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Growing a Cystic Fibrosis-Relevant Polymicrobial Biofilm to Probe Community Phenotypes
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Structural Identifiability and Observability of Microbial Community Models.

Sandra Díaz-Seoane1, Elena Sellán1, Alejandro F Villaverde1,2

  • 1Department of Systems Engineering & Control, Universidade de Vigo, 36310 Vigo, Spain.

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|April 28, 2023
PubMed
Summary
This summary is machine-generated.

This study assessed the identifiability and observability of ordinary differential equation (ODE) models for microbial communities. Some models are reliable, but many lack structural identifiability or observability for accurate predictions.

Keywords:
dynamic modellingidentifiabilitymicrobial communitiesobservabilitysystems biology

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Area of Science:

  • Microbiology and Systems Biology
  • Mathematical Modeling of Biological Systems

Background:

  • Biological communities involve interacting species; microbial communities are crucial in biotechnology and biomedicine.
  • Ordinary differential equation (ODE) models describe microbial community dynamics but often lack verified identifiability and observability.
  • Structural identifiability and observability are essential for reliable model predictions and parameter/state inference.

Purpose of the Study:

  • To analyze the structural identifiability and observability of common microbial community ODE models.
  • To determine the suitability of various modeling frameworks for experimental data analysis.
  • To identify limitations of current models under typical experimental conditions.

Main Methods:

  • Systematic analysis of structural identifiability and observability for main families of microbial community ODE models.
  • Evaluation of over one hundred different model configurations considering various dimensions and measurements.
  • Assessment of theoretical possibility to infer model parameters and internal states from observed outputs.

Main Results:

  • Identified specific microbial community ODE models that are fully structurally identifiable and observable.
  • Demonstrated that a significant number of models are structurally unidentifiable and/or unobservable under common experimental settings.
  • Highlighted the impact of model structure and measurement strategies on identifiability and observability.

Conclusions:

  • Not all ODE models for microbial communities guarantee reliable predictions due to potential lack of identifiability/observability.
  • The study provides guidance on selecting appropriate modeling frameworks for microbial community research.
  • Researchers should carefully consider model properties to avoid compromised predictions in biotechnological and biomedical applications.