Modeling photosynthetic resource allocation connects physiology with evolutionary environments

por Esther M. Sundermann, Martin J. Lercher, David Heckmann
Publicado 2021

Modeling genome-wide enzyme evolution predicts strong epistasis underlying catalytic turnover rates

por David Heckmann, Daniel C. Zielinski, Bernhard O. Palsson
Publicado 2018

Deep learning allows genome-scale prediction of Michaelis constants from structural features.

por Alexander Kroll, Martin K M Engqvist, David Heckmann, Martin J Lercher
Publicado 2021

A biochemically-interpretable machine learning classifier for microbial GWAS

por Erol S. Kavvas, Laurence Yang, Jonathan M. Monk, David Heckmann, Bernhard O. Palsson
Publicado 2020

Machine learning applied to enzyme turnover numbers reveals protein structural correlates and improves metabolic models

por David Heckmann, Colton J. Lloyd, Nathan Mih, Yuanchi Ha, Daniel C. Zielinski, Zachary B. Haiman, Abdelmoneim Amer Desouki, Martin J. Lercher, Bernhard O. Palsson
Publicado 2018

Machine learning and structural analysis of Mycobacterium tuberculosis pan-genome identifies genetic signatures of antibiotic resistance

por Erol S. Kavvas, Edward Catoiu, Nathan Mih, James T. Yurkovich, Yara Seif, Nicholas Dillon, David Heckmann, Amitesh Anand, Laurence Yang, Victor Nizet, Jonathan M. Monk, Bernhard O. Palsson
Publicado 2018