Metab. Eng.

Machine learning predicts system-wide metabolic flux control in cyanobacteria.

Kugler A, Stensjö K

Metab. Eng. 82 (-) 171-182 [2024-03-00; online 2024-02-21]

Bioinformatics Support for Computational Resources [Service]

Fine-tuning of p-coumaric acid synthesis to increase (2S)-naringenin production in yeast.

Mao J, Mohedano MT, Fu J, ..., Siewers V, Chen Y

Metab. Eng. 79 (-) 192-202 [2023-09-00; online 2023-08-22]

Chalmers Mass Spectrometry Infrastructure [Service]

Suppressors of amyloid-β toxicity improve recombinant protein production in yeast by reducing oxidative stress and tuning cellular metabolism.

Chen X, Li X, Ji B, ..., Siewers V, Engqvist MKM

Metab. Eng. 72 (-) 311-324 [2022-07-00; online 2022-05-01]

Glycoproteomics and MS Proteomics [Service]

Genome scale metabolic modeling of cancer.

Nilsson A, Nielsen J

Metab. Eng. 43 (Pt B) 103-112 [2017-09-00; online 2016-11-04]

Bioinformatics Support, Infrastructure and Training [Technology development] Systems Biology [Technology development]

Evolutionary engineering reveals divergent paths when yeast is adapted to different acidic environments.

Fletcher E, Feizi A, Bisschops MMM, ..., Siewers V, Nielsen J

Metab. Eng. 39 (-) 19-28 [2017-01-00; online 2016-11-02]

Bioinformatics Support for Computational Resources [Service] NGI Stockholm (Genomics Applications) [Service] NGI Stockholm (Genomics Production) [Service] NGI Uppsala (Uppsala Genome Center) [Service] National Genomics Infrastructure [Service]

Evolution reveals a glutathione-dependent mechanism of 3-hydroxypropionic acid tolerance.

Kildegaard KR, Hallström BM, Blicher TH, ..., Nielsen J, Borodina I

Metab. Eng. 26 (-) 57-66 [2014-11-00; online 2014-09-30]

NGI Stockholm (Genomics Applications) NGI Stockholm (Genomics Production) National Genomics Infrastructure