Kinetic-model-guided engineering of multipleS. cerevisiaestrains improvesp-coumaric acid production DOI Creative Commons
B Lakshmi Narayanan, Wei Jiang,

Shengbao Wang

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 17, 2024

Abstract The use of kinetic models metabolism in design-build-learn-test cycles is limited despite their potential to guide and accelerate the optimization cell factories. This primarily due difficulties constructing capable capturing complexities fermentation conditions. Building on recent advances kinetic-model-based strain design, we present rational metabolic engineering an S. cerevisiae designed overproduce p -coumaric acid ( -CA), aromatic amino with valuable nutritional therapeutic applications. To this end, built nine already engineered -CA-producing by integrating different types omics data imposing physiological constraints pertinent strain. These contained 297 mass balances involved 303 reactions across four compartments could reproduce dynamic characteristics batch simulations. We used constraint-based control analysis generate combinatorial designs 3 enzyme manipulations that increase p-CA yield glucose while ensuring resulting strains did not deviate far from reference phenotype. Among 39 unique designs, 10 proved robust phenotypic uncertainty reliably -CA nonlinear implemented these top a setting using promoter-swapping strategy for down-regulations plasmids up-regulations. Eight out ten produced higher titers than strain, 19 – 32% increases at end fermentation. Importantly, eight also maintained least 90% growth strain; indicates importance constraint. high success rate our in-silico experimental demonstrates utility design. work sets foundation accelerated design-build-test-learn large-scale as scaffold.

Language: Английский

Harnessing Aromatic Properties for Sustainable Bio-valorization of Lignin Derivatives into Flavonoids DOI Creative Commons
Siyu Zhu, Na Li, Zhihua Liu

et al.

Green Carbon, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Language: Английский

Citations

0
Microbial production systems and optimization strategies of antimicrobial peptides: a review DOI

Mengxue Lou,

Shuaiqi Ji,

Rina Wu

et al.

World Journal of Microbiology and Biotechnology, Journal Year: 2025, Volume and Issue: 41(2)

Published: Feb. 1, 2025

Language: Английский

Citations

0
Rhodotorula sp. as a promising host for microbial cell factories DOI

Baisong Tong,

Yi Yu, Shuobo Shi

et al.

Metabolic Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: March 1, 2025

Language: Английский

Citations

0
BGC heteroexpression strategy for production of novel microbial secondary metabolites DOI
Yuanyuan Liu,

Yuqi Tang,

Zhiyang Fu

et al.

Metabolic Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: March 1, 2025

Language: Английский

Citations

0
The Dawn of High-Throughput and Genome-Scale Kinetic Modeling: Recent Advances and Future Directions DOI Creative Commons

Ilias Toumpe,

Subham Choudhury, Vassily Hatzimanikatis

et al.

ACS Synthetic Biology, Journal Year: 2025, Volume and Issue: unknown

Published: April 22, 2025

Researchers have invested much effort into developing kinetic models due to their ability capture dynamic behaviors, transient states, and regulatory mechanisms of metabolism, providing a detailed realistic representation cellular processes. Historically, the requirements for parametrization significant computational resources created barriers development adoption high-throughput studies. However, recent advancements, including integration machine learning with mechanistic metabolic models, novel parameter databases, use tailor-made strategies, are reshaping field modeling. In this Review, we discuss these developments offer future directions, highlighting potential advances drive progress in systems synthetic biology, engineering, medical research at an unprecedented scale pace.

Language: Английский

Citations

0
Generative Approaches to Kinetic Parameter Inference in Metabolic Networks via Latent Space Exploration DOI
Subham Choudhury,

Ilias Toumpe,

Oussama Gabouj

et al.

Published: Jan. 1, 2025

Language: Английский

Citations

0
Recent advances in microbial synthesis of polyphenols DOI

Yuxiang Hong,

Pornpatsorn Lertphadungkit,

Yongkun Lv

et al.

Current Opinion in Biotechnology, Journal Year: 2025, Volume and Issue: 93, P. 103308 - 103308

Published: May 5, 2025

Language: Английский

Citations

0
Kinetic-model-guided engineering of multipleS. cerevisiaestrains improvesp-coumaric acid production DOI Creative Commons
B Lakshmi Narayanan, Wei Jiang,

Shengbao Wang

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 17, 2024

Abstract The use of kinetic models metabolism in design-build-learn-test cycles is limited despite their potential to guide and accelerate the optimization cell factories. This primarily due difficulties constructing capable capturing complexities fermentation conditions. Building on recent advances kinetic-model-based strain design, we present rational metabolic engineering an S. cerevisiae designed overproduce p -coumaric acid ( -CA), aromatic amino with valuable nutritional therapeutic applications. To this end, built nine already engineered -CA-producing by integrating different types omics data imposing physiological constraints pertinent strain. These contained 297 mass balances involved 303 reactions across four compartments could reproduce dynamic characteristics batch simulations. We used constraint-based control analysis generate combinatorial designs 3 enzyme manipulations that increase p-CA yield glucose while ensuring resulting strains did not deviate far from reference phenotype. Among 39 unique designs, 10 proved robust phenotypic uncertainty reliably -CA nonlinear implemented these top a setting using promoter-swapping strategy for down-regulations plasmids up-regulations. Eight out ten produced higher titers than strain, 19 – 32% increases at end fermentation. Importantly, eight also maintained least 90% growth strain; indicates importance constraint. high success rate our in-silico experimental demonstrates utility design. work sets foundation accelerated design-build-test-learn large-scale as scaffold.

Language: Английский

Citations

1