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Best matches for Ebenhöh O[au]:

The importance of the photosynthetic Gibbs effect in the elucidation of the Calvin-Benson-Bassham cycle. Ebenhöh O et al. Biochem Soc Trans. (2018)

Modelling phosphorus uptake in microalgae. Singh D et al. Biochem Soc Trans. (2018)

Review and perspective on mathematical modeling of microbial ecosystems. Succurro A et al. Biochem Soc Trans. (2018)

Search results

Items: 1 to 50 of 65

1.

A single-input binary counting module based on serine integrase site-specific recombination.

Zhao J, Pokhilko A, Ebenhöh O, Rosser SJ, Colloms SD.

Nucleic Acids Res. 2019 May 21;47(9):4896-4909. doi: 10.1093/nar/gkz245.

2.

Emergent Subpopulation Behavior Uncovered with a Community Dynamic Metabolic Model of Escherichia coli Diauxic Growth.

Succurro A, Segrè D, Ebenhöh O.

mSystems. 2019 Jan 15;4(1). pii: e00230-18. doi: 10.1128/mSystems.00230-18. eCollection 2019 Jan-Feb.

3.

Balancing energy supply during photosynthesis - a theoretical perspective.

Matuszyńska A, Saadat NP, Ebenhöh O.

Physiol Plant. 2019 May;166(1):392-402. doi: 10.1111/ppl.12962. Epub 2019 Mar 25.

PMID:
30864189
4.

The intertwined metabolism during symbiotic nitrogen fixation elucidated by metabolic modelling.

Pfau T, Christian N, Masakapalli SK, Sweetlove LJ, Poolman MG, Ebenhöh O.

Sci Rep. 2018 Aug 21;8(1):12504. doi: 10.1038/s41598-018-30884-x.

5.

Mathematical model of a serine integrase-controlled toggle switch with a single input.

Pokhilko A, Ebenhöh O, Stark WM, Colloms SD.

J R Soc Interface. 2018 Jun;15(143). pii: 20180160. doi: 10.1098/rsif.2018.0160.

6.

Data-driven dynamical model indicates that the heat shock response in Chlamydomonas reinhardtii is tailored to handle natural temperature variation.

Magni S, Succurro A, Skupin A, Ebenhöh O.

J R Soc Interface. 2018 May;15(142). pii: 20170965. doi: 10.1098/rsif.2017.0965.

7.

Modelling phosphorus uptake in microalgae.

Singh D, Nedbal L, Ebenhöh O.

Biochem Soc Trans. 2018 Apr 17;46(2):483-490. doi: 10.1042/BST20170262. Review.

PMID:
29666218
8.

Review and perspective on mathematical modeling of microbial ecosystems.

Succurro A, Ebenhöh O.

Biochem Soc Trans. 2018 Apr 17;46(2):403-412. doi: 10.1042/BST20170265. Epub 2018 Mar 14. Review.

9.

The importance of the photosynthetic Gibbs effect in the elucidation of the Calvin-Benson-Bassham cycle.

Ebenhöh O, Spelberg S.

Biochem Soc Trans. 2018 Feb 19;46(1):131-140. doi: 10.1042/BST20170245. Epub 2018 Jan 5. Review.

10.

A systems-wide understanding of photosynthetic acclimation in algae and higher plants.

Moejes FW, Matuszynska A, Adhikari K, Bassi R, Cariti F, Cogne G, Dikaios I, Falciatore A, Finazzi G, Flori S, Goldschmidt-Clermont M, Magni S, Maguire J, Le Monnier A, Müller K, Poolman M, Singh D, Spelberg S, Stella GR, Succurro A, Taddei L, Urbain B, Villanova V, Zabke C, Ebenhöh O.

J Exp Bot. 2017 May 17;68(11):2667-2681. doi: 10.1093/jxb/erx137. Review.

PMID:
28830099
11.

Design starch: stochastic modeling of starch granule biogenesis.

Raguin A, Ebenhöh O.

Biochem Soc Trans. 2017 Aug 15;45(4):885-893. doi: 10.1042/BST20160407. Epub 2017 Jul 3. Review.

12.

A Diverse Community To Study Communities: Integration of Experiments and Mathematical Models To Study Microbial Consortia.

Succurro A, Moejes FW, Ebenhöh O.

J Bacteriol. 2017 Jul 11;199(15). pii: e00865-16. doi: 10.1128/JB.00865-16. Print 2017 Aug 1. Review.

13.

A simplified mathematical model of directional DNA site-specific recombination by serine integrases.

Pokhilko A, Zhao J, Stark WM, Colloms SD, Ebenhöh O.

J R Soc Interface. 2017 Jan;14(126). pii: 20160618. doi: 10.1098/rsif.2016.0618.

14.

StrigoQuant: A genetically encoded biosensor for quantifying strigolactone activity and specificity.

Samodelov SL, Beyer HM, Guo X, Augustin M, Jia KP, Baz L, Ebenhöh O, Beyer P, Weber W, Al-Babili S, Zurbriggen MD.

Sci Adv. 2016 Nov 4;2(11):e1601266. eCollection 2016 Nov.

15.

Modelling Robust Feedback Control Mechanisms That Ensure Reliable Coordination of Histone Gene Expression with DNA Replication.

Christopher A, Hameister H, Corrigall H, Ebenhöh O, Müller B, Ullner E.

PLoS One. 2016 Oct 31;11(10):e0165848. doi: 10.1371/journal.pone.0165848. eCollection 2016.

16.

A mathematical model of non-photochemical quenching to study short-term light memory in plants.

Matuszyńska A, Heidari S, Jahns P, Ebenhöh O.

Biochim Biophys Acta. 2016 Dec;1857(12):1860-1869. doi: 10.1016/j.bbabio.2016.09.003. Epub 2016 Sep 12.

17.

The mechanism of ϕC31 integrase directionality: experimental analysis and computational modelling.

Pokhilko A, Zhao J, Ebenhöh O, Smith MC, Stark WM, Colloms SD.

Nucleic Acids Res. 2016 Sep 6;44(15):7360-72. doi: 10.1093/nar/gkw616. Epub 2016 Jul 7.

18.

A reductionist approach to model photosynthetic self-regulation in eukaryotes in response to light.

Matuszyńska A, Ebenhöh O.

Biochem Soc Trans. 2015 Dec;43(6):1133-9. doi: 10.1042/BST20150136. Review.

PMID:
26614650
19.

Insight into metabolic pathways of the potential biofuel producer, Paenibacillus polymyxa ICGEB2008.

Adlakha N, Pfau T, Ebenhöh O, Yazdani SS.

Biotechnol Biofuels. 2015 Sep 25;8:159. doi: 10.1186/s13068-015-0338-4. eCollection 2015.

20.

Mathematical modelling of diurnal regulation of carbohydrate allocation by osmo-related processes in plants.

Pokhilko A, Ebenhöh O.

J R Soc Interface. 2015 Mar 6;12(104):20141357. doi: 10.1098/rsif.2014.1357.

21.

Mathematical modelling of the diurnal regulation of the MEP pathway in Arabidopsis.

Pokhilko A, Bou-Torrent J, Pulido P, Rodríguez-Concepción M, Ebenhöh O.

New Phytol. 2015 May;206(3):1075-85. doi: 10.1111/nph.13258. Epub 2015 Jan 16.

22.

Taxonomic database and cut-off value for processing mcrA gene 454 pyrosequencing data by MOTHUR.

Yang S, Liebner S, Alawi M, Ebenhöh O, Wagner D.

J Microbiol Methods. 2014 Aug;103:3-5. doi: 10.1016/j.mimet.2014.05.006. Epub 2014 May 21.

PMID:
24858450
23.

Short-term acclimation of the photosynthetic electron transfer chain to changing light: a mathematical model.

Ebenhöh O, Fucile G, Finazzi G, Rochaix JD, Goldschmidt-Clermont M.

Philos Trans R Soc Lond B Biol Sci. 2014 Mar 3;369(1640):20130223. doi: 10.1098/rstb.2013.0223. Print 2014 Apr 19.

24.

Adjustment of carbon fluxes to light conditions regulates the daily turnover of starch in plants: a computational model.

Pokhilko A, Flis A, Sulpice R, Stitt M, Ebenhöh O.

Mol Biosyst. 2014 Mar 4;10(3):613-27. doi: 10.1039/c3mb70459a. Epub 2014 Jan 13.

PMID:
24413396
25.

Regulatory principles and experimental approaches to the circadian control of starch turnover.

Seaton DD, Ebenhöh O, Millar AJ, Pokhilko A.

J R Soc Interface. 2013 Dec 11;11(91):20130979. doi: 10.1098/rsif.2013.0979. Print 2014 Feb 6.

26.

Mesoscopic behavior from microscopic Markov dynamics and its application to calcium release channels.

Christian N, Skupin A, Morante S, Jansen K, Rossi G, Ebenhöh O.

J Theor Biol. 2014 Feb 21;343:102-12. doi: 10.1016/j.jtbi.2013.11.010. Epub 2013 Nov 21.

PMID:
24270093
27.

A bacterial glucanotransferase can replace the complex maltose metabolism required for starch to sucrose conversion in leaves at night.

Ruzanski C, Smirnova J, Rejzek M, Cockburn D, Pedersen HL, Pike M, Willats WG, Svensson B, Steup M, Ebenhöh O, Smith AM, Field RA.

J Biol Chem. 2013 Oct 4;288(40):28581-98. doi: 10.1074/jbc.M113.497867. Epub 2013 Aug 15.

28.

A generic rate law for surface-active enzymes.

Kartal O, Ebenhöh O.

FEBS Lett. 2013 Sep 2;587(17):2882-90. doi: 10.1016/j.febslet.2013.07.026. Epub 2013 Jul 23.

29.

The Metabolic Interplay between Plants and Phytopathogens.

Duan G, Christian N, Schwachtje J, Walther D, Ebenhöh O.

Metabolites. 2013 Jan 8;3(1):1-23. doi: 10.3390/metabo3010001.

30.

Evolutionary significance of metabolic network properties.

Basler G, Grimbs S, Ebenhöh O, Selbig J, Nikoloski Z.

J R Soc Interface. 2012 Jun 7;9(71):1168-76. doi: 10.1098/rsif.2011.0652. Epub 2011 Nov 30.

31.

Carbohydrate-active enzymes exemplify entropic principles in metabolism.

Kartal O, Mahlow S, Skupin A, Ebenhöh O.

Mol Syst Biol. 2011 Oct 25;7:542. doi: 10.1038/msb.2011.76.

32.

Systems approaches to modelling pathways and networks.

Pfau T, Christian N, Ebenhöh O.

Brief Funct Genomics. 2011 Sep;10(5):266-79. doi: 10.1093/bfgp/elr022. Epub 2011 Sep 8. Review.

PMID:
21903724
33.

Mass-balanced randomization of metabolic networks.

Basler G, Ebenhöh O, Selbig J, Nikoloski Z.

Bioinformatics. 2011 May 15;27(10):1397-403. doi: 10.1093/bioinformatics/btr145. Epub 2011 Mar 23.

34.

Modeling the complex dynamics of enzyme-pathway coevolution.

Schütte M, Skupin A, Segrè D, Ebenhöh O.

Chaos. 2010 Dec;20(4):045115. doi: 10.1063/1.3530440.

PMID:
21198127
35.

Introduction to focus issue: dynamics in systems biology.

Brackley CA, Ebenhöh O, Grebogi C, Kurths J, de Moura A, Romano MC, Thiel M.

Chaos. 2010 Dec;20(4):045101. doi: 10.1063/1.3530126.

PMID:
21198113
36.

Integration of proteomic and metabolomic profiling as well as metabolic modeling for the functional analysis of metabolic networks.

May P, Christian N, Ebenhöh O, Weckwerth W, Walther D.

Methods Mol Biol. 2011;694:341-63. doi: 10.1007/978-1-60761-977-2_21.

PMID:
21082444
37.

A minimal mathematical model of nonphotochemical quenching of chlorophyll fluorescence.

Ebenhöh O, Houwaart T, Lokstein H, Schlede S, Tirok K.

Biosystems. 2011 Feb;103(2):196-204. doi: 10.1016/j.biosystems.2010.10.011. Epub 2010 Oct 26.

PMID:
21029763
38.

Analyzing gene coexpression data by an evolutionary model.

Schütte M, Mutwil M, Persson S, Ebenhöh O.

Genome Inform. 2010;24:154-63.

39.

Analysis and prediction of nutritional requirements using structural properties of metabolic networks and support vector machines.

Tamura T, Christian N, Takemoto K, Ebenhöh O, Akutsu T.

Genome Inform. 2010 Jan;22:176-90.

40.

Co-evolution of metabolism and protein sequences.

Schütte M, Klitgord N, Segrè D, Ebenhöh O.

Genome Inform. 2010 Jan;22:156-66.

41.

Slow deactivation of ribulose 1,5-bisphosphate carboxylase/oxygenase elucidated by mathematical models.

Witzel F, Götze J, Ebenhöh O.

FEBS J. 2010 Feb;277(4):931-50. doi: 10.1111/j.1742-4658.2009.07541.x. Epub 2010 Jan 11.

42.

Ground state robustness as an evolutionary design principle in signaling networks.

Kartal O, Ebenhöh O.

PLoS One. 2009 Dec 1;4(12):e8001. doi: 10.1371/journal.pone.0008001.

43.

Assembly of an interactive correlation network for the Arabidopsis genome using a novel heuristic clustering algorithm.

Mutwil M, Usadel B, Schütte M, Loraine A, Ebenhöh O, Persson S.

Plant Physiol. 2010 Jan;152(1):29-43. doi: 10.1104/pp.109.145318. Epub 2009 Nov 4.

44.

An integrative approach towards completing genome-scale metabolic networks.

Christian N, May P, Kempa S, Handorf T, Ebenhöh O.

Mol Biosyst. 2009 Dec;5(12):1889-903. doi: 10.1039/B915913b. Epub 2009 Sep 10.

PMID:
19763335
45.

Functional classification of genome-scale metabolic networks.

Ebenhöh O, Handorf T.

EURASIP J Bioinform Syst Biol. 2009:570456. doi: 10.1155/2009/570456. Epub 2009 Mar 17.

46.

Metabolic synergy: increasing biosynthetic capabilities by network cooperation.

Christian N, Handorf T, Ebenhöh O.

Genome Inform. 2007;18:320-9.

PMID:
18546499
47.

Metabolomics- and proteomics-assisted genome annotation and analysis of the draft metabolic network of Chlamydomonas reinhardtii.

May P, Wienkoop S, Kempa S, Usadel B, Christian N, Rupprecht J, Weiss J, Recuenco-Munoz L, Ebenhöh O, Weckwerth W, Walther D.

Genetics. 2008 May;179(1):157-66. doi: 10.1534/genetics.108.088336.

48.

Biosynthetic potentials of metabolites and their hierarchical organization.

Matthäus F, Salazar C, Ebenhöh O.

PLoS Comput Biol. 2008 Apr 4;4(4):e1000049. doi: 10.1371/journal.pcbi.1000049.

49.

Biosynthetic potentials from species-specific metabolic networks.

Basler G, Nikoloski Z, Ebenhöh O, Handorf T.

Genome Inform. 2008;20:135-48.

50.

Measuring correlations in metabolomic networks with mutual information.

Numata J, Ebenhöh O, Knapp EW.

Genome Inform. 2008;20:112-22.

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