|
|
| (8 intermediate revisions not shown) |
| Line 5: |
Line 5: |
| | Related Links:</div> | | Related Links:</div> |
| | <div id="boxcontent"><DL> | | <div id="boxcontent"><DL> |
| - | <div class="heading">Vitamin A:</div>
| |
| - | <DD><a href="https://2011.igem.org/Team:Johns_Hopkins/Project/VitA">Project</a><br/>
| |
| - | <DD><a href="#">Parts</a><br/>
| |
| - | <DD><a href="#">Protocols</a><br/></br/></DL>
| |
| | <div class="heading">Modeling:</div><DL> | | <div class="heading">Modeling:</div><DL> |
| | <DD><a href="https://2011.igem.org/Team:Johns_Hopkins/Modeling/Platforms">Modeling Platforms</a><br/> | | <DD><a href="https://2011.igem.org/Team:Johns_Hopkins/Modeling/Platforms">Modeling Platforms</a><br/> |
| | <DD><a href="https://2011.igem.org/Team:Johns_Hopkins/Modeling/LBSMod">LBS Models</a><br/> | | <DD><a href="https://2011.igem.org/Team:Johns_Hopkins/Modeling/LBSMod">LBS Models</a><br/> |
| | <DD><a href="https://2011.igem.org/Team:Johns_Hopkins/Modeling/Opt">Optimization</a><br/> | | <DD><a href="https://2011.igem.org/Team:Johns_Hopkins/Modeling/Opt">Optimization</a><br/> |
| - | <DD><a href="https://2011.igem.org/Team:Johns_Hopkins/Modeling/Sensitivity">Sensitivity</a><br/> | + | <DD><a href="https://2011.igem.org/Team:Johns_Hopkins/Modeling/Sensitivity">Sensitivity</a><br/></DL> |
| - | </br/></DL>
| + | |
| | </div> | | </div> |
| | </div> | | </div> |
| Line 22: |
Line 17: |
| | ===== Parameter fitting ===== | | ===== Parameter fitting ===== |
| | While a model takes known qualitative information and organizes it systematically, parameter-fitting allows us to compare models with measured data, giving our models predictive power. | | While a model takes known qualitative information and organizes it systematically, parameter-fitting allows us to compare models with measured data, giving our models predictive power. |
| - | Parameter fitting can be construed as an optimization problem. Here, we attempt to minimize an error function between our model and our observations. A common error function is the squared error: | + | Parameter fitting can be construed as an optimization problem. Here, we attempt to minimize an error function between our model and our observations. A common error function is the root-mean-squared error, which we plan to minimize using a number of nonlinear optimization algorithms. |
| | | | |
| - | \[||simulation-observation||{_{2}}^{2}=\sum_{i,j}^{N}(simulation_{i})^{2}-(observation_{j})^{2}\]
| + | Using the SimBiology Toolbox in Matlab, we have access to a convenient graphical interface for parameter fitting. Matlab uses the Levenberg–Marquardt Algorithm (LMA), which minimizes the least-squares error function mentioned above. While fairly robust, LMA has two drawbacks: it finds only a local minimum of the error function, and it assumes no measurement error in the measurements parameters. Nonetheless, it is a popular method that works well on most models. |
| | | | |
| - | While mathematically elegant, it does not explicitly bound how badly a particular point might be fit. To ensure that even our worst points are reasonably well-fit by the model, we use the minimax error function:
| + | We are currently awaiting the result of experiments to quantify the production of vitamins and other products using [https://2011.igem.org/Team:Johns_Hopkins/Notebook/VitProtocol#Spectroscopy spectroscopy] and [https://2011.igem.org/Team:Johns_Hopkins/Notebook/VitProtocol#High_Performance_Liquid_Chromatography_.28HPLC.29 HPLC]. Once the data is available, our modeling group will be able to format the data and fit our model using LMA and minimax optimization. |
| | | | |
| - | \[max_{i,j}[(simulation_{i})^{2}-(observation_{j})^{2}]\]
| |
| | | | |
| - | In addition using the SimBiology Toolbox in Matlab, we have access to a convenient graphical interface for parameter fitting. Matlab uses the Levenberg–Marquardt Algorithm (LMA), which minimizes the least-squares error function mentioned above. While fairly robust, LMA has two drawbacks: it finds only a local minimum of the error function, and it assumes no measurement error in the measurements parameters. Nonetheless, it is a popular method that works well on most models.
| + | <html></div> |
| - | | + | |
| - | We are currently awaiting the result of experiments to quantify the production of vitamins and other products using [https://2011.igem.org/Team:Johns_Hopkins/Notebook/VitProtocol#Spectroscopy spectroscopy] and [[lab:protocols#HPLC|HPLC]]. Once the data is available, our modeling group will be able to format the data and fit our model using LMA and minimax optimization.
| + | |
Parameter fitting
While a model takes known qualitative information and organizes it systematically, parameter-fitting allows us to compare models with measured data, giving our models predictive power.
Parameter fitting can be construed as an optimization problem. Here, we attempt to minimize an error function between our model and our observations. A common error function is the root-mean-squared error, which we plan to minimize using a number of nonlinear optimization algorithms.
Using the SimBiology Toolbox in Matlab, we have access to a convenient graphical interface for parameter fitting. Matlab uses the Levenberg–Marquardt Algorithm (LMA), which minimizes the least-squares error function mentioned above. While fairly robust, LMA has two drawbacks: it finds only a local minimum of the error function, and it assumes no measurement error in the measurements parameters. Nonetheless, it is a popular method that works well on most models.
We are currently awaiting the result of experiments to quantify the production of vitamins and other products using spectroscopy and HPLC. Once the data is available, our modeling group will be able to format the data and fit our model using LMA and minimax optimization.
"
