Juan Nogales

ML4SD improves microbial biomanufacturing by combining active learning and high-throughput search to optimize growth-coupled production with fewer design iterations.

This paper introduces PROMETEO, a genetic-circuit framework that programs microbial population composition through asymmetric circuits, enabling functional organization and efficient biotransformation.

GALLANT automates genome-scale metabolic model generation and contextualization, enabling analysis of microbial consortia and population heterogeneity using multi-omics data.

EMBER identifies adaptive metabolic reactions in bacteria by integrating transcriptomics and flux analysis, revealing differences in resource allocation strategies across species.

This study explores underground metabolism in Pseudomonas putida to identify novel metabolic traits through adaptive laboratory evolution, revealing how promiscuous enzymatic activities contribute to metabolic innovation.

This paper develops a microbial platform in E. coli to produce glycosylated flavonoids using a phosphate-responsive promoter and optimized gene expression.

The paper introduces FROG analysis to standardize reproducibility assessments of genome-scale metabolic models, resulting in full reproducibility of all tested models.

The paper introduces Golden Standard, a modular cloning tool compatible with various bacterial hosts, enabling standardized genetic circuit assembly and validated for performance and portability.

Standard-GEM provides a template for reproducible genome-scale metabolic model development following FAIR principles and software best practices.

This paper constructs a genome-scale metabolic model of Bdellovibrio bacteriovorus to analyze its growth-phase dependent metabolic states and provides insights into its biphasic lifestyle and metabolic robustness.

Memote, open-source Python software, runs a community-maintained, partly scored test suite (consistency, biomass, annotations, optional experimental data) on SBML-encoded genome-scale metabolic models, once or through continuous integration.

This study expands the computable metabolic network of Pseudomonas putida, revealing metabolic cycles that enhance robustness and providing insights for biocatalyst engineering and drug target discovery.