Sara Moreno Paz

This study uses in-silico DBTL cycles to evaluate how process parameters affect strain optimization outcomes in metabolic engineering.

This paper applies a machine learning-assisted recommendation strategy with exploration-exploitation balance to optimize p-coumaric acid production in yeast, achieving a 137% increase over parent strains.

This study redesigns bacterial catabolism by repurposing the shikimate pathway for aromatic compound production, achieving over 89% of theoretical yield.

The paper identifies aroB as a bottleneck in pABA production in Pseudomonas putida by using combinatorial engineering and statistical modeling to optimize the shikimate pathway.

The paper uses machine learning to optimize p-coumaric acid production in yeast through DBTL cycles, achieving a 68% increase in production.

This study optimizes p-coumaric acid production in Saccharomyces cerevisiae by combining statistical experimental design with process and genetic factors, achieving a 168-fold increase in titer.

This paper optimizes curcuminoid production in Pseudomonas putida using dynamic modeling and gene expression tuning, achieving a 4.1-fold increase in efficiency.