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Team:Manchester/Enzyme
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| - | + | To build our model, we first collected parameters from published literature and the online database BRENDA. In our search we discovered that published data on the value of parameters in the E.coli fatty acid biosynthesis pathway is limited. Hence, we decided to take uncertainty into account by creating probability distribution for each individual parameter. The method used to determine the distribution depended on the information available on that parameter and the parameters were categorised into 2 groups. Group 1 contained parameters with both the mean and standard deviation determined experimentally and published in the literature. In the case of group 1, both the mean and standard deviation were collected to determine the probability distribution. Group 2 contained parameters with neither the means nor the standard deviations available for the enzyme or parameter. In the case of group 2, we used the mean and standard deviation of all enzymes in the same class or subclass with known kinetic parameters. Once each parameter had a probability distribution associated with it, we randomly sampled values from each parameter distribution to run our model simulation.This was done by constructing an initial model in Copasi, using appropriate enzyme kinetic equations, and then exporting this in SBML format to PySCeS, in which a set of non-linear differential equations are used to obtain both structural and kinetic information about the system from these randomly generated kinetic values. This was repeated to create a collection of 1,000 models. From there we were able to determine the uncertainty in our model predictions: instead of a single prediction, we get a distribution of predictions from a large collection of plausible models. | |
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Revision as of 21:09, 4 October 2013
Accomplishments
We have created the first ever model based on uncertainty analysis in iGEM history, and, most importantly, made it functional. This meant we were able to get a series of predictions for the fatty acid biosynthesis pathway, with error bars detailing the extent to which we were able to be confident in our data. Analysis in this area shows that predictions for pathway output are confident. We believe that this method of modelling is an incredibly powerful tool in the investigation
Aim To use uncertainty modelling to model E.coli fatty acid biosynthesis. Early modelling attempts using traditional methods of modelling were largely unsuccessful, due to the the nature of the fatty acid biosynthesis pathway, and the lack of experimentally defined kinetic values. Rather than use models that were arbitrary or lacked information, we decided to use a less traditional method, based on Monte Carlo sampling, that can give us a clear idea of what the uncertainty of our predictions might be. By embracing this uncertainty, we hoped to create a model with practical, representative results.
Aim To use uncertainty modelling to model E.coli fatty acid biosynthesis. Early modelling attempts using traditional methods of modelling were largely unsuccessful, due to the the nature of the fatty acid biosynthesis pathway, and the lack of experimentally defined kinetic values. Rather than use models that were arbitrary or lacked information, we decided to use a less traditional method, based on Monte Carlo sampling, that can give us a clear idea of what the uncertainty of our predictions might be. By embracing this uncertainty, we hoped to create a model with practical, representative results.
To build our model, we first collected parameters from published literature and the online database BRENDA. In our search we discovered that published data on the value of parameters in the E.coli fatty acid biosynthesis pathway is limited. Hence, we decided to take uncertainty into account by creating probability distribution for each individual parameter. The method used to determine the distribution depended on the information available on that parameter and the parameters were categorised into 2 groups. Group 1 contained parameters with both the mean and standard deviation determined experimentally and published in the literature. In the case of group 1, both the mean and standard deviation were collected to determine the probability distribution. Group 2 contained parameters with neither the means nor the standard deviations available for the enzyme or parameter. In the case of group 2, we used the mean and standard deviation of all enzymes in the same class or subclass with known kinetic parameters. Once each parameter had a probability distribution associated with it, we randomly sampled values from each parameter distribution to run our model simulation.This was done by constructing an initial model in Copasi, using appropriate enzyme kinetic equations, and then exporting this in SBML format to PySCeS, in which a set of non-linear differential equations are used to obtain both structural and kinetic information about the system from these randomly generated kinetic values. This was repeated to create a collection of 1,000 models. From there we were able to determine the uncertainty in our model predictions: instead of a single prediction, we get a distribution of predictions from a large collection of plausible models.
Useful terminology
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.
Fatty Acid Biosynthesis
Fatty acid biosynthesis is a process that occurs in all living organisms. Glucose is converted into acetyl-coa through the citric acid cycle, which is fed into the fatty acid biosynthesis pathway. Here it combines with malonyl-CoA to first form a five carbon compound. The five carbon compound is then being converted into a four carbon compound via four successive steps, executed by the enzymes as indicated in Figure XXX . To this resulting C4 body, another malonyl-CoA is added to form a C7 body - which is converted the same manner as the previous C5 body. A number of unchanging enzymes act on the intermediates of this cyclic pathway to ultimately produce fatty acids. From the initial reaction to the end products the whole pathway numbers 43 reactions, about 60 metabolites and 267 parameters.Steady State
The steady state of a metabolic system is the situation where the concentrations of the pathway intermediates remain constant, although there is metabolic flux through the system.
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