Identifiability, Sensitivity, and Genetic Algorithms in Bacterial Biofilm Selection Models
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915467660099584 |
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| author | Williams, Stephen Cheam, Daravuth Nishiguchi, Michele K. Sindi, Suzanne S. Khatri, Shilpa Rutter, Erica M. |
| author_facet | Williams, Stephen Cheam, Daravuth Nishiguchi, Michele K. Sindi, Suzanne S. Khatri, Shilpa Rutter, Erica M. |
| contents | Bacteria often develop distinct phenotypes to adapt to environmental stress. In particular, they can produce biofilms, dense communities of bacteria that live in a complex extracellular matrix. Bacterial biofilms provide a safe haven from environmental conditions by distributing metabolic workload and allowing them to perform complex multicellular processes. While previous studies have investigated how bacterial biofilms are regulated under laboratory conditions, they have not considered (1) the data requirements necessary to estimate model parameters and (2) how bacteria respond to recurring stressors in their natural habitats. To address (1), we adapted a mechanistic population model to explore the dynamics of biofilm formation in the presence of predator stress, using synthetic data. We used a Maximum Likelihood Estimation framework to measure crucial parameters underpinning the biofilm formation dynamics. We used genetic algorithms to propose an optimal data collection schedule that minimised parameter identifiability confidence interval widths. Our sensitivity analysis revealed that we could simplify the binding dynamics and eliminate biofilm detachment. To address (2), we proposed a structured version of our model to capture the long-term behaviour and evolutionary selection. In our extended model, the subpopulations feature different maximal rates of biofilm formation. We compared the selection under different predator types and amounts and identified key parameters that affected the speed of selection via sensitivity analysis. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2508_20219 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Identifiability, Sensitivity, and Genetic Algorithms in Bacterial Biofilm Selection Models Williams, Stephen Cheam, Daravuth Nishiguchi, Michele K. Sindi, Suzanne S. Khatri, Shilpa Rutter, Erica M. Quantitative Methods Populations and Evolution Bacteria often develop distinct phenotypes to adapt to environmental stress. In particular, they can produce biofilms, dense communities of bacteria that live in a complex extracellular matrix. Bacterial biofilms provide a safe haven from environmental conditions by distributing metabolic workload and allowing them to perform complex multicellular processes. While previous studies have investigated how bacterial biofilms are regulated under laboratory conditions, they have not considered (1) the data requirements necessary to estimate model parameters and (2) how bacteria respond to recurring stressors in their natural habitats. To address (1), we adapted a mechanistic population model to explore the dynamics of biofilm formation in the presence of predator stress, using synthetic data. We used a Maximum Likelihood Estimation framework to measure crucial parameters underpinning the biofilm formation dynamics. We used genetic algorithms to propose an optimal data collection schedule that minimised parameter identifiability confidence interval widths. Our sensitivity analysis revealed that we could simplify the binding dynamics and eliminate biofilm detachment. To address (2), we proposed a structured version of our model to capture the long-term behaviour and evolutionary selection. In our extended model, the subpopulations feature different maximal rates of biofilm formation. We compared the selection under different predator types and amounts and identified key parameters that affected the speed of selection via sensitivity analysis. |
| title | Identifiability, Sensitivity, and Genetic Algorithms in Bacterial Biofilm Selection Models |
| topic | Quantitative Methods Populations and Evolution |
| url | https://arxiv.org/abs/2508.20219 |