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David Heckmann

Showing results (1-10 of 24) with videos related to

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Biochemical Society Transactions|November 29, 2015
Modelling metabolic evolution on phenotypic fitness landscapes: a case study on C4 photosynthesisDavid Heckmann
Current Opinion in Plant Biology|May 7, 2016
C4 photosynthesis evolution: the conditional Mt. FujiDavid Heckmann
Nature Communications|December 12, 2018
Modeling genome-wide enzyme evolution predicts strong epistasis underlying catalytic turnover ratesDavid Heckmann, Daniel C Zielinski, Bernhard O Palsson
Molecular Plant|May 3, 2017
Machine Learning Techniques for Predicting Crop Photosynthetic Capacity from Leaf Reflectance SpectraDavid Heckmann, Urte Schlüter, Andreas P M Weber
Scientific Reports|August 6, 2021
Modeling photosynthetic resource allocation connects physiology with evolutionary environmentsEsther M Sundermann, Martin J Lercher, David Heckmann
Frontiers in Plant Science|September 29, 2017
BLISTER Regulates Polycomb-Target Genes, Represses Stress-Regulated Genes and Promotes Stress Responses in <i>Arabidopsis thaliana</i>Julia A Kleinmanns, Nicole Schatlowski, David Heckmann, et al.
Journal of Experimental Botany|September 24, 2016
Combining genetic and evolutionary engineering to establish C4 metabolism in C3 plantsYuanyuan Li, David Heckmann, Martin J Lercher, et al.
Plos Biology|October 19, 2021
Deep learning allows genome-scale prediction of Michaelis constants from structural featuresAlexander Kroll, Martin K M Engqvist, David Heckmann, et al.
Nature Communications|May 24, 2020
A biochemically-interpretable machine learning classifier for microbial GWASErol S Kavvas, Laurence Yang, Jonathan M Monk, et al.
Plos Computational Biology|February 2, 2021
Independent component analysis recovers consistent regulatory signals from disparate datasetsAnand V Sastry, Alyssa Hu, David Heckmann, et al.
Pageof 3

Showing results (1-10 of 24) with videos related to

Sort By:
Pageof 3
Biochemical Society Transactions|November 29, 2015
Modelling metabolic evolution on phenotypic fitness landscapes: a case study on C4 photosynthesisDavid Heckmann
Current Opinion in Plant Biology|May 7, 2016
C4 photosynthesis evolution: the conditional Mt. FujiDavid Heckmann
Nature Communications|December 12, 2018
Modeling genome-wide enzyme evolution predicts strong epistasis underlying catalytic turnover ratesDavid Heckmann, Daniel C Zielinski, Bernhard O Palsson
Molecular Plant|May 3, 2017
Machine Learning Techniques for Predicting Crop Photosynthetic Capacity from Leaf Reflectance SpectraDavid Heckmann, Urte Schlüter, Andreas P M Weber
Scientific Reports|August 6, 2021
Modeling photosynthetic resource allocation connects physiology with evolutionary environmentsEsther M Sundermann, Martin J Lercher, David Heckmann
Frontiers in Plant Science|September 29, 2017
BLISTER Regulates Polycomb-Target Genes, Represses Stress-Regulated Genes and Promotes Stress Responses in <i>Arabidopsis thaliana</i>Julia A Kleinmanns, Nicole Schatlowski, David Heckmann, et al.
Journal of Experimental Botany|September 24, 2016
Combining genetic and evolutionary engineering to establish C4 metabolism in C3 plantsYuanyuan Li, David Heckmann, Martin J Lercher, et al.
Plos Biology|October 19, 2021
Deep learning allows genome-scale prediction of Michaelis constants from structural featuresAlexander Kroll, Martin K M Engqvist, David Heckmann, et al.
Nature Communications|May 24, 2020
A biochemically-interpretable machine learning classifier for microbial GWASErol S Kavvas, Laurence Yang, Jonathan M Monk, et al.
Plos Computational Biology|February 2, 2021
Independent component analysis recovers consistent regulatory signals from disparate datasetsAnand V Sastry, Alyssa Hu, David Heckmann, et al.
Pageof 3