Identification of a Dynamic Metabolic Flux Model for a Mammalian Cell Culture

Mariana Carvalho1, Ali Nikdel1, Jeremiah Riesberg2, Delia Lyons2, Hector M. Budman3

  • 1University of Waterloo
  • 2MilliporeSigma - A business of Merck KGaA
  • 3Univ. of Waterloo

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Category

Regular Session

Sessions

10:00 - 11:40 | Wed 24 Apr | Baia Norte | WeA3

Metabolic Network Analysis

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Abstract

A Dynamic Metabolic Flux Model (DMFM) is a constraint-based approach where a cell is assumed to act as an optimizing agent that allocates resources to maximize/minimize a suitable biological objective. In this modeling approach, a linear programming (LP) problem solves the flux vector, at each time interval, by optimizing an objective function subject to constraints. An important advantage of dynamic metabolic flux models, as compared to other modelling approaches reported before, is that DMFM models typically require a smaller number of calibration parameters to fit/predict the data. The ultimate purpose of this research was to identify a DMFM model with a minimal number of parameters to fit and predict experimental data for batch operation of a mammalian cell culture. Two main objectives were pursued in this work: (i) - to identify a biologically meaningful objective function that mammalian cells are trying to maximize/minimize by comparing different candidates and (ii) - to systematically find a minimal set of limiting constraints. The limiting constraints were found from the values of the Lagrange multipliers of an optimization problem where the data was described by set based bounds. The developed DMFM model for CHO cells was in good agreement with data. The selected objective function involves the simultaneous maximization of growth combined with minimization of apoptosis and the limiting constraints were found to be associated to six metabolites (alanine, glutamate, lactate, ammonia, glycine, and glutamine).

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