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Team:Manchester/Enzyme
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| - | + | <p><u><b> Uncertainty: </u></b></p> | |
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| + | <p> In synthetic biology two main classes of computational models are commonly used: constraint-based genome-scale models and differential-equation-based dynamic models. In our project, we employed the latter approach, because we are interested in the concentrations of compounds and their dynamic changes, which cannot be predicted using purely constraint-based models. We also wanted to identify the reactions and corresponding enzymes with the highest control over the fatty acid synthesis pathway; again, this is not possible with constraint-based models. </p> | ||
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| + | <p> However, for a dynamic model one needs to know the enzyme kinetic parameters, and these are often unknown or very unreliable for enzymes of fatty acid biosynthesis. <b> We wanted to account for the resulting uncertainty using a new “uncertainty modelling” approach, which can potentially serve as a principled approach to handling parameter uncertainty in the future. </p> | ||
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| + | <p> Building models with incorporated acknowledgment of uncertainty will produce specified confidence intervals for all model predictions and thus could lead to robust design of engineered cellular machines of fatty acid synthesis and beyond. </p> | ||
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Revision as of 20:00, 4 October 2013
Uncertainty:
In synthetic biology two main classes of computational models are commonly used: constraint-based genome-scale models and differential-equation-based dynamic models. In our project, we employed the latter approach, because we are interested in the concentrations of compounds and their dynamic changes, which cannot be predicted using purely constraint-based models. We also wanted to identify the reactions and corresponding enzymes with the highest control over the fatty acid synthesis pathway; again, this is not possible with constraint-based models.
However, for a dynamic model one needs to know the enzyme kinetic parameters, and these are often unknown or very unreliable for enzymes of fatty acid biosynthesis. We wanted to account for the resulting uncertainty using a new “uncertainty modelling” approach, which can potentially serve as a principled approach to handling parameter uncertainty in the future.
Building models with incorporated acknowledgment of uncertainty will produce specified confidence intervals for all model predictions and thus could lead to robust design of engineered cellular machines of fatty acid synthesis and beyond.
