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Team:LCG-UNAM-Mexico/Simulation
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<p class="style2"> Sensitivity analysis </p> | <p class="style2"> Sensitivity analysis </p> | ||
<p> Definir brevemente de que se trata, mostrar análisis a diferentes tiempos. Señalar los resultados que esperábamos y los que no</p> | <p> Definir brevemente de que se trata, mostrar análisis a diferentes tiempos. Señalar los resultados que esperábamos y los que no</p> | ||
| - | <p class="style2"> Stoichiometric matrix </p> | + | <p class="style2"> <a name="StoichiometricMatrix"></a>Stoichiometric matrix </p> |
<p class="bodyText"> Definir la información que contiene la matriz estequimétrica. <br> | <p class="bodyText"> Definir la información que contiene la matriz estequimétrica. <br> | ||
Definir los espacios nulos (link a wikipedia o matworld?) <br> | Definir los espacios nulos (link a wikipedia o matworld?) <br> | ||
Presentar las bases calculadas y una interpretación concisa </p> | Presentar las bases calculadas y una interpretación concisa </p> | ||
| - | <p class="style2"> Steady-states </p> | + | <p class="style2"> <a name="Steady_State"></a>Steady-states </p> |
<p> <span class="bodyText">Definir estado estacionario, decir algo de la complejidad del problema y justificar la estrategia elegida (aproximación numérica)</span></p> | <p> <span class="bodyText">Definir estado estacionario, decir algo de la complejidad del problema y justificar la estrategia elegida (aproximación numérica)</span></p> | ||
<p> Presentar la solución ontenida. </p> | <p> Presentar la solución ontenida. </p> | ||
<p class="style2"> Jacobian </p> | <p class="style2"> Jacobian </p> | ||
| - | <p class="bodyText"> | + | <p class="bodyText" align="justify"> The Jacobian of a system is defined as the matrix of first order partial derivatives, ad it represents the best linear approximation to a function at a given point. In biochemical networks, the Jacobian can be defined for metabolites(Jx) or fluxes(Jv):</p> |
| - | + | <p align="justify">Jx=S*G<br>Jv= G*S</p> | |
| - | + | <p align="justify" class="bodyText">Where S is the stoichiometric matrix and G is the gradient matrix. S defines the structure of the network and has the stoichiometric coefficients of all reactions which are represented by the rows of S while metabolites are represented by columns. G is the matrix of first order derivatives of fluxes with respect to species concentrations:</p> | |
| - | + | <p align="justify">G_ij= (dv_i)/(dx_j )</p> | |
| + | <p align="justify" class="bodyText">This formal representation of the Jacobian formalizes the relation between the topology of the network, and its biophysical and kinetic characteristics.</p> | ||
| + | <p align="justify" class="bodyText">For our system, we first obtained S which is rank deficient: its rank is 7 and it has 13 rows, this difference is explained by the <a href="https://2008.igem.org/Team:LCG-UNAM-Mexico/Simulation#StoichiometricMatrix">moietie</a> of the system. We reduced S to make it congruent with its rank by eliminating the rows corresponding to: AiiA, LuxR, pcI, p, Ni-ext and Unk. Then we constructed G as previously defined; the partial derivatives are calculated only with respect to the species that remained in S; the state at which G is calculated is the <a href="https://2008.igem.org/Team:LCG-UNAM-Mexico/Simulation#Steady_State">steady-state</a>.</p> | ||
| + | <p align="justify" class="bodyText">Both Jacobians were calculated and they were decomposed through similarity transformation of their eigenvalues and eigenvectos to obtain the modal matrices. This matrices represent the formation of molecular ‘pools’ on the system and the negative inverse of the eigenvalus represent the time scales at which this interactions occur. The Jacobians and modal matrices can be downloaded on Excel (.xls) format if you click <a href="https://static.igem.org/mediawiki/2008/6/6b/TimeScaleAnalysis.xls">here</a>.</p> | ||
| + | <p align="justify"><img src="https://static.igem.org/mediawiki/2008/7/7f/Tabla_TSx.jpg"></p> | ||
| + | <p align="justify"><img src="https://static.igem.org/mediawiki/2008/d/d9/Tabla_TSv.jpg"></p> | ||
| + | <p align="justify" class="bodyText">The values in the tables show that the nickel response is very fast, in the order of 6.03e-8 s, which is what we need to make this system and efficient transcriptional indicator. On the other hand, there is a pool of RcnA that forms immediately and another one that takes much more time to form.</p> | ||
<p><a href="#top"><img src="https://static.igem.org/mediawiki/2008/c/cd/Boton_back.jpg" alt="Back to top" width="190" height="31" border="0"></a> <a href="https://2008.igem.org/Team:LCG-UNAM-Mexico/Modeling"><img src="https://static.igem.org/mediawiki/2008/5/5b/Model1a.jpg" alt="Modeling the system" width="190" height="31" border="0"></a><a href="https://2008.igem.org/Team:LCG-UNAM-Mexico/Parameters"><img src="https://static.igem.org/mediawiki/2008/f/fd/Model2ae.jpg" alt="Parameters & kinetics" width="190" height="31" border="0"></a><br> | <p><a href="#top"><img src="https://static.igem.org/mediawiki/2008/c/cd/Boton_back.jpg" alt="Back to top" width="190" height="31" border="0"></a> <a href="https://2008.igem.org/Team:LCG-UNAM-Mexico/Modeling"><img src="https://static.igem.org/mediawiki/2008/5/5b/Model1a.jpg" alt="Modeling the system" width="190" height="31" border="0"></a><a href="https://2008.igem.org/Team:LCG-UNAM-Mexico/Parameters"><img src="https://static.igem.org/mediawiki/2008/f/fd/Model2ae.jpg" alt="Parameters & kinetics" width="190" height="31" border="0"></a><br> | ||
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With the aim of predicting the behavior of the system, the biochemical reactions were implemented in the SimBiology package of MATLAB, using the previously defined parameters. Simulations were run for different values of the initial concentration of AHL and Nitotal (Niint + Niext) which are the metabolites that we can directly manipulate in our experiments. A parameter scan was also run for some parameters to understand their influence on the system. In order to gain insights into the system dynamics to elucidate the conditions needed to get the desired behavior, we performed a series of analyses on it: Sensitivity analysis allowed us to identify critical parameters that needed to be defined on the most stringent way. Basis for the (right) null and left null space were calculated to obtain information about the general network behavior. Steady-states were calculated by numerical integration of the non-linear ODEs system. Finally the Jacobian of the system was calculated around the steady-states. All simulations and analyses were implemented and performed on MATLAB. Simulation and parameter scan Sensitivity analysis Definir brevemente de que se trata, mostrar análisis a diferentes tiempos. Señalar los resultados que esperábamos y los que no Definir la información que contiene la matriz estequimétrica. Definir estado estacionario, decir algo de la complejidad del problema y justificar la estrategia elegida (aproximación numérica) Presentar la solución ontenida. Jacobian The Jacobian of a system is defined as the matrix of first order partial derivatives, ad it represents the best linear approximation to a function at a given point. In biochemical networks, the Jacobian can be defined for metabolites(Jx) or fluxes(Jv): Jx=S*G Where S is the stoichiometric matrix and G is the gradient matrix. S defines the structure of the network and has the stoichiometric coefficients of all reactions which are represented by the rows of S while metabolites are represented by columns. G is the matrix of first order derivatives of fluxes with respect to species concentrations: G_ij= (dv_i)/(dx_j ) This formal representation of the Jacobian formalizes the relation between the topology of the network, and its biophysical and kinetic characteristics. For our system, we first obtained S which is rank deficient: its rank is 7 and it has 13 rows, this difference is explained by the moietie of the system. We reduced S to make it congruent with its rank by eliminating the rows corresponding to: AiiA, LuxR, pcI, p, Ni-ext and Unk. Then we constructed G as previously defined; the partial derivatives are calculated only with respect to the species that remained in S; the state at which G is calculated is the steady-state. Both Jacobians were calculated and they were decomposed through similarity transformation of their eigenvalues and eigenvectos to obtain the modal matrices. This matrices represent the formation of molecular ‘pools’ on the system and the negative inverse of the eigenvalus represent the time scales at which this interactions occur. The Jacobians and modal matrices can be downloaded on Excel (.xls) format if you click here.
The values in the tables show that the nickel response is very fast, in the order of 6.03e-8 s, which is what we need to make this system and efficient transcriptional indicator. On the other hand, there is a pool of RcnA that forms immediately and another one that takes much more time to form.
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