2026-10-14T16:56:08Z https://api.figshare.com/v2/oai

oai:figshare.com:article/34074011 2026-10-05T17:55:45Z category_7 category_13 category_21 category_39 category_734 category_931 category_811 category_132 portal_5 item_type_3 month_year_10_2026

<p>Model parameters and terminology.</p> Sarah Sundius (25317320) Jennifer Farrell (1796587) Kelly L. Eick (25317323) Rachel Kuske (16738230) Sam P. Brown (11156568) Medicine Genetics Biotechnology Ecology Biological Sciences not elsewhere classified Information Systems not elsewhere classified Mathematical Sciences not elsewhere classified Infectious Diseases targeting pathogen cells quantifies classic rate pseudomonas aeruginosa </ g ., mic div >< p biological time series based fitting algorithm quantifying antibiotic susceptibility led computational pipeline 7 inoculum sizes classifies transient dynamics based model parameterization clustering approach suggest saturating antibiotic pipeline identifies inoculum effects approach utilizing novel metrics modern medicine loss term dependent effects controlled perturbations clinical infections bacterial populations 12 doses <div><p>Antibiotics are a cornerstone of modern medicine, targeting pathogen cells by disrupting essential cellular processes. However, standard antibiotic susceptibility metrics (e.g., MIC) and textbook models neglect transient dynamics and density-dependent effects, despite their ubiquity in nature. In clinical infections, where bacterial populations are the units we treat, this can increase the risk of under treatment. To address this gap, we generate high resolution optical density time series data for <i>Pseudomonas aeruginosa</i> (3 antibiotics, 12 doses, 7 inoculum sizes, 4x replication), enabling gradient estimation and gradient-based model parameterization. We develop a dynamics-led computational pipeline that (1) evaluates population scale ordinary differential equation models in the context of estimated time derivative data, and (2) classifies transient dynamics in dose-inoculum space using unsupervised clustering. Applied to our data, the pipeline identifies an ordinary differential equation model with a saturating antibiotic-loss term and a threshold-dependent weak Allee term that recapitulates and quantifies classic rate, yield, and inoculum effects of antibiotics. In addition, our model and clustering approach suggest a set of novel metrics, defining thresholds separating distinct dynamical regimes. Beyond antibiotic data sets, our approach utilizing a derivative-based fitting algorithm and clustering of derivative trajectories is applicable to any biological time series with controlled perturbations and variable initial conditions.</p></div> 2026-10-05T17:47:55Z Dataset Dataset 10.1371/journal.pcbi.1014827.t001 https://figshare.com/articles/dataset/_p_Model_parameters_and_terminology_p_/34074011 CC BY 4.0