An analytical theory of balanced cellular growth

par Hugo Dourado, Martin J. Lercher
Publié 2020

Modeling photosynthetic resource allocation connects physiology with evolutionary environments

par Esther M. Sundermann, Martin J. Lercher, David Heckmann
Publié 2021

Flux balance analysis with or without molecular crowding fails to predict two thirds of experimentally observed epistasis in yeast

par Deya Alzoubi, Abdelmoneim Amer Desouki, Martin J. Lercher
Publié 2019

On the optimality of the enzyme-substrate relationship in bacteria.

par Hugo Dourado, Matteo Mori, Terence Hwa, Martin J Lercher
Publié 2021

On the optimality of the enzyme–substrate relationship in bacteria

par Hugo Dourado, Matteo Mori, Terence Hwa, Martin J. Lercher
Publié 2021

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

par Alexander Kroll, Martin K M Engqvist, David Heckmann, Martin J Lercher
Publié 2021

Energy efficiency trade-offs drive nucleotide usage in transcribed regions

par Wei-Hua Chen, Guanting Lu, Peer Bork, Songnian Hu, Martin J. Lercher
Publié 2016

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

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

Adaptive evolution of complex innovations through stepwise metabolic niche expansion

par Balázs Szappanos, Jonathan Fritzemeier, Bálint Csörgő, Viktória Lázár, Xiaowen Lu, Gergely Fekete, Balázs Bálint, Róbert Herczeg, István Nagy, Richard A. Notebaart, Martin J. Lercher, Csaba Pál, Balázs Papp
Publié 2016