ArticlePloS one2025
Comprehensive and deep learning classification for analyses of the biological complexity of growth and biofilms of Cobetia marina under different temperature growths.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Thermal modulation of transcriptional states, biofilm architecture and survival strategies inFrontiers in microbiology · 2026Article
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7 authors.
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Abstract
backgroundBiological complexity represents a fundamental challenge in understanding microbial behavior, particularly when analyzing heterogeneous data from bacterial growth and biofilm formation. Traditional models often reduce data dispersion at the cost of losing biological interpretation, limiting their applicability to real-world scenarios.
methodsWe investigated biological complexity using Cobetia marina as a model organism, conducting comprehensive studies on growth kinetics and biofilm formation across a wide temperature range (8 ∘C to 41 ∘C). Mutant strains were generated using pUTmini-Tn5-Km transposons to study phenotypic variations independent of environmental variables. High-throughput screening was performed using 96-well microplates to ensure adequate experimental replication. Data analysis employed advanced mathematical techniques, including semi-automatic bi- and tri-classifiers, a novel fractional derivative method for growth classification, and SPOCU (Scaled Polynomial Constant Unit) for biofilm formation.
resultsWe successfully developed classification systems to distinguish growth kinetics at minimum, optimal, and maximum temperatures. A neural network incorporating the SPOCU (Scaled Polynomial Constant Unit) transfer function demonstrated superior performance compared to conventional classifiers (SELU and RELU) in predicting biofilm production. The fractional derivative method proved effective in addressing key challenges in bi- and tri- classifier systems for temperature-dependent growth analysis.
conclusionsThis study demonstrates the effectiveness of advanced computational approaches in analyzing biological complexity. The integration of deep learning methods with comprehensive experimental design provides a robust framework for understanding microbial behavior under varying environmental conditions, with potential applications in biotechnology and environmental monitoring.
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