Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Erol S. Kavvas"'
Experimental Evolution Reveals Unifying Systems-Level Adaptations but Diversity in Driving Genotypes
Autor:
Erol S. Kavvas, Christopher P. Long, Anand Sastry, Saugat Poudel, Maciek R. Antoniewicz, Yang Ding, Elsayed T. Mohamed, Richard Szubin, Jonathan M. Monk, Adam M. Feist, Bernhard O. Palsson
Publikováno v:
mSystems, Vol 7, Iss 6 (2022)
ABSTRACT Genotype-fitness maps of evolution have been well characterized for biological components, such as RNA and proteins, but remain less clear for systems-level properties, such as those of metabolic and transcriptional regulatory networks. Here
Externí odkaz:
https://doaj.org/article/583e4c50343e475a9bd6506e2053a0bd
Publikováno v:
Nature Communications, Vol 11, Iss 1, Pp 1-11 (2020)
Current machine learning classifiers have been applied to whole-genome sequencing data to identify determinants of antimicrobial resistance, but they lack interpretability. Here the authors present a metabolic machine learning classifier that uses fl
Externí odkaz:
https://doaj.org/article/975d3eec652849ec8a3f9bfe2f5ad9c0
Autor:
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
Publikováno v:
Nature Communications, Vol 9, Iss 1, Pp 1-9 (2018)
Mycobacterium tuberculosis exhibits complex evolution of antimicrobial resistance (AMR). Here, the authors perform machine learning and structural analysis to identify signatures of AMR evolution to 13 antibiotics.
Externí odkaz:
https://doaj.org/article/43c5a44a956f4bc3a0b1035a0afc669c
Autor:
Angela Zhang, Mateo M. Cepeda, Erol S. Kavvas, Anna E. Case, Ilias Tagkopoulos, Morgan M. Matson, Austin L Carroll, Shota Atsumi, Xiaokang Wang
Publikováno v:
Metabolic Engineering. 69:50-58
Previously, Escherichia coli was engineered to produce isobutyl acetate (IBA). Titers greater than the toxicity threshold (3 g/L) were achieved by using layer-assisted production. To avoid this costly and complex method, adaptive laboratory evolution
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f90143dde478523079166a3a3911df4b
https://doi.org/10.1016/j.ymben.2021.11.002
https://doi.org/10.1016/j.ymben.2021.11.002
Publikováno v:
PLoS Computational Biology, Vol 16, Iss 3, p e1007608 (2020)
The evolution of antimicrobial resistance (AMR) poses a persistent threat to global public health. Sequencing efforts have already yielded genome sequences for thousands of resistant microbial isolates and require robust computational tools to system
Externí odkaz:
https://doaj.org/article/468c84af76484b478dab2caa61ca1172