BayFlux: A Bayesian method to quantify metabolic Fluxes and their uncertainty at the genome scale.

Autor: Tyler W H Backman, Christina Schenk, Tijana Radivojevic, David Ando, Jahnavi Singh, Jeffrey J Czajka, Zak Costello, Jay D Keasling, Yinjie Tang, Elena Akhmatskaya, Hector Garcia Martin

Publikováno v: PLoS Computational Biology, Vol 19, Iss 11, p e1011111 (2023)

Metabolic fluxes, the number of metabolites traversing each biochemical reaction in a cell per unit time, are crucial for assessing and understanding cell function. 13C Metabolic Flux Analysis (13C MFA) is considered to be the gold standard for measu

Externí odkaz: https://doaj.org/article/23460991878e4f2daf19048745ca008c

Combining mechanistic and machine learning models for predictive engineering and optimization of tryptophan metabolism

Autor: Jie Zhang, Søren D. Petersen, Tijana Radivojevic, Andrés Ramirez, Andrés Pérez-Manríquez, Eduardo Abeliuk, Benjamín J. Sánchez, Zak Costello, Yu Chen, Michael J. Fero, Hector Garcia Martin, Jens Nielsen, Jay D. Keasling, Michael K. Jensen

Publikováno v: Nature Communications, Vol 11, Iss 1, Pp 1-13 (2020)

In metabolic engineering, mechanistic models require prior metabolism knowledge of the chassis strain, whereas machine learning models need ample training data. Here, the authors combine the mechanistic and machine learning models to improve predicti

Externí odkaz: https://doaj.org/article/12b7d69682c04fc38cfe252ad2aedc1b

Industrial brewing yeast engineered for the production of primary flavor determinants in hopped beer

Autor: Charles M. Denby, Rachel A. Li, Van T. Vu, Zak Costello, Weiyin Lin, Leanne Jade G. Chan, Joseph Williams, Bryan Donaldson, Charles W. Bamforth, Christopher J. Petzold, Henrik V. Scheller, Hector Garcia Martin, Jay D. Keasling

Publikováno v: Nature Communications, Vol 9, Iss 1, Pp 1-10 (2018)

Production of aromatic monoterpene molecules in hop flowers is affected by genetic, environmental, and processing factors. Here, the authors engineer brewer’s yeast for the production of linalool and geraniol, and show pilot-scale beer produced by

Externí odkaz: https://doaj.org/article/dc211afb41564846a4e2eccbdbf109cd

Combining mechanistic and machine learning models for predictive engineering and optimization of tryptophan metabolism

Autor: Michael Krogh Jensen, Jens Nielsen, Jie Zhang, Eduardo Abeliuk, Zak Costello, Andrés Ramirez, Andrés Pérez-Manríquez, Benjamin Sanchez, Søren D. Petersen, Jay D. Keasling, Hector Garcia Martin, Michael J. Fero, Tijana Radivojevic, Yu Chen

Publikováno v: Nature Communications, Vol 11, Iss 1, Pp 1-13 (2020)
Nature Communications
Nature communications, vol 11, iss 1
Zhang, J, Petersen, S D, Radivojevic, T, Ramirez, A, Pérez-Manríquez, A, Abeliuk, E, Sánchez, B J, Costello, Z, Chen, Y, Fero, M J, Martin, H G, Nielsen, J, Keasling, J D & Jensen, M K 2020, ' Combining mechanistic and machine learning models for predictive engineering and optimization of tryptophan metabolism ', Nature Communications, vol. 11, no. 1, 4880 . https://doi.org/10.1038/s41467-020-17910-1

Through advanced mechanistic modeling and the generation of large high-quality datasets, machine learning is becoming an integral part of understanding and engineering living systems. Here we show that mechanistic and machine learning models can be c

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::54e52e82bd0714a12e1be42785be8cff
http://link.springer.com/article/10.1038/s41467-020-17910-1

Investigation of Bar-seq as a method to study population dynamics of Saccharomyces cerevisiae deletion library during bioreactor cultivation

Autor: Aindrila Mukhopadhyay, Maren Wehrs, Carolina Barcelos, Jay D. Keasling, Jadie Moon, Mitchell G. Thompson, Zak Costello, Deepanwita Banerjee, Patrick M. Shih, Jan-Philip Prahl, Norma M. Morella, Deepti Tanjore

Publikováno v: Microbial cell factories, vol 19, iss 1
Microbial Cell Factories
Microbial Cell Factories, Vol 19, Iss 1, Pp 1-15 (2020)

Background Despite the latest advancements in metabolic engineering for genome editing and characterization of host performance, the successful development of robust cell factories used for industrial bioprocesses and accurate prediction of the behav

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::27840ba9399929b983819dfd75f33b75
https://escholarship.org/uc/item/5z05g1xt