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Team:CIDEB-UANL Mexico/mathmodel
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Revision as of 01:56, 7 June 2013
Mathematical modeling
System topology
Our system is composed by two parts, the first consists of the transcription factor cI (c0051) under the regulation of a promoter with a riboswitch (J23100 and k110517) that can be activated or repressed depending on the temperature; we call this our thermosensor. The second part consist of two genes -the insecticide protein VIP and the green fluorescent protein, GFP- that are under the regulation of cI.
The thermosensor is repressed when the temperature is below 32 °C; when the temperatures reaches a range between 32 and 37 °C, the thermosensor is activated.
cI (c0051) is a gene that constitutively represses the promoter r0051. Once our promoter is activated starts the production of Vip3Ac3 and GFP. This is means that a range of 32 °C to 37 °C disables the production of the Vip and GFP.
Deterministic model
Our deterministic model represents the change in time of the concentrations of mRNAs and their corresponding proteins. It assumes that the variables behave continuously and obey kinetic rules that can be represented by constants. We are aware that, in practice, the components and variables in the model may not fall under the assumptions of a deterministic model and that there is always the chance that noise effects are being grossly underestimated; however, we propose this model as a general framework for thermoregulator modeling, upon which further work can be done.
Equations
\begin{equation} \large \frac{d[mC]}{dt} = \alpha_{1} - \mu_{1}[mC] \end{equation}
\begin{equation} \large \frac{d[C]}{dt} = \alpha_{2} \cdot f_{RBS} \cdot [mC] - \mu_{2}[C] \end{equation}
\begin{equation} \large [mC]_{max} = \frac{\alpha _{1}}{\mu _{1}} \end{equation}
\begin{equation} \large [C]_{max} = \frac{\alpha _{1} \cdot \alpha _{T}} {\mu _{1} \cdot \mu _{T}} \end{equation}
\begin{equation} \large f_{RBS} = \left\{ \begin{array}{rcl} 0 & \mbox{if} & t \geq ON \\ 1 & \mbox{if} & t < ON \end{array} \right. \end{equation}
\begin{equation} \large \frac{d[mV]}{dt} = \alpha_{3} \cdot \frac{K_{D}^h}{K_{D}^h + [C]^h} - \mu_{1}[mV] \end{equation}
\begin{equation} \large \frac{d[V]}{dt} = \alpha_{4} \cdot [mV] - \mu_{4}[V] \end{equation}
\begin{equation} \large [mV]_{max} = \frac{\alpha _{3}}{\mu _{3}} \end{equation}
\begin{equation} \large [V]_{max} = \frac{\alpha _{3} \cdot \alpha _{4}} {\mu _{3} \cdot \mu _{4}} \end{equation}
\begin{equation} \large \frac{d[mG]}{dt} = \alpha_{5} \cdot \frac{K_{D}^h}{K_{D}^h + [C]^h} - \mu_{5}[mG] \end{equation}
\begin{equation} \large \frac{d[G]}{dt} = \alpha_{6} \cdot [mG] - \mu_{6}[G] \end{equation}
\begin{equation} \large [mG]_{max} = \frac{\alpha _{5}}{\mu _{5}} \end{equation}
\begin{equation} \large [G]_{max} = \frac{\alpha _{5} \cdot \alpha _{6}} {\mu _{5} \cdot \mu _{6}} \end{equation}
Parameters and variables
Our gene circuit is made of six different variables: the concentrations of three proteins (cI, VIP and GFP) and their respective mRNA inside a cell. In table 1, the symbols for each variable are shown. Proteins are represented by a single letter and their mRNAs are represented by that same letter with a lowercase "m" before it.
| Symbol | Definition | Gene size in bp |
| mC, C | Transcription factor cI (mRNA and protein) | ---- |
| mV, V | Insecticide protein VIP (mRNA and protein) | ---- |
| mG, G | Reporter protein GFP (mRNA and protein) | ---- |
To parameterize our model, we chose to follow the approach of team Beijing 2009; they propose a relationship between the gene length in base pairs and the maximum transcription rate and, similarly, between the protein length in amino acid numbers and the maximum translation rate. Assuming that the number of polymerases and ribosomes are the average values determined for E. coli, the following equations are used:
| Symbol | Definition | Values | Formula | Source |
|---|---|---|---|---|
| α1 | Transcription rate of cI | 5.6 | 4200/Gene Length (nM/min) | https://2009.igem.org/Team:PKU_Beijing/Modeling/Parameters |
| α2 | Translation rate of cI | 9.6 | 2400RBS/Protein Length | https://2009.igem.org/Team:PKU_Beijing/Modeling/Parameters |
| α3 | Transcription rate of VIP | 1.74129353 | 4200/Gene Length (nM/min) | https://2009.igem.org/Team:PKU_Beijing/Modeling/Parameters |
| α4 | Translation rate of VIP | 2.985075 | 2400RBS/Protein Length | https://2009.igem.org/Team:PKU_Beijing/Modeling/Parameters |
| α5 | Transcription rate of GFP | 5.53359684 | 4200/Gene Length (nM/min) | https://2009.igem.org/Team:PKU_Beijing/Modeling/Parameters |
| α6 | Translation rate of GFP | 9.486166 | 2400RBS/Protein Length | https://2009.igem.org/Team:PKU_Beijing/Modeling/Parameters |
For all the variables the degradation rate is expressed by the formula (ln(2)/half life)+(ln(2)/division time), with the same division time of e. coli (30 min), because all the process occurs within it. The only thing that change is the half time; for cI, VIP and GFP (mRNA) is 6.8 minutes and for cI (Selinger, GW, et al., 2003), VIP and GFP protein is more than 10 hours (Varshavsky, (1997) and Tobias et al., 1991).
| Symbol | Definition | Values | Formula | Source |
|---|---|---|---|---|
| μ1 | Degradation rate of cI (mRNA | 0.18063836 | Half life = 6.8 min, Division time = 30 min | (Selinger, GW, et al., 2003) |
| μ2 | Degradation rate of cI (protein) | 0.03885825 | Half life > 10 h; division time = 30 min | (Varshavsky, (1997) and Tobias et al., 1991) |
Simulations
The saturation values for mRNAs and proteins were calculated analytically; but since there are several variables, it becomes complicated to integrate by analytical methods, so we use methods of numerical integration in a computer program by called Simulink. The values of the parameters (rate of transcription, translation, degradation and dissociation) are the ones we have found so far, but we continue researching in order to improve and expand our model.
cI inactive-active simulation
This simulation are showing our model in a temperature below 32ºC making the variables of cI protein production in their minimum value for the first 30 minutes, creating the possibility of Vip3Ca3 and GFP proteins to be processed in the bacteria at maximum capacity (maximum capacities shown in the parameter table). Past the half hour the temperature is higher of our ideal parameters of cI production (above 32ºC) allowing the transcription and translation of cI protein in a factor of 0 (represented in the equation number 5 showed above), inhibiting the formation of the other two system parts: Vip3Ca3 and GFP proteins. Whose percents in the E.Coli bacterium drops to the minimum until the cI production stops again and the process restarts.
cI active-inactive simulation
The graphics that are shown represent the change of the concentration of each part in relation to time, including all the parameters that we presented and the equation 5, where we represent the repression and activation of the riboregulator making the value of its function 0 or 1. Figure1 shows the graphic that in the simulation are showing our model in a temperature between 32 to 37ºC in the first 30 minutes. As you can see, the graphic of cI (blue) that in this case is decreasing of 20 to 37ºC is active and then it is producing constantly, repressing the Vip3Ca3 (red) and GFP (green), but when the temperature changes higher than the 20 to 37oC the cI it is now turning off allowing the transcription and translation of Vip3Ca3 and GFP so changes the graphic but the GFP it is producing more than the Vip3Ca3 by the size. In the case of the graphic cI off-on, the cI (blue) it is now turn off when the temperature increase of 20 to 37oC and it is not producing, by result the Vip3Ca3 (red) is producing such as GFP (green) with the parameters that are showed in that section but when the temperature decrease the cI it is now producing, by result the Vip3Ca3 is repressing now such as the GFP.
