),
278-290. https://doi.org/10.1111/mmi.14008
Zhang,
Z., Milias-Argeitis, A., & Heinemann,
M. (2018). Dynamic single-cell NAD(P)H measurement
reveals oscillatory metabolism throughout the E. coli cell division
cycle. Scientific Reports,
8(1), Article 2162. https://doi.org/10.1038/s41598-018-20550-7
Takhaveev,
V., & Heinemann, M. (2018). Metabolic
heterogeneity in clonal microbial populations.
Current Opinion in Microbiology, 45,
30-38. https://doi.org/10.1016/j.mib.2018.02.004
Kimkes,
T. E. P., & Heinemann, M. (2018).
Reassessing the role of the Escherichia coli CpxAR system in
sensing surface contact. PLoS ONE,
13(11), Article e0207181. https://doi.org/10.1371/journal.pone.0207181
Milias
Argeitis, A., & Kurdyaeva, T. (2018).
Analytical calculation of Sobol sensitivity indices for
Gaussian Processes with a squared exponential covariance
function.
Rullan,
M., Benzinger, D., Schmidt, G. W., Milias-Argeitis,
A., & Khammash, M. (2018). An Optogenetic Platform
for Real-Time, Single-Cell Interrogation of Stochastic
Transcriptional Regulation. Molecular
Cell, 70(4), 745-756. https://doi.org/10.1016/j.molcel.2018.04.012
Garcia,
H. G., Benzinger, D., Rullan, M., Milias-Argeitis, A.,
Khammash, M., Deutschbauer, A. M., Langdon, E. M., &
Gladfelter, A. S. (2018). Principles of Systems Biology, No.
30. Cell systems, 7(1), 1-2.
https://doi.org/10.1016/j.cels.2018.07.002
Thadani,
R., Kamenz, J., Heeger, S., Munoz, S., & Uhlmann,
F. (2018). Cell-Cycle Regulation of Dynamic
ChromosomeAssociation of the Condensin Complex.
Cell reports, 23(8), 2308-2317. https://doi.org/10.1016/j.celrep.2018.04.082
2017
Litsios,
A., Ortega, Á. D., Wit, E. C., &
Heinemann, M. (2018). Metabolic-flux dependent
regulation of microbial physiology. Current
Opinion in Microbiology, 42, 71-78. https://doi.org/10.1016/j.mib.2017.10.029
Papagiannakis,
A., Niebel, B., Wit, E., & Heinemann,
M. (2017). A CDK-independent metabolic oscillator
orchestrates the budding yeast cell cycle. Febs
Journal, 284(S1), 54. Article S.5.4-002. https://doi.org/10.1111/febs.14170
Radzikowski,
J. L., Schramke, H., & Heinemann, M. (2017).
Bacterial persistence from a system-level perspective.
Current Opinion in Biotechnology,
46, 98-105. https://doi.org/10.1016/j.copbio.2017.02.012
Heinemann,
M., & Pilpel, Y. (2017). Editorial overview:
Systems biology for biotechnology. Current Opinion
in Biotechnology, 46, iv-v. https://doi.org/10.1016/j.copbio.2017.07.001
Papagiannakis,
A., de Jonge, J. J., Zhang, Z., & Heinemann, M.
(2017). Quantitative characterization of the auxin-inducible
degron: a guide for dynamic protein depletion in single yeast
cells. Scientific Reports, 7,
Article 4704. https://doi.org/10.1038/s41598-017-04791-6
Filer,
D., Thompson, M. A., Takhaveev, V., Dobson, A. J.,
Kotronaki, I., Green, J. W. M., Heinemann, M., Tullet,
J. M. A., & Alic, N. (2017). RNA polymerase III limits
longevity downstream of TORC1. Nature,
552(7684), 263-267. https://doi.org/10.1038/nature25007
Gupta,
A., Milias-Argeitis, A., & Khammash, M. (2017).
Dynamic disorder in simple enzymatic reactions induces
stochastic amplification of substrate. Journal of
the Royal Society Interface, 14(132), Article
20170311. https://doi.org/10.1098/rsif.2017.0311
Kuzmanovska,
I., Milias Argeitis, A., Mikelson, J., Zechner, C.,
& Khammash, M. (2017). Parameter inference for stochastic
single-cell dynamics from lineage tree data. BMC
Systems Biology, 11(52), Article 52. https://doi.org/10.1186/s12918-017-0425-1
Kamenz,
J., & Ferrell, J. E. (2017). The Temporal Ordering
of Cell-Cycle Phosphorylation. Molecular
Cell, 65(3), 371-373. https://doi.org/10.1016/j.molcel.2017.01.025
Kamenz,
J., & Hauf, S. (2017). Time To Split Up: Dynamics
of Chromosome Separation. Trends in Cell
Biology, 27(1), 42-54. https://doi.org/10.1016/j.tcb.2016.07.008
2016
Papagiannakis,
A., Niebel, B., Wit, E. C., & Heinemann,
M. (2017). Autonomous Metabolic Oscillations Robustly
Gate the Early and Late Cell Cycle. Molecular
Cell, 65(2), 285-295. https://doi.org/10.1016/j.molcel.2016.11.018
Radzikowski,
J. L., Vedelaar, S., Siegel, D., Ortega, Á. D.,
Schmidt, A., & Heinemann, M. (2016).
Bacterial persistence is an active σS stress response to
metabolic flux limitation. Molecular Systems
Biology, 12(9), 1-18. Article 882. https://doi.org/10.15252/msb.20166998
van
Rijsewijk, B. R. B. H., Kochanowski, K., Heinemann,
M., & Sauer, U. (2016). Distinct transcriptional
regulation of the two Escherichia coli transhydrogenases PntAB and
UdhA. Microbiology-Reading,
162(9), 1672-1679. https://doi.org/10.1099/mic.0.000346
Heinemann,
M. (2016). Flux Controls Flux – a Key Challenge
for Metabolic Engineering.
Chemie-Ingenieur-Technik, 88(9),
1392. https://doi.org/10.1002/cite.201650531
Milias-Argeitis,
A., & Khammash, M. (2016). Adaptive Model
Predictive Control of an optogenetic system. In 2015
54th IEEE Conference on Decision and Control, CDC 2015 (Vol.
2016-February, pp. 1265-1270). Article 7402385 Institute of
Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CDC.2015.7402385
Milias-Argeitis,
A., Rullan, M., Aoki, S. K., Buchmann, P., & Khammash,
M. (2016). Automated optogenetic feedback control for precise
and robust regulation of gene expression and cell growth.
Nature Communications, 7, Article
12546. https://doi.org/10.1038/ncomms12546
Milias-Argeitis,
A., Oliveira, A. P., Gerosa, L., Falter, L., Sauer, U.,
& Lygeros, J. (2016). Elucidation of Genetic Interactions
in the Yeast GATA-Factor Network Using Bayesian Model
Selection. PLoS Computational Biology,
12(3), 1-27. Article e1004784. https://doi.org/10.1371/journal.pcbi.1004784
2015
Schmidt,
A., Kochanowski, K., Vedelaar, S., Ahrné, E.,
Volkmer, B., Callipo, L., Knoops, K., Bauer, M., Aebersold,
R., & Heinemann, M. (2016). The quantitative
and condition-dependent Escherichia coli proteome.
Nature Biotechnology, 34(1),
104-110. https://doi.org/10.1038/nbt.3418
Janssens,
G. E., Meinema, A. C., Gonzalez, J., Wolters, J. C.,
Schmidt, A., Guryev, V., Bischoff,
R., Wit, E. C., Veenhoff, L.
M., & Heinemann, M. (2015). Protein
biogenesis machinery is a driver of replicative aging in
yeast. eLife, 4, Article
e08527. https://doi.org/10.7554/eLife.08527
Ruess,
J., Parise, F., Milias-Argeitis, A., Khammash, M.,
& Lygeros, J. (2015). Iterative experiment design guides
the characterization of a light-inducible gene expression
circuit. Proceedings of the National Academy of
Sciences, 112(26), 8148-8153. https://doi.org/10.1073/pnas.1423947112
Milias-Argeitis,
A., & Khammash, M. (2015). Optimization-based
Lyapunov function construction for continuous-time Markov chains
with affine transition rates. In Proceedings of the IEEE
Conference on Decision and Control (pp. 4617-4622).
(Proceedings of the IEEE Conference on Decision and Control; Vol.
2015-February). IEEE. https://doi.org/10.1109/CDC.2014.7040110
Milias-Argeitis,
A., Engblom, S., Bauer, P., & Khammash, M. (2015).
Stochastic focusing coupled with negative feedback enables
robust regulation in biochemical reaction networks.
Journal of the Royal Society Interface,
12, 1-32. https://doi.org/10.1098/rsif.2015.0831
Kamenz,
J., Mihaljev, T., Kubis, A., Legewie, S., & Hauf, S.
(2015). Robust Ordering of Anaphase Events by
AdaptiveThresholds and Competing Degradation Pathways.
Molecular Cell, 60, 446-459. https://doi.org/10.1016/j.molcel.2015.09.022
2014
Daszczuk,
A., Dessalegne, Y., Drenth, I., Hendriks, E., Jo, E.,
van Lente, T., Oldebesten, A., Parrish, J., Poljakova, W.,
Purwanto, A. A., van Raaphorst, R., Boonstra,
M., van Heel, A., Herber, M., van der
Meulen, S., Siebring, J., Sorg, R. A., Heinemann,
M., Kuipers, O. P., & Veening, J.-W.
(2014). Bacillus subtilis Biosensor Engineered To Assess Meat
Spoilage. ACS Synthetic Biology,
3(12), 999-1002. https://doi.org/10.1021/sb5000252
Huberts,
D. H. E. W., Gonzalez Hernandez, J., Lee, S. S., Litsios, A.,
Hubmann, G., Wit, E. C., & Heinemann,
M. (2014). Calorie restriction does not elicit a
robust extension of replicative lifespan in Saccharomyces
cerevisiae. Proceedings of the National Academy of
Science of the United States of America,
111(32), 11727-11731. https://doi.org/10.1073/pnas.1410024111
Kotte,
O., Volkmer, B., Radzikowski, J. L., & Heinemann,
M. (2014). Phenotypic bistability in Escherichia
coli's central carbon metabolism. Molecular
Systems Biology, 10(7), Article 736. https://doi.org/10.15252/msb.20135022
Lee,
S. S., Dechant, R., Vizcarra, I. A., Huberts, D. H. E. W., Lee, L.
P., Heinemann, M., & Peter, M. (2014).
Single cell analysis of yeast aging using microfluidic
dissection. In 18th International Conference on
Miniaturized Systems for Chemistry and Life Sciences, MicroTAS
2014 (pp. 666-668). Chemical and Biological Microsystems
Society .
Milias-Argeitis,
A., Lygeros, J., & Khammash, M. (2014). Fast
variance reduction for steady-state simulation and sensitivity
analysis of stochastic chemical systems using shadow function
estimators. Journal of Chemical
Physics, 141(2), Article 024104. https://doi.org/10.1063/1.4886935
Fromm,
S. A., Kamenz, J., Nöldeke, E. R., Neu, A.,
Zocher, G., & Sprangers, R. (2014). In vitro
reconstitution of a cellular phase-transition process that involves
the mRNA decapping machinery. Angewandte Chemie -
International Edition, 53(28), 7354-7359. https://doi.org/10.1002/anie.201402885
Kamenz,
J., & Hauf, S. (2014). Slow checkpoint activation
kinetics as a safety device in anaphase. Current
Biology, 24(6), 646-651. https://doi.org/10.3389/fnins.2013.00195
2013
Huberts,
D. H. E. W., Sik Lee, S., Gonzáles, J., Janssens, G. E.,
Vizcarra, I. A., & Heinemann, M. (2013).
Construction and use of a microfluidic dissection platform
for long-term imaging of cellular processes in budding
yeast. Nature protocols,
8(6), 1019-1027. https://doi.org/10.1038/nprot.2013.060
Huberts,
D. H. E. W., Janssens, G. E., Lee, S. S., Vizcarra, I. A.,
& Heinemann, M. (2013). Continuous High-resolution
Microscopic Observation of Replicative Aging in Budding
Yeast. Journal of visualized experiments :
JoVE, (78), Article 50143. https://doi.org/10.3791/50143
Gerosa,
L., Kochanowski, K., Heinemann, M., & Sauer, U.
(2013). Dissecting specific and global transcriptional
regulation of bacterial gene expression. Molecular
Systems Biology, 9, Article 658. https://doi.org/10.1038/msb.2013.14
Kochanowski,
K., Volkmer, B., Gerosa, L., Haverkorn van Rijsewijk, B. R.,
Schmidt, A., & Heinemann, M. (2013).
Functioning of a metabolic flux sensor in Escherichia
coli. Proceedings of the National Academy of
Sciences of the United States of America,
110(3), 1130-1135. https://doi.org/10.1073/pnas.1202582110
Ibáñez,
A. J., Fagerer, S. R., Schmidt, A. M., Urban, P. L., Jefimovs, K.,
Geiger, P., Dechant, R., Heinemann, M., & Zenobi,
R. (2013). Mass spectrometry-based metabolomics of single
yeast cells. Proceedings of the National Academy
of Sciences of the United States of America,
110(22), 8790-8794. https://doi.org/10.1073/pnas.1209302110
Zampar,
G. G., Kümmel, A., Ewald, J., Jol, S., Niebel, B., Picotti,
P., Aebersold, R., Sauer, U., Zamboni, N., & Heinemann,
M. (2013). Temporal system-level organization of the
switch from glycolytic to gluconeogenic operation in yeast.
Molecular Systems Biology, 9,
651-1-651-13. Article 651. https://doi.org/10.1038/msb.2013.11
Esfahani,
P. M., Milias-Argeitis, A., & Chatterjee, D.
(2013). Analysis of controlled biological switches via
stochastic motion planning. In 2013 European Control
Conference, ECC 2013 (pp. 93-98). Article 6669626
Ruess,
J., Milias-Argeitis, A., & Lygeros, J. (2013).
Designing experiments to understand the variability in
biochemical reaction networks. Journal of the
Royal Society Interface, 10(88), Article
20130588. https://doi.org/10.1098/rsif.2013.0588
Milias-Argeitis,
A., & Lygeros, J. (2013). Steady-state simulation
of metastable stochastic chemical systems. Journal
of Chemical Physics, 138(18), Article 184109.
https://doi.org/10.1063/1.4804191
Heinrich,
S., Geissen, E.-M., Kamenz, J., Trautmann, S., Widmer,
C., Drewe, P., Knop, M., Radde, N., Hasenauer, J., & Hauf, S.
(2013). Determinants of robustness in spindle assembly
checkpoint signalling. Nature Cell
Biology, 15, 1328-1339. https://doi.org/10.1038/ncb2864
2012
Huberts,
D. H. E. W., Niebel, B., & Heinemann, M. (2012).
A flux-sensing mechanism could regulate the switch between
respiration and fermentation. Fems Yeast
Research, 12(2), 118-128. https://doi.org/10.1111/j.1567-1364.2011.00767.x
Schuetz,
R., Zamboni, N., Zampieri, M., Heinemann, M., &
Sauer, U. (2012). Multidimensional optimality of microbial
metabolism. Science,
336(6081), 601-604. https://doi.org/10.1126/science.1216882
Adadi,
R., Volkmer, B., Milo, R., Heinemann, M., &
Shlomi, T. (2012). Prediction of Microbial Growth Rate versus
Biomass Yield by a Metabolic Network with Kinetic
Parameters. PLoS Computational
Biology, 8(7), Article e1002575. https://doi.org/10.1371/journal.pcbi.1002575
Jol,
S. J., Kümmel, A., Terzer, M., Stelling, J., &
Heinemann, M. (2012). System-level insights into yeast
metabolism by thermodynamic analysis of elementary flux
modes. PLoS Computational Biology,
8(3), Article e1002415. https://doi.org/10.1371/journal.pcbi.1002415
Lee,
S. S., Avalos Vizcarra, I., Huberts, D. H. E. W., Lee, L. P.,
& Heinemann, M. (2012). Whole lifespan microscopic
observation of budding yeast aging through a microfluidic
dissection platform. Proceedings of the National
Academy of Sciences of the United States of America,
109(13), 4916-4920. https://doi.org/10.1073/pnas.1113505109
Fromm,
S. A., Truffault, V., Kamenz, J., Braun, J. E.,
Hoffmann, N. A., Izaurralde, E., & Sprangers, R. (2012).
The structural basis of Edc3‐ and Scd6‐mediated
activation of the Dcp1:Dcp2 mRNA decapping complex.
The EMBO Journal, 31, 279-290. https://doi.org/10.1038/emboj.2011.408
2011
Urban,
P. L., Schmidt, A. M., Fagerer, S. R., Amantonico, A., Ibañez,
A., Jefimovs, K., Heinemann, M., & Zenobi, R.
(2011). Carbon-13 labelling strategy for studying the ATP
metabolism in individual yeast cells by micro-arrays for mass
spectrometry. Molecular BioSystems,
7(10), 2837-2840. https://doi.org/10.1039/c1mb05248a
Costenoble,
R., Picotti, P., Reiter, L., Stallmach, R., Heinemann,
M., Sauer, U., & Aebersold, R. (2011).
Comprehensive quantitative analysis of central carbon and
amino-acid metabolism in Saccharomyces cerevisiae under multiple
conditions by targeted proteomics. Molecular
Systems Biology, 7(1), 464-1-464-13. Article
464. https://doi.org/10.1038/msb.2010.122
Volkmer,
B., & Heinemann, M. (2011).
Condition-dependent cell volume and concentration of
Escherichia coli to facilitate data conversion for systems biology
modeling. PLoS ONE, 6(7),
Article 23126. https://doi.org/10.1371/journal.pone.0023126
Heinemann,
M., & Sauer, U. (2011). From good old biochemical
analyses to high-throughput omics measurements and back.
Current Opinion in Biotechnology,
22(1), 1-2. https://doi.org/10.1016/j.copbio.2010.12.002
Bujara,
M., Schümperli, M., Pellaux, R., Heinemann, M.,
& Panke, S. (2011). Optimization of a blueprint for in
vitro glycolysis by metabolic real-time analysis.
Nature Chemical Biology, 7(5),
271-277. https://doi.org/10.1038/nchembio.541
Heinemann,
M., & Zenobi, R. (2011). Single cell
metabolomics. Current Opinion in
Biotechnology, 22(1), 26-31. https://doi.org/10.1016/j.copbio.2010.09.008
Sturm,
A., Heinemann, M., Arnoldini, M., Benecke, A.,
Ackermann, M., Benz, M., Dormann, J., & Hardt, W.-D. (2011).
The cost of virulence: retarded growth of Salmonella
Typhimurium cells expressing type III secretion system 1.
PLoS Pathogens, 7(7), Article
1002143. https://doi.org/10.1371/journal.ppat.1002143
Milias-Argeitis,
A., Summers, S., Stewart-Ornstein, J., Zuleta, I., Pincus,
D., El-Samad, H., Khammash, M., & Lygeros, J. (2011). In
silico feedback for in vivo regulation of a gene expression
circuit. Nature Biotechnology,
29, 1114-1116. https://doi.org/10.1038/nbt.2018
Ruess,
J., Milias-Argeitis, A., Summers, S., & Lygeros,
J. (2011). Moment estimation for chemically reacting systems
by extended Kalman filtering. Journal of Chemical
Physics, 135(16), Article 165102. https://doi.org/10.1063/1.3654135
2010
Kotte,
O., Zaugg, J. B., & Heinemann, M. (2010).
Bacterial adaptation through distributed sensing of metabolic
fluxes. Molecular Systems Biology,
82(9), 1492-1493. Article 355. https://doi.org/10.1038/msb.2010.10
Kummel,
A., Ewald, J. C., Fendt, S.-M., Jol, S. J., Picotti, P., Aebersold,
R., Sauer, U., Zamboni, N., & Heinemann, M.
(2010). Differential glucose repression in common yeast
strains in response to HXK2 deletion. Fems Yeast
Research, 10(3), 322-332. https://doi.org/10.1111/j.1567-1364.2010.00609.x
Bujara,
M., Schümperli, M., Billerbeck, S.,
Heinemann, M., & Panke, S. (2010). Exploiting
Cell-Free Systems: Implementation and Debugging of a System of
Biotransformations. Biotechnology and
Bioengineering, 106(3), 376-389. https://doi.org/10.1002/bit.22666
Heinemann,
M., & Sauer, U. (2010). Systems biology of
microbial metabolism. Current Opinion in
Microbiology, 13(3), 337-343. https://doi.org/10.1016/j.mib.2010.02.005
Jol,
S. J., Kümmel, A., Hatzimanikatis, V., Beard, D. A.,
& Heinemann, M. (2010). Thermodynamic calculations
for biochemical transport and reaction processes in metabolic
networks. Biophysical Journal,
99(10), 3139-3144. https://doi.org/10.1016/j.bpj.2010.09.043
Kleijn,
R., Fendt, S.-M., Schuetz, R., Heinemann, M., Zamboni,
N., & Sauer, U. (2010). Transcriptional control of
metabolic fluxes and computational identification of the governing
principles. Febs Journal,
277, 27-27.
Ramponi,
F., Chatterjee, D., Milias-Argeitis, A., Hokayem, P.,
& Lygeros, J. (2010). Attaining mean square boundedness
of a marginally stable stochastic linear system with a bounded
control input. IEEE Transactions on Automatic
Control, 55(10), 2414-2418. https://doi.org/10.1109/TAC.2010.2054850
Milias-Argeitis,
A., Porreca, R., Summers, S., & Lygeros, J. (2010).
Bayesian model selection for the yeast GATA-factor network: A
comparison of computational approaches. In 2010 49th
IEEE Conference on Decision and Control, CDC 2010 (pp.
3379-3384). Article 5717307 https://doi.org/10.1109/CDC.2010.5717307
2009
Kotte,
O., & Heinemann, M. (2009). A
divide-and-conquer approach to analyze underdetermined biochemical
models. Bioinformatics,
25(4), 519-525. https://doi.org/10.1093/bioinformatics/btp004
Lee,
S. S., Vizcarra, I. A., Lee, L. P., & Heinemann,
M. (2009). Long-term monitoring of yeast cell division
via elastic micro-pad. In Proceedings of Conference,
MicroTAS 2009 - The 13th International Conference on Miniaturized
Systems for Chemistry and Life Sciences (pp. 478-480).
Chemical and Biological Microsystems Society .
Graaf,
A. A. D., Freidig, A. P., Roos, B. D., Jamshidi, N.,
Heinemann, M., Rullmann, J. A. C., Hall, K. D., Adiels, M.,
& Ommen, B. V. (2009). Nutritional Systems Biology
Modeling: From Molecular Mechanisms to Physiology.
PLoS Computational Biology, 5(11),
Article e1000554. https://doi.org/10.1371/journal.pcbi.1000554
Cook,
G. M., Berney, M., Gebhard, S., Heinemann, M., Cox, R.
A., Danilchanka, O., & Niederweis, M. (2009). Physiology
of mycobacteria. Advances in microbial
physiology, 55, 81-182. https://doi.org/10.1016/S0065-2911(09)05502-7
Heinemann,
M., & Panke, S. (2009). Synthetic Biology: Putting
Engineering into Bioengineering. In P. Fu, & S. Panke
(Eds.), Systems Biology and Synthetic Biology (pp.
387-409). Wiley. https://doi.org/10.1002/9780470437988.ch11
Cinquemani,
E., Milias-Argeitis, A., Summers, S., & Lygeros,
J. (2009). Local identification of piecewise deterministic
models of genetic networks. In Hybrid Systems:
Computation and Control - 12th International Conference, HSCC 2009,
Proceedings (Vol. 5469, pp. 105-119). (Lecture Notes in
Computer Science (including subseries Lecture Notes in Artificial
Intelligence and Lecture Notes in Bioinformatics); Vol. 5469). https://doi.org/10.1007/978-3-642-00602-9_8
2008
Herrgård,
M. J., Swainston, N., Dobson, P., Dunn, W. B., Arga, K. Y., Arvas,
M., Blüthgen, N., Borger, S., Costenoble, R., Heinemann,
M., Hucka, M., Novère, N. L., Li, P., Liebermeister,
W., Mo, M. L., Oliveira, A. P., Petranovic, D., Pettifer, S.,
Simeonidis, E., ... Kell, D. B. (2008). A consensus yeast
metabolic network reconstruction obtained from a community approach
to systems biology. Nature
Biotechnology, 26(10), 1155-1160. https://doi.org/10.1038/nbt1492
Zamboni,
N., Kümmel, A., & Heinemann, M. (2008).
anNET: a tool for network-embedded thermodynamic analysis of
quantitative metabolome data. Bmc
Bioinformatics, 9(199), Article 199. https://doi.org/10.1186/1471-2105-9-199
Amantonico,
A., Oh, J. Y., Sobek, J., Heinemann, M., & Zenobi,
R. (2008). Mass Spectrometric Method for Analyzing
Metabolites in Yeast with Single Cell Sensitivity.
Angewandte Chemie International Edition,
47(29), 5382-5385. https://doi.org/10.1002/anie.200705923
Cinquemani,
E., Milias-Argeitis, A., & Lygeros, J. (2008).
Identification of genetic regulatory networks: A stochastic
hybrid approach. In Proceedings of the 17th World
Congress, International Federation of Automatic Control, IFAC
(IFAC Proceedings Volumes (IFAC-PapersOnline); Vol. 17, No. 1 PART
1). Elsevier.
Cinquemani,
E., Milias-Argeitis, A., Summers, S., & Lygeros,
J. (2008). Stochastic dynamics of genetic networks: modelling
and parameter identification. Bioinformatics
(Oxford, England), 24(23), 2748-2754. https://doi.org/10.1093/bioinformatics/btn527
2007
Makart,
S., Heinemann, M., & Panke, S. (2007).
Characterization of the AlkS/P-alkB-expression system as an
efficient tool for the production of recombinant proteins in
Escherichia coli fed-batch fermentations.
Biotechnology and Bioengineering,
96(2), 326-336. https://doi.org/10.1002/bit.21117
Sauer,
U., Heinemann, M., & Zamboni, N. (2007).
Genetics - Getting closer to the whole picture.
Science, 316(5824), 550-551. https://doi.org/10.1126/science.1142502
Hübscher,
J., Jansen, A., Kotte, O., Schäfer, J., Majcherczyk, P. A.,
Harris, L. G., Bierbaum, G., Heinemann, M., &
Berger-Bächi, B. (2007). Living with an imperfect cell
wall: compensation of femAB inactivation in Staphylococcus
aureus. BMC Genomics, 8,
Article 307. https://doi.org/10.1186/1471-2164-8-307
2006
Seggewib,
J., Becker, K., Kotte, O., Eisenacher, M., Khoschkhoi Yazdi, M. R.,
Fischer, A., McNamara, P., Proctor, R. A., Peters, G.,
Heinemann, M., & von Eiff, C. (2006). Detailed
survey of genome-wide expression differences between a
Staphylococcus aureus mutant displaying the small colony variant
phenotype and its parental strain. International
journal of medical microbiology, 296,
132-133.
Bechtold,
M., Makart, S., Heinemann, M., & Panke, S. (2006).
Integrated operation of continuous chromatography and
biotransformations for the generic high yield production of fine
chemicals. Journal of Biotechnology,
124(1), 146-162. https://doi.org/10.1016/j.jbiotec.2006.01.019
Kümmel,
A., Panke, S., & Heinemann, M. (2006).
Putative regulatory sites unraveled by network-embedded
thermodynamic analysis of metabolome data.
Molecular Systems Biology, 2,
2006.0034. https://doi.org/10.1038/msb4100074
Seggewiß,
J., Becker, K., Kotte, O., Eisenacher, M., Khoschkhoi Yazdi, M. R.,
Fischer, A., McNamara, P., Laham, N. A., Proctor, R., Peters,
G., Heinemann, M., & Eiff, C. V. (2006).
Reporter Metabolite Analysis of Transcriptional Profiles of a
Staphylococcus aureus Strain with Normal Phenotype and Its Isogenic
hemB Mutant Displaying the Small-Colony-Variant Phenotype.
Journal of Bacteriology, 188(22),
7765-7777. https://doi.org/10.1128/JB.00774-06
Davidescu,
F. P., Madsen, H., Schümperli, M., Heinemann, M.,
Panke, S., & Jørgensen, S. B. (2006). Stochastic
grey box modeling of the enzymatic biochemical reaction network of
E. coli mutants. In Proceedings of the 16th European
Symposium on Computer Aided Process Engineering and 9th
International Symposium on Process Systems Engineering (21
ed., pp. 161-166). Elsevier.
Bechtold,
M., Heinemann, M., & Panke, S. (2006).
Suitability of teicoplanin-aglycone bonded stationary phase
for simulated moving bed enantio separation of racemic amino acids
employing composition-constrained eluents. Journal
of Chromatography A, 1113(1-2), 167-176. https://doi.org/10.1016/j.chroma.2006.02.007
Heinemann,
M., & Panke, S. (2006). Synthetic biology--putting
engineering into biology.
Bioinformatics, 22(22), 2790-2799.
https://doi.org/10.1093/bioinformatics/btl469
Kümmel,
A., Panke, S., & Heinemann, M. (2006).
Systematic assignment of thermodynamic constraints in
metabolic network models. Bmc
Bioinformatics, 7(512). https://doi.org/10.1186/1471-2105-7-512,
https://doi.org/10.1186/1471-2105-5-133
2005
Schumperli,
M., Heinemann, M., Gomolka, S., Kummel, A., &
Panke, S. (2005). A new approach for the production of DHAP:
The system of biotransformations. Journal of
Biotechnology, 118, S90-S90.
Kummel,
A., Schumperli, M., & Heinemann, M. (2005).
Design of a system of biotransformations by means of
stoichiometric network analysis. Journal of
Biotechnology, 118, S105-S105.
Heinemann,
M., Kümmel, A., Ruinatscha, R., & Panke, S. (2005).
In Silico Genome-Scale Reconstruction and Validation of the
Staphylococcus aureus Metabolic Network.
Biotechnology and Bioengineering,
92(7), 850-864.
Heinemann,
M., Meinberg, H., Büchs, J., Koß, H.-J., &
Ansorge-Schumacher, M. B. (2005). Method for Quantitative
Determination of Spatial Polymer Distribution in Alginate Beads
Using Raman Spectroscopy. Applied
Spectroscopy, 59(3), 280-285. https://doi.org/10.1366/0003702053585363
Kluge,
J., Kummel, A., Panke, S., & Heinemann, M. (2005).
Model-based identification of regulation patterns controlling
metabolic redundancy in central carbon metabolism.
Journal of Biotechnology, 118,
S3-S3.
Trivedi,
A., Heinemann, M., Spiess, A. C., Daussmann, T., &
Büchs, J. (2005). Optimization of Adsorptive
Immobilization of Alcohol Dehydrogenases. Journal
of Bioscience and Bioengineering, 99(4),
340-347. https://doi.org/10.1263/jbb.99.340
| Last modified: | 09 June 2023 8.36 p.m. |