Abstract
BackgroundConstraint-based models enable structured cellular representations in which intracellular kinetics are circumvented. These models, combined with experimental data, are useful analytical tools to estimate the state exhibited (the phenotype) by the cells at given pseudo-steady conditions.ResultsIn this contribution, a simplified constraint-based stoichiometric model of the metabolism of the yeast Pichia pastoris, a workhorse for heterologous protein expression, is validated against several experimental available datasets. Firstly, maximum theoretical growth yields are calculated and compared to the experimental ones. Secondly, possibility theory is applied to quantify the consistency between model and measurements. Finally, the biomass growth rate is excluded from the datasets and its prediction used to exemplify the capability of the model to calculate non-measured fluxes.ConclusionsThis contribution shows how a small-sized network can be assessed following a rational, quantitative procedure even when measurements are scarce and imprecise. This approach is particularly useful in lacking data scenarios.
Highlights
Constraint-based models enable structured cellular representations in which intracellular kinetics are circumvented
The collection of biochemical reactions involved in the metabolism of a cell can be assembled in networks in order to carry out studies under a system-level approach [1]
We introduce a set of measurements constraints ( ) considering imprecision, as in (3), but substituting em by two pairs of non-negative decision variables: This degree of possibility provides an indication of the consistency between model ( ) and measurements ( ): a possibility equal to one must be interpreted as complete agreement between the model and the original measurements; lower values of possibility imply that certain error in the measurements is needed to find a flux vector fulfilling the model constraints
Summary
Constraint-based models enable structured cellular representations in which intracellular kinetics are circumvented. The collection of biochemical reactions involved in the metabolism of a cell can be assembled in networks in order to carry out studies under a system-level approach [1]. Such analysis have been done with large, even genome-scale, reconstructions of well-characterised organisms such as Escherichia coli, Saccharomyces cerevisiae, Pseudomonas putida [2,3,4], and with simpler networks that consider only a few key metabolites [5,6,7]. A constraint-based model can be assembled [11,12]
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