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Modeling antibiotic resistance in the microbiota using multi-level Petri Nets.

Roberta Bardini1, Stefano Di Carlo2, Gianfranco Politano2

  • 1Politecnico di Torino, Control and Computer Engineering Department, Corso Duca degli Abruzzi 24, Torino, 10129, Italy. roberta.bardini@polito.it.

BMC Systems Biology
|November 23, 2018
PubMed
Summary
This summary is machine-generated.

Unregulated antibiotic use drives resistance. This study models antibiotic resistance dynamics at population and microbiota levels using the Nets-Within-Nets formalism, aiding in developing better prevention strategies.

Keywords:
Antibiotic resistanceComputational systems biologyHuman microbiotaHybrid modelsNets-Within-NetsPetri Nets

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Area of Science:

  • Computational Biology
  • Microbiology
  • Epidemiology

Background:

  • Unregulated antibiotic use in clinical settings and agriculture accelerates the emergence of antibiotic-resistant bacterial strains.
  • Antibiotic resistance dynamics can be studied at multiple scales, from population-level spread to molecular mechanisms within bacteria.
  • Individual health and resistance spread are influenced by societal antibiotic policies and the complex interactions within the host-associated microbiota.

Purpose of the Study:

  • To present the Nets-Within-Nets (NWN) formalism as a tool for modeling antibiotic resistance.
  • To demonstrate the application of NWN in simulating resistance dynamics at both population and individual microbiota levels.
  • To showcase the flexibility of NWN in integrating diverse data for complex biological system modeling.

Main Methods:

  • Utilized the Nets-Within-Nets (NWN) formalism for computational modeling.
  • Modeled dynamics between antibiotic administration protocols and antibiotic resistance.
  • Applied the model to individual microbiota and host population levels.

Main Results:

  • Successfully modeled antibiotic resistance dynamics using the NWN formalism across different scales.
  • Demonstrated the integration of heterogeneous information within a single NWN model.
  • Simulations allowed for the explicit consideration of timing and stochastic events in resistance development.

Conclusions:

  • The NWN formalism provides an efficient method for modeling antibiotic resistance population dynamics at various levels of detail.
  • This approach facilitates the investigation of causal relationships and mechanisms underlying antibiotic resistance.
  • The NWN modeling approach serves as a valuable tool for decision-making and the development of antibiotic stewardship protocols.