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Publications

2019

Balaban, N. Q., Helaine, S., Lewis, K., Ackermann, M., Aldridge, B., Andersson, D. I., ... 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
Balaban, N. Q., Helaine, S., Lewis, K., Ackermann, M., Aldridge, B., Andersson, D. I., ... 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
Yang, Y-S., Kato, M., Wu, X., Litsios, A., Sutter, B. M., Wang, Y., ... 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
Leupold, S., Hubmann, G., Litsios, A., Meinema, A. C., Takhaveev, V., Papagiannakis, A., ... Heinemann, M. (2019). Saccharomyces cerevisiae goes through distinct metabolic phases during its replicative lifespan. eLife, 8. https://doi.org/10.7554/eLife.41046
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
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). [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

Kimkes, T. E. P., & Heinemann, M. (2018). Reassessing the role of the Escherichia coli CpxAR system in sensing surface contact. PLoS ONE, 13(11), [e0207181]. https://doi.org/10.1371/journal.pone.0207181
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
Bley Folly, B., Ortega, A. D., Hubmann, G., Bonsing-Vedelaar, S., Wijma, H. J., van der Meulen, P., ... 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
von Borzyskowski, L. S., Carrillo, M., Leupold, S., Glatter, T., Kiefer, P., Weishaupt, R., ... 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
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), [2162]. https://doi.org/10.1038/s41598-018-20550-7
Garcia, H. G., Benzinger, D., Rullan, M., Milias-Argeitis, A., Khammash, M., Deutschbauer, A. 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
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.e6. https://doi.org/10.1016/j.molcel.2018.04.012
Milias Argeitis, A., & Kurdyaeva, T. (2018). Analytical calculation of Sobol sensitivity indices for Gaussian Processes with a squared exponential covariance function.

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
Filer, D., Thompson, M. A., Takhaveev, V., Dobson, A. J., Kotronaki, I., Green, J. W. M., ... Alic, N. (2017). RNA polymerase III limits longevity downstream of TORC1. Nature, 552(7684), 263-267. https://doi.org/10.1038/nature25007
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. [S.5.4-002]. https://doi.org/10.1111/febs.14170
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, [4704]. https://doi.org/10.1038/s41598-017-04791-6
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
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), [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), [52]. https://doi.org/10.1186/s12918-017-0425-1

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. [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., 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. [e1004784]. https://doi.org/10.1371/journal.pcbi.1004784
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). [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, [12546]. https://doi.org/10.1038/ncomms12546

2015

Schmidt, A., Kochanowski, K., Vedelaar, S., Ahrné, E., Volkmer, B., Callipo, L., ... 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., ... Heinemann, M. (2015). Protein biogenesis machinery is a driver of replicative aging in yeast. eLife, 4, [e08527]. https://doi.org/10.7554/eLife.08527
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). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CDC.2014.7040110
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., 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

2014

Daszczuk, A., Dessalegne, Y., Drenth, I., Hendriks, E., Jo, E., van Lente, T., ... 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), [736]. https://doi.org/10.15252/msb.20135022
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), [024104]. https://doi.org/10.1063/1.4886935

2013

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), [50143]. https://doi.org/10.3791/50143
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
Ibáñez, A. J., Fagerer, S. R., Schmidt, A. M., Urban, P. L., Jefimovs, K., Geiger, P., ... 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
Gerosa, L., Kochanowski, K., Heinemann, M., & Sauer, U. (2013). Dissecting specific and global transcriptional regulation of bacterial gene expression. Molecular Systems Biology, 9, [658]. https://doi.org/10.1038/msb.2013.14
Zampar, G. G., Kümmel, A., Ewald, J., Jol, S., Niebel, B., Picotti, P., ... 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. [651]. https://doi.org/10.1038/msb.2013.11
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
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), 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), [184109]. https://doi.org/10.1063/1.4804191
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). [6669626]

2012

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), [e1002575]. https://doi.org/10.1371/journal.pcbi.1002575
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
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
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), [e1002415]. https://doi.org/10.1371/journal.pcbi.1002415
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

2011

Urban, P. L., Schmidt, A. M., Fagerer, S. R., Amantonico, A., Ibañez, A., Jefimovs, K., ... 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
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), [23126]. https://doi.org/10.1371/journal.pone.0023126
Sturm, A., Heinemann, M., Arnoldini, M., Benecke, A., Ackermann, M., Benz, M., ... Hardt, W-D. (2011). The cost of virulence: retarded growth of Salmonella Typhimurium cells expressing type III secretion system 1. PLoS Pathogens, 7(7), [1002143]. https://doi.org/10.1371/journal.ppat.1002143
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
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. [464]. https://doi.org/10.1038/msb.2010.122
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
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
Milias-Argeitis, A., Summers, S., Stewart-Ornstein, J., Zuleta, I., Pincus, D., El-Samad, H., ... 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), [165102]. https://doi.org/10.1063/1.3654135

2010

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
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
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.
Kummel, A., Ewald, J. C., Fendt, S-M., Jol, S. J., Picotti, P., Aebersold, R., ... 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
Kotte, O., Zaugg, J. B., & Heinemann, M. (2010). Bacterial adaptation through distributed sensing of metabolic fluxes. Molecular Systems Biology, 82(9), 1492-1493. [355]. https://doi.org/10.1002/cite.201050719
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
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). [5717307] https://doi.org/10.1109/CDC.2010.5717307

2009

Graaf, A. A. D., Freidig, A. P., Roos, B. D., Jamshidi, N., Heinemann, M., Rullmann, J. A. C., ... Ommen, B. V. (2009). Nutritional Systems Biology Modeling: From Molecular Mechanisms to Physiology. PLoS Computational Biology, 5(11), [e1000554]. https://doi.org/10.1371/journal.pcbi.1000554
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
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
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

Zamboni, N., Kümmel, A., & Heinemann, M. (2008). anNET: a tool for network-embedded thermodynamic analysis of quantitative metabolome data. Bmc Bioinformatics, 9(199), [199]. https://doi.org/10.1186/1471-2105-9-199
Herrgård, M. J., Swainston, N., Dobson, P., Dunn, W. B., Arga, K. Y., Arvas, M., ... 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
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

Hübscher, J., Jansen, A., Kotte, O., Schäfer, J., Majcherczyk, P. A., Harris, L. G., ... Berger-Bächi, B. (2007). Living with an imperfect cell wall: compensation of femAB inactivation in Staphylococcus aureus. BMC Genomics, 8, [307]. https://doi.org/10.1186/1471-2164-8-307
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
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

2006

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
Heinemann, M., & Panke, S. (2006). Synthetic biology--putting engineering into biology. Bioinformatics, 22(22), 2790-2799. https://doi.org/10.1093/bioinformatics/btl469
Seggewib, J., Becker, K., Kotte, O., Eisenacher, M., Khoschkhoi Yazdi, M. R., Fischer, A., ... 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., 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
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., ... 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.

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.
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.
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
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
Buthe, A., Recker, T., Heinemann, M., Hartmeier, W., Büchs, J., & Ansorge-Schumacher, M. B. (2005). pH-optima in lipase-catalysed esterification. Biocatalysis and Biotransformation, 23(5), 307-314.

2004

Panke, S., Kümmel, A., Schümperli, M., & Heinemann, M. (2004). Industrial multi-step biotransformations. Chimica Oggi-Chemistry Today, 22(9), 44-47.
Ferloni, C., Heinemann, M., Hummel, W., Daussmann, T., & Büchs, J. (2004). Optimization of enzymatic gas-phase reactions by increasing the long-term stability of the catalyst. Biotechnology Progress, 20(3), 975-978. https://doi.org/10.1021/bp034334e
Heinemann, M., Limper, U., & Büchs, J. (2004). New insights in the spatially resolved dynamic pH measurement in macroscopic large absorbent particles by confocal laser scanning microscopy. Journal of Chromatography A, 1024(1), 45-53. https://doi.org/10.1016/j.chroma.2003.09.065

2003

Heinemann, M., Kümmel, A., Giesen, R., Ansorge-Schumacher, M. B., & Büchs, J. (2003). Experimental and Theoretical Analysis of Phase Equilibria in a Two-phase System Used for Biocatalytic Esterifications. Biocatalysis and Biotransformation, 21(3), 115-121. https://doi.org/10.1080/1024242031000155082

2002

Heinemann, M., Wagner, T., Doumèche, B., Ansorge-Schumacher, M., & Büchs, J. (2002). A new approach for the spatially resolved qualitative analysis of the protein distribution in hydrogel beads based on confocal laser scanning microscopy. Biotechnology Letters, 24(10), 845-850. https://doi.org/10.1023/A:1015558823726
Doumèche, B., Heinemann, M., Büchs, J., Hartmeier, W., & Ansorge-Schumacher, M. B. (2002). Enzymatic catalysis in gel-stabilized two-phase systems: improvement of the solvent phase. Journal of Molecular Catalysis B: Enzymatic, 18(1), 19-27. https://doi.org/10.1016/S1381-1177(02)00044-9
Last modified:10 November 2016 11.00 a.m.