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Bioaugmentation Approaches for Suppression of Antibiotic Resistance: Model-Based Design

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Emerging Applications of Control and Systems Theory

Part of the book series: Lecture Notes in Control and Information Sciences - Proceedings ((LNCOINSPRO))

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Abstract

We present a systems modelling investigation of a bioaugmentation approach to suppression of antibiotic resistance. Bioaugmentation is the manipulation of an environment by the addition of biological agents. We investigate a strategy for limiting the threat of antibiotic-resistant bacterial pathogens by delivery of engineered genetic elements to a target pathogen population. This genetic payload can either trigger cell death or suppress the expression of antibiotic resistance genes and then be passed on to other cells. We present both deterministic (ordinary differential equation) and stochastic (master equation-based) models of the proposed strategy, using the mammalian gut as a representative environment. We provide a stability analysis of the deterministic model, along with an investigation of the potential behaviours of the model through simulation over a range of parameterizations and a global sensitivity analysis. Our focus is on the role of the design parameters in achieving a successful treatment.

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Authors and Affiliations

  1. University of Waterloo, Waterloo, Canada

    Aida Ahmadzadegan, Abdullah Hamadeh, Midhun Kathanaruparambil Sukumaran & Brian Ingalls

Authors
  1. Aida Ahmadzadegan
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  2. Abdullah Hamadeh
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  3. Midhun Kathanaruparambil Sukumaran
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  4. Brian Ingalls
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Corresponding author

Correspondence to Brian Ingalls .

Editor information

Editors and Affiliations

  1. IEIIT—CNR, Politecnico di Torino, Turin, Italy

    Roberto Tempo

  2. Department of Systems Engineering, University of Texas at Dallas, Richardson, Texas, USA

    Stephen Yurkovich

  3. Russ Engineering Center, Wright State University, Dayton, Ohio, USA

    Pradeep Misra

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Ahmadzadegan, A., Hamadeh, A., Sukumaran, M.K., Ingalls, B. (2018). Bioaugmentation Approaches for Suppression of Antibiotic Resistance: Model-Based Design. In: Tempo, R., Yurkovich, S., Misra, P. (eds) Emerging Applications of Control and Systems Theory. Lecture Notes in Control and Information Sciences - Proceedings. Springer, Cham. https://doi.org/10.1007/978-3-319-67068-3_14

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  • DOI: https://doi.org/10.1007/978-3-319-67068-3_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67067-6

  • Online ISBN: 978-3-319-67068-3

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