Alvarenga, E. Z. | bioRxiv Science

bioRxiv2026

No-code microbial growth phenotyping with GUIbiont

Microbial growth screens generate thousands of curves, but cross-experiment comparison and mapping growth phenotypes to genotypes or environments routinely require custom code. GUIbiont is a no-code browser application for quality control, curve fitting, clustering and metadata-linked analysis with machine learning techniques. Interactive sessions export as Julia scripts, allowing users to reproduce or extend browser analyses. Validated across 3,885 E. coli deletion strains and 13,608 defined-media curves, GUIbiont recovered known auxotrophic and nutrient-dependent phenotypes.

microbiology↗

bioRxiv2024

Kinbiont: From time series to ecological and evolutionary responses in microbial systems

Microbial behavior is quantitatively characterized by observables inferred from kinetics experiments. Growth rate and biomass yield, for example, are used to map response patterns across different conditions including antibiotic growth inhibition and yield dependence on substrate. As microbial kinetics datasets grow, there is immense potential to advance our understanding of ecological and evolutionary processes. But how can we turn these data into actionable insights about microbial responses? Here we introduce Kinbiont - an ecosystem of numerical methods integrating advanced ordinary differential equation solvers, non-linear optimization, signal processing, and interpretable machine learning algorithms. Kinbiont offers a model-based data analysis pipeline covering all aspects of microbial kinetics, from pre-processing to result interpretation. We demonstrate Kinbionts performance using synthetic and real datasets, including bacterial growth, diauxic curves, phage-bacteria co-cultures, and ecotoxicological responses. Kinbiont can aid biological discovery through data-driven generation of hypotheses that can be tested in targeted experiments.

microbiology↗