Paula Jouhten

In silico prediction of metabolic trait robustness in microbial cells

This paper introduces TRAEV, a computational framework to predict the evolutionary robustness of metabolic traits in microbial cells, validated using yeast strains in controlled environments.

ActSeekN: A Structural-Motif-Based Pipeline for Interpretable Enzyme Function Annotation

ActSeekN improves enzyme function annotation by using structural motifs from AlphaFold and UniProt data, outperforming existing methods in yeast, human, and Trichoderma reesei proteomes.

Adaptive evolution of engineered Saccharomyces cerevisiae in favored and unusual chemical environments

This study investigates how Darwinian selection affects engineered traits in yeast by evolving strains with pigment production pathways under different carbon sources.

New-to-nature PHA synthase design using deep learning

This study uses a conditional variational autoencoder to design new-to-nature PHA synthases, which are validated for activity in yeast and show potential for expanding PHA material properties.

Spontaneous mutation rate in Saccharomyces cerevisiae is lower in nutritional and genotypic conditions reducing specific growth rate

This study shows that spontaneous mutation rates in yeast decrease under various nutritional and genotypic conditions that reduce growth rate.

Influence of ploidy and genetic background on stress tolerance of intraspecific yeast hybrids

This study investigates how ploidy and genetic background influence stress tolerance in yeast hybrids, finding condition-dependent effects on heterosis.

Compound-specific DNA adduct profiling with nanopore sequencing and IonStats

This study develops IonStats, a toolkit for profiling DNA adducts using nanopore sequencing, revealing compound-specific effects on sequencing data.

Highly parallelized laboratory evolution of wine yeasts for enhanced metabolic phenotypes

This study develops a parallelized ALE platform to evolve wine yeasts with enhanced metabolic traits, finding that expanded replicates and lineages improve the chances of evolving improved strains with desirable industrial performance.

Genome-scale metabolic models reveal determinants of phenotypic differences in non-Saccharomyces yeasts

The paper uses genome-scale metabolic models to identify metabolic differences between non-Saccharomyces yeasts, finding that Complex I in the electron transport chain distinguishes Metschnikowia pulcherrima from other species.

Reconstruction of compartmentalized genome-scalemetabolic models using deep learning for over 800fungi

The paper develops a deep learning method to reconstruct compartmentalized genome-scale metabolic models for over 800 fungi, improving predictions of metabolic phenotypes and phylogenetic clustering.

Operation of a TCA cycle subnetwork in the mammalian nucleus

The paper shows that parts of the TCA cycle operate in the nucleus, challenging the canonical view of metabolic compartmentalization.

Strain design optimization using reinforcement learning

This paper uses multi-agent reinforcement learning to optimize microbial strain production, showing improved convergence and stability in synthetic biology applications.

Predictive evolution of metabolic phenotypes using model-designed selection niches

This paper predicts the evolution of fitness-costly traits by designing selection niches using metabolic models and validates it in yeast for increased aroma compound secretion.