Zobrazeno 1 - 10
of 166
pro vyhledávání: '"Martin J. Lercher"'
Publikováno v:
Nature Communications, Vol 14, Iss 1, Pp 1-14 (2023)
Abstract The turnover number k cat, a measure of enzyme efficiency, is central to understanding cellular physiology and resource allocation. As experimental k cat estimates are unavailable for the vast majority of enzymatic reactions, the development
Externí odkaz:
https://doaj.org/article/fa44e10401ad43b7b67e9df37de3e6e7
Publikováno v:
Nature Communications, Vol 14, Iss 1, Pp 1-13 (2023)
Abstract For most proteins annotated as enzymes, it is unknown which primary and/or secondary reactions they catalyze. Experimental characterizations of potential substrates are time-consuming and costly. Machine learning predictions could provide an
Externí odkaz:
https://doaj.org/article/9a14800df9344922b342fa032ed19bcb
Publikováno v:
mSystems, Vol 8, Iss 5 (2023)
ABSTRACT Understanding the allocation of the cellular proteome to different cellular processes is central to unraveling the organizing principles of bacterial physiology. Proteome allocation to protein translation itself is maximally efficient, i.e.,
Externí odkaz:
https://doaj.org/article/bdd10d4aefcf4039a9e4dbd870bdac51
Autor:
Itai Yanai, Martin J. Lercher
Publikováno v:
Genome Biology, Vol 23, Iss 1, Pp 1-8 (2022)
Externí odkaz:
https://doaj.org/article/c96b8b6be91c44a8976500907552fd0e
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
Abstract The regulation of resource allocation in biological systems observed today is the cumulative result of natural selection in ancestral and recent environments. To what extent are observed resource allocation patterns in different photosynthet
Externí odkaz:
https://doaj.org/article/228f401034474625ae9f21e1a08f1b3b
Publikováno v:
Nature Communications, Vol 11, Iss 1, Pp 1-10 (2020)
The protein translation machinery is the most expensive cellular subsystem in fast growing bacteria. Providing a detailed mechanistic model for this complex system, the authors show that the translation machinery components are expressed such that th
Externí odkaz:
https://doaj.org/article/8368f1712aa4497b9baaa3f01b8450b4
Autor:
Hugo Dourado, Martin J. Lercher
Publikováno v:
Nature Communications, Vol 11, Iss 1, Pp 1-14 (2020)
Genome-scale models of microbial metabolism largely ignore reaction kinetics. Here, the authors develop a general mathematical framework for modeling cellular growth with explicit non-linear reaction kinetics and use it to glean insights into the pri
Externí odkaz:
https://doaj.org/article/a2c6498bcef043a6befe71c2f267545f
Publikováno v:
PLoS Biology, Vol 19, Iss 10 (2021)
Much recent progress has been made to understand the impact of proteome allocation on bacterial growth; much less is known about the relationship between the abundances of the enzymes and their substrates, which jointly determine metabolic fluxes. He
Externí odkaz:
https://doaj.org/article/0e3306f0e85b4afeb0e9973df3446271
Publikováno v:
Scientific Reports, Vol 9, Iss 1, Pp 1-9 (2019)
Abstract Computational predictions of double gene knockout effects by flux balance analysis (FBA) have been used to characterized genome-wide patterns of epistasis in microorganisms. However, it is unclear how in silico predictions are related to in
Externí odkaz:
https://doaj.org/article/c0f1bab04c3e40feba14e3a4d52be1dc
Autor:
David Heckmann, Colton J. Lloyd, Nathan Mih, Yuanchi Ha, Daniel C. Zielinski, Zachary B. Haiman, Abdelmoneim Amer Desouki, Martin J. Lercher, Bernhard O. Palsson
Publikováno v:
Nature Communications, Vol 9, Iss 1, Pp 1-10 (2018)
Experimental data on enzyme turnover numbers is sparse and noisy. Here, the authors use machine learning to successfully predict enzyme turnover numbers for E. coli, and show that using these to parameterize mechanistic genome-scale models enhances t
Externí odkaz:
https://doaj.org/article/4253a943492247b28ad5cd46f32d93b3