Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Lars Schrübbers"'
Autor:
Mareike Bongers, Jordi Perez-Gil, Mark P Hodson, Lars Schrübbers, Tune Wulff, Morten OA Sommer, Lars K Nielsen, Claudia E Vickers
Publikováno v:
eLife, Vol 9 (2020)
Volatile isoprenoids produced by plants are emitted in vast quantities into the atmosphere, with substantial effects on global carbon cycling. Yet, the molecular mechanisms regulating the balance between volatile and non-volatile isoprenoid productio
Externí odkaz:
https://doaj.org/article/3da291e036554f8f9d8e3953b74937f2
Autor:
Markus J. Herrgård, Bo Burla, Lars Schrübbers, Douglas McCloskey, Pasquale Colaianni, Lars K. Nielsen, Oliver Kohlbacher, Timo Sachsenberg, Federico Torta, Mette Kristensen, Hannes L. Röst, Svetlana Kutuzova, Oliver Alka
Publikováno v:
Kutuzova, S, Colaianni, P D, Röst, H, Sachsenberg, T, Alka, O, Kohlbacher, O, Burla, B, Torta, F, Schrübbers, L, Kristensen, M, Nielsen, L, Herrgård, M J & McCloskey, D 2020, ' SmartPeak Automates Targeted and Quantitative Metabolomics Data Processing ', Analytical Chemistry, vol. 94, no. 24, pp. 15968–15974 . https://doi.org/10.1021/acs.analchem.0c03421
SmartPeak is an application that encapsulates advanced algorithms to enable fast, accurate, and automated processing of CE-, GC- and LC-MS(/MS) data, and HPLC data for targeted and semi-targeted metabolomics, lipidomics, and fluxomics experiments.Hig
Autor:
Jie Zhang, Lea G. Hansen, Olga Gudich, Konrad Viehrig, Lærke M. M. Lassen, Lars Schrübbers, Khem B. Adhikari, Paulina Rubaszka, Elena Carrasquer-Alvarez, Ling Chen, Vasil D’Ambrosio, Beata Lehka, Ahmad K. Haidar, Saranya Nallapareddy, Konstantina Giannakou, Marcos Laloux, Dushica Arsovska, Marcus A. K. Jørgensen, Leanne Jade G. Chan, Mette Kristensen, Hanne B. Christensen, Suresh Sudarsan, Emily A. Stander, Edward Baidoo, Christopher J. Petzold, Tune Wulff, Sarah E. O’Connor, Vincent Courdavault, Michael K. Jensen, Jay D. Keasling
Publikováno v:
Nature
Zhang, J, Hansen, L G, Gudich, O, Viehrig, K, Lassen, L M M, Schrübbers, L, Adhikari, K B, Rubaszka, P, Carrasquer-Alvarez, E, Chen, L, D’Ambrosio, V, Lehka, B, Haidar, A K, Nallapareddy, S, Giannakou, K, Laloux, M, Arsovska, D, Jørgensen, M A K, Chan, L J G, Kristensen, M, Christensen, H B, Sudarsan, S, Stander, E A, Baidoo, E, Petzold, C J, Wulff, T, O’Connor, S E, Courdavault, V, Jensen, M K & Keasling, J D 2022, ' A microbial supply chain for production of the anti-cancer drug vinblastine ', Nature, vol. 609, no. 7926, pp. 341-347 . https://doi.org/10.1038/s41586-022-05157-3
Nature, vol 609, iss 7926
Zhang, J, Hansen, L G, Gudich, O, Viehrig, K, Lassen, L M M, Schrübbers, L, Adhikari, K B, Rubaszka, P, Carrasquer-Alvarez, E, Chen, L, D’Ambrosio, V, Lehka, B, Haidar, A K, Nallapareddy, S, Giannakou, K, Laloux, M, Arsovska, D, Jørgensen, M A K, Chan, L J G, Kristensen, M, Christensen, H B, Sudarsan, S, Stander, E A, Baidoo, E, Petzold, C J, Wulff, T, O’Connor, S E, Courdavault, V, Jensen, M K & Keasling, J D 2022, ' A microbial supply chain for production of the anti-cancer drug vinblastine ', Nature, vol. 609, no. 7926, pp. 341-347 . https://doi.org/10.1038/s41586-022-05157-3
Nature, vol 609, iss 7926
Monoterpene indole alkaloids (MIAs) are a diverse family of complex plant secondary metabolites with many medicinal properties, including the essential anti-cancer therapeutics vinblastine and vincristine1. As MIAs are difficult to chemically synthes
Publikováno v:
McCloskey, D, Xu, J, Schrübbers, L, Christensen, H B & Herrgård, M J 2018, ' RapidRIP quantifies the intracellular metabolome of 7 industrial strains of E. coli ', Metabolic Engineering, vol. 47, pp. 383-392 . https://doi.org/10.1016/j.ymben.2018.04.009
Metabolic Engineering
Metabolic Engineering
Fast metabolite quantification methods are required for high throughput screening of microbial strains obtained by combinatorial or evolutionary engineering approaches. In this study, a rapid RIP-LC-MS/MS (RapidRIP) method for high-throughput quantit
Autor:
Douglas McCloskey, Allison J. Lopatkin, Lars Schrübbers, Sarah N Wright, Graham C. Walker, Sangeeta Satish, Bernhard O. Palsson, Amir Nili, Miguel A. Alcantar, Meagan Hamblin, Jason H. Yang, James J. Collins
Publikováno v:
PMC
Cell
Yang, J H, Wright, S N, Hamblin, M, McCloskey, D, Alcantar, M A, Schrübbers, L, Lopatkin, A J, Satish, S, Nili, A, Palsson, B O, Walker, G C & Collins, J J 2019, ' A White-Box Machine Learning Approach for Revealing Antibiotic Mechanisms of Action ', Cell, vol. 177, no. 6, pp. 1649-1661.e9 . https://doi.org/10.1016/j.cell.2019.04.016
Cell
Yang, J H, Wright, S N, Hamblin, M, McCloskey, D, Alcantar, M A, Schrübbers, L, Lopatkin, A J, Satish, S, Nili, A, Palsson, B O, Walker, G C & Collins, J J 2019, ' A White-Box Machine Learning Approach for Revealing Antibiotic Mechanisms of Action ', Cell, vol. 177, no. 6, pp. 1649-1661.e9 . https://doi.org/10.1016/j.cell.2019.04.016
© 2019 Elsevier Inc. Current machine learning techniques enable robust association of biological signals with measured phenotypes, but these approaches are incapable of identifying causal relationships. Here, we develop an integrated “white-box”
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a17b6e733d1b694dcad766ea0231ee40
https://hdl.handle.net/1721.1/135182
https://hdl.handle.net/1721.1/135182