Mono- and Polyauxic Growth Kinetics: A Semi-Mechanistic Framework for Complex Biological Dyanmics
by Gustavo Mockaitis
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Understanding how microbes grow in complex mixtures, like those in bioenergy and waste valorization, is tricky. Current math models are either too basic or demand impractical amounts of data. This study introduces a smart, open-source tool that bridges the gap. It breaks down messy, multi-phase growth curves into clear, overlapping steps. By using automated algorithms to filter bad data and find the best fit, it pulls real biological insights, such as true growth rates and delay times, straight from standard, easy-to-collect observations. It’s a reliable way to turn everyday reactor data into deep, actionable understanding.
Unified semi-mechanistic framework for polyauxic microbial growth analysis. Experimental biomass-versus-time data, illustrated with a chemostat context, are processed through a modeling pipeline that reformulates canonical sigmoidal equations, estimates parameters by global and local optimization, and performs model selection. The output is an overall fitted curve decomposed into individual growth phases, yielding interpretable phase-specific kinetic parameters such as maximum growth rate and lag time.
