Ville Mustonen

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.

This study uses machine learning to predict multi-strain microbial community growth from monoculture data, finding that simpler models often suffice.

The paper evaluates fairness in microbial community assembly by comparing observed biomass distribution to a game-theoretic fair distribution based on Shapley values across 56 community outcomes.

This study uses symbolic regression to discover microbial growth models from data, finding that models balancing data fit, parsimony, and biological relevance include linear approximations and Monod dynamics.

This paper characterizes genome-environment interactions in Klebsiella pneumoniae using chemical genomics and machine learning to predict fitness across diverse conditions.

This study tracks four-year microbial community evolution in two environments, showing how adaptive mutations in one species can cause abrupt community state shifts.

The paper investigates non-ergodicity in ecological and evolutionary dynamics through case studies, showing how stochasticity affects population growth, genotype fitness, and cooperation evolution.

This study examines genome-wide evolution in bacterial prey under ciliate predation, finding that clade dynamics remain similar to those in non-predated populations despite phenotypic and mutational changes.

This study shows that total carbon availability, not resource complexity, drives tristability in a synthetic microbial community, with lag phase duration predicting competitive success.

The paper shows antibiotic history shapes microbial response through eco-evolutionary and environmental feedbacks, creating a trade-off between resistance and recovery.

This paper identifies long deletion signatures in repetitive regions as markers of microsatellite instability and develops a classifier to detect MSI in low-quality sequencing data.

The paper quantifies microbial growth variability across spatial locations using machine learning and develops a spatial consumer-resource model to explain growth patterns and infer intrinsic growth parameters.

This paper identifies mutational signatures caused by formalin fixation and introduces FFPEsig, a method to correct these artefacts in genome sequencing data.

This paper develops a multiobjective optimization method to identify cancer-selective drug combinations, validated in BRAF-V600E melanoma, showing improved selectivity through combinatorial targeting.

This paper predicts antibiotic resistance in E. coli from whole genome sequences using machine learning, achieving high accuracy without prior knowledge of resistance mechanisms.

The paper identifies natural genetic variants associated with longevity in fission yeast by analyzing segregant pools and genome sequencing during aging.

The paper investigates how standing and de novo genetic variants contribute to drug adaptation in yeast, finding they act on shared molecular targets but via different mechanisms.

The paper shows that the Drosophila genome has two modes of evolution: one with limited linkage effects and one with clonal competition, linked to recombination rates.

The paper investigates how clonal heterogeneity and genetic variation influence antimicrobial resistance in yeast, showing that both pre-existing and de novo mutations contribute to fitness and adaptation.

This paper introduces a method to identify genetic drivers of multiple phenotypes in malaria parasites by combining LGS, sequencing, and mathematical modeling, demonstrating its effectiveness in detecting novel alleles for growth rate and strain-specific immunity.

The paper investigates how recombination rates influence adaptive evolution in Drosophila, finding higher adaptive substitution rates in high-recombining regions and significant collateral fitness costs in low-recombining regions.