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Pharmaceuticals 2018, 11(2), 31;

Computational Modelling of Large Scale Phage Production Using a Two-Stage Batch Process

Department of Chemical Engineering, The University of Melbourne, Parkville 3010, Australia
The Bio21 Institute, 30 Flemington Rd, The University of Melbourne, Parkville 3052, Australia
Author to whom correspondence should be addressed.
Received: 5 January 2018 / Revised: 5 March 2018 / Accepted: 27 March 2018 / Published: 8 April 2018
(This article belongs to the Special Issue Phage Therapy and Phage-Mediated Biological Control)
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Cost effective and scalable methods for phage production are required to meet an increasing demand for phage, as an alternative to antibiotics. Computational models can assist the optimization of such production processes. A model is developed here that can simulate the dynamics of phage population growth and production in a two-stage, self-cycling process. The model incorporates variable infection parameters as a function of bacterial growth rate and employs ordinary differential equations, allowing application to a setup with multiple reactors. The model provides simple cost estimates as a function of key operational parameters including substrate concentration, feed volume and cycling times. For the phage and bacteria pairing examined, costs and productivity varied by three orders of magnitude, with the lowest cost found to be most sensitive to the influent substrate concentration and low level setting in the first vessel. An example case study of phage production is also presented, showing how parameter values affect the production costs and estimating production times. The approach presented is flexible and can be used to optimize phage production at laboratory or factory scale by minimizing costs or maximizing productivity. View Full-Text
Keywords: phage production; modelling; population dynamics phage production; modelling; population dynamics

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Krysiak-Baltyn, K.; Martin, G.J.O.; Gras, S.L. Computational Modelling of Large Scale Phage Production Using a Two-Stage Batch Process. Pharmaceuticals 2018, 11, 31.

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