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.

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