| Summary: | <p>Abstract</p> <p>Background</p> <p><it>Pichia pastoris </it>has been recognized as an effective host for recombinant protein production. A number of studies have been reported for improving this expression system. However, its physiology and cellular metabolism still remained largely uncharacterized. Thus, it is highly desirable to establish a systems biotechnological framework, in which a comprehensive <it>in silico </it>model of <it>P. pastoris </it>can be employed together with high throughput experimental data analysis, for better understanding of the methylotrophic yeast's metabolism.</p> <p>Results</p> <p>A fully compartmentalized metabolic model of <it>P. pastoris </it>(<it>iPP</it>668), composed of 1,361 reactions and 1,177 metabolites, was reconstructed based on its genome annotation and biochemical information. The constraints-based flux analysis was then used to predict achievable growth rate which is consistent with the cellular phenotype of <it>P. pastoris </it>observed during chemostat experiments. Subsequent <it>in silico </it>analysis further explored the effect of various carbon sources on cell growth, revealing sorbitol as a promising candidate for culturing recombinant <it>P. pastoris </it>strains producing heterologous proteins. Interestingly, methanol consumption yields a high regeneration rate of reducing equivalents which is substantial for the synthesis of valuable pharmaceutical precursors. Hence, as a case study, we examined the applicability of <it>P. pastoris </it>system to whole-cell biotransformation and also identified relevant metabolic engineering targets that have been experimentally verified.</p> <p>Conclusion</p> <p>The genome-scale metabolic model characterizes the cellular physiology of <it>P. pastoris</it>, thus allowing us to gain valuable insights into the metabolism of methylotrophic yeast and devise possible strategies for strain improvement through <it>in silico </it>simulations. This computational approach, combined with synthetic biology techniques, potentially forms a basis for rational analysis and design of <it>P. pastoris </it>metabolic network to enhance humanized glycoprotein production.</p>
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