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Team:Calgary/Modelling/Method
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<div class="heading"> | <div class="heading"> | ||
DIFFERENTIAL EQUATIONS MODELLING METHODS | DIFFERENTIAL EQUATIONS MODELLING METHODS | ||
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<div class="desc"> | <div class="desc"> | ||
| + | <img src="http://i1001.photobucket.com/albums/af132/igemcalgary/Mo.gif" align="left"> | ||
The simbiology interface from Matlab was used to simulate the differential equations model. Chemical Kinetic equations were used to build the model for simulation. | The simbiology interface from Matlab was used to simulate the differential equations model. Chemical Kinetic equations were used to build the model for simulation. | ||
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<b>Sundial Solver</b> | <b>Sundial Solver</b> | ||
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<br><br> | <br><br> | ||
| - | When sundials solver is selected, the program selects one of | + | The sundial solver (SUNDIALS) was developed so that robust time integrators and non-linear solvers can be easily combined with already existing simulation codes. Minimal information from user is required and this solver allow users to easily supply their own data structures. The Sundials solvers are part of a third-party package developed at Lawrence Livermore National Laboratory. Built-in ordinary differential equation (ODE) solvers (ode45 and ode15s) are also part of the interface. More more information about sundials within Simbiology, please visit the Matlab website. |
| - | + | <br><br> | |
| + | When sundials solver is selected, the program selects one of the two sundials solvers that suits your model: CVODE or IDA. CVODE is used for systems of ODEs (stiff or nonstiff) and this type of solver is usually used for a model that has no algebraic rules. IDA is a differential-algebraic equation (DAE) solver and it is usually used when there is one more algebraic rules. Since our model incorporates an event (the addition of autoinducer-II (AI-2)), this type of solver was used in our model. More information can be found here: https://computation.llnl.gov/casc/sundials/description/description.html | ||
| + | <br> | ||
| + | ***Information provided by Matlab website | ||
| + | <br><br> | ||
| + | <b>Rationale</b> | ||
| + | <br><br> | ||
| + | The following initial conditions and k constants are estimated values. The modelling team was unable to find much literature regarding rate constants and initial conditions for our signalling system. With that being said, this model still serves a purpose in understanding the signalling pathway. We were able to demonstrate various trends when AI-2 and expression of specific proteins were adjusted. The output when inputs were changed showed significant trends that may be taken into consideration when the laboratory work is performed. This model provides a good starting place for understanding of the basic signalling system. Further research and work will be dedicated to provide a more accurate model. The main focus for this team is to find significant trends that will effect the AI-2 signalling cascade. | ||
| + | <br><br> | ||
| + | During the past few months, the modelling team developed the AI-2 signalling circuit within Simbiology. The following is a diagram that simplifies the AI-2 signalling cascade with interactions of different species: | ||
| + | <br><br> | ||
| + | <center > <div class="heading">AI-2 Signalling System Map Developed</div> </center> | ||
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| + | <a href ="https://static.igem.org/mediawiki/2009/c/c6/Dig1.jpg"> <img src = "https://static.igem.org/mediawiki/2009/c/c6/Dig1.jpg" height="325px" alt =" click to view full size "> </a> | ||
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<div class="desc"> | <div class="desc"> | ||
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| + | The following parameters were assigned values based on the assumptions made from the knowledge of the system. The values are choosen on the basis of a comparison to each other. | ||
<center><b>Table: Initial Values of the Species in the System</b> </center> | <center><b>Table: Initial Values of the Species in the System</b> </center> | ||
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<td>AI-2</td> | <td>AI-2</td> | ||
<td>0</td> | <td>0</td> | ||
| - | <td><align = "left">Initially the amount of AI-2 is constant at 0. After an | + | <td><align = "left">Initially the amount of AI-2 is constant at 0. After an equilibrium is established variable amounts of AI-2 are added at different simulations. <br><br> </td> |
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<td>LuxU:p</td> | <td>LuxU:p</td> | ||
<td>2</td> | <td>2</td> | ||
| - | <td> | + | <td>-</td> |
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<td>LuxO:p</td> | <td>LuxO:p</td> | ||
<td>2</td> | <td>2</td> | ||
| - | <td>Equal amounts of LuxO:p and LuxU:p | + | <td>Equal amounts of LuxO:p and LuxU:p were considered in the model . LuxU:p phosphorylates LuxO . This phosphorylation reaction is considered to be a fast reaction therefore there are equal amounts of the two proteins present.<br><br></td> |
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<td>p</td> | <td>p</td> | ||
<td>10.0658</td> | <td>10.0658</td> | ||
| - | <td> | + | <td>An assumption is made that there is enough p is the environment that it doesn’t become a limiting factor. For that reason we assign p as a constant value in simbiology. (It doesn’t really matter that the initial amount is presented as a comparatively small number in this case. ) <br><br></td> |
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<td>sigma54:LuxO:p:Pqrr4</td> | <td>sigma54:LuxO:p:Pqrr4</td> | ||
<td>0.63</td> | <td>0.63</td> | ||
| - | <td> There is only 1 copy of Pqrr4 present in each cell . | + | <td> There is only 1 copy of Pqrr4 present in each cell. In the reaction equations Pqrr4 is shared between 3 equations therefore we decided to break the concentration of Pqrr4 between 3 species: sigma54:LuxO:p:Pqrr4 , Pqrr4 , sigma54:Pqrr4 . The initial values of the three species add up to one. The fractions of the Pqrr4 combination species are weighted differently . Since the Pqrr4 promotor stays on most of the time we decided the sigma54:LuxO:p:Pqrr4 complex should recieve the most weight. Pqrr4 is assumed to stay unbound from any complex for the least amount of time therefore Pqrr4 initial amount is the smallest. <br><br></td> |
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<td>GFP</td> | <td>GFP</td> | ||
<td> 0</td> | <td> 0</td> | ||
| - | <td> The model assumes that initially we have no GFP present | + | <td> The model assumes that initially we have no GFP present . <br><br></td> |
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<td> kPhosU</td> | <td> kPhosU</td> | ||
<td>1.0E-6</td> | <td>1.0E-6</td> | ||
| - | <td> | + | <td>Fitted to Data</td> |
</tr> | </tr> | ||
<tr> | <tr> | ||
<td>kPhosO</td> | <td>kPhosO</td> | ||
<td>1.0E-6</td> | <td>1.0E-6</td> | ||
| - | <td> | + | <td>FItted to Data</td> |
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<td>kOPqrr4Unbind</td> | <td>kOPqrr4Unbind</td> | ||
<td>1.0</td> | <td>1.0</td> | ||
| - | <td>The binding and unbinding is assumed to be a fast reaction | + | <td>The binding and unbinding is assumed to be a fast reaction having equal probability of staying in both states.</td> |
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<td>kRNAdegrad</td> | <td>kRNAdegrad</td> | ||
<td>0.0048</td> | <td>0.0048</td> | ||
| - | <td> | + | <td>Bba_F2620 experience page</td> |
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</table> | </table> | ||
Latest revision as of 03:46, 22 October 2009
UNIVERSITY OF CALGARY
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MODELLING INDEX
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DIFFERENTIAL EQUATIONS MODELLING METHODS
The simbiology interface from Matlab was used to simulate the differential equations model. Chemical Kinetic equations were used to build the model for simulation.
 
k is the kinetic rate constant. The size of k will determine the speed of the reaction. A smaller value of k will produce a slow reaction rate while a larger value of k will produce a fast reaction rate. [A] is the amount of reactant A present. The simulations were run for 50000 seconds . It was considered to be enough time for the system to reach equilibrium after disturbance. Sundial Solver The sundial solver (SUNDIALS) was developed so that robust time integrators and non-linear solvers can be easily combined with already existing simulation codes. Minimal information from user is required and this solver allow users to easily supply their own data structures. The Sundials solvers are part of a third-party package developed at Lawrence Livermore National Laboratory. Built-in ordinary differential equation (ODE) solvers (ode45 and ode15s) are also part of the interface. More more information about sundials within Simbiology, please visit the Matlab website. When sundials solver is selected, the program selects one of the two sundials solvers that suits your model: CVODE or IDA. CVODE is used for systems of ODEs (stiff or nonstiff) and this type of solver is usually used for a model that has no algebraic rules. IDA is a differential-algebraic equation (DAE) solver and it is usually used when there is one more algebraic rules. Since our model incorporates an event (the addition of autoinducer-II (AI-2)), this type of solver was used in our model. More information can be found here: https://computation.llnl.gov/casc/sundials/description/description.html ***Information provided by Matlab website Rationale The following initial conditions and k constants are estimated values. The modelling team was unable to find much literature regarding rate constants and initial conditions for our signalling system. With that being said, this model still serves a purpose in understanding the signalling pathway. We were able to demonstrate various trends when AI-2 and expression of specific proteins were adjusted. The output when inputs were changed showed significant trends that may be taken into consideration when the laboratory work is performed. This model provides a good starting place for understanding of the basic signalling system. Further research and work will be dedicated to provide a more accurate model. The main focus for this team is to find significant trends that will effect the AI-2 signalling cascade. During the past few months, the modelling team developed the AI-2 signalling circuit within Simbiology. The following is a diagram that simplifies the AI-2 signalling cascade with interactions of different species: AI-2 Signalling System Map Developed 
The Reactions
The system was represented by the following reactions. The reactions with double headed arrows have two rate constants(forward/ reverse rate constant). All reactions were assumed to be elementary reactions.
 Parameter Rationale
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