Difference between revisions of "Team:Dundee/Modeling/Fingerprints"

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}
 
}
 
$$
 
$$
Sensitivity analysis was performed to find the optimum values for the two parameters, \(\gamma\) and \(\lambda\), and the ratio, \(v_{0}\) which give the highest concentration of the final complex, lanosterol. Consider \(\lambda\) and \(\gamma\) first by setting \(v_{0}\) as the suggested value from <a href="#eq7">(7)</a><!--The anchor tag references an equation further down the page for reference of the user--> and setting a range of values for \(\gamma\) and \(\lambda\).  The range of values chosen has the maximum value as twice the suggested value from <a href="#eq10">(10)</a>. </p>  
+
Sensitivity analysis was performed to find the optimum values for the two parameters, \(\gamma\) and \(\lambda\), and the ratio, \(v_{0}\) which give the highest concentration of the final complex, lanosterol. Consider \(\lambda\) and \(\gamma\) first by setting \(v_{0}\) as the suggested value from <a href="#eq7">(Eq 7)</a><!--The anchor tag references an equation further down the page for reference of the user--> and setting a range of values for \(\gamma\) and \(\lambda\).  The range of values chosen has the maximum value as twice the suggested value from <a href="#eq10">(Eq 10)</a>. </p>  
 
<a class="anchor" id="modelfig1"></a>
 
<a class="anchor" id="modelfig1"></a>
 
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<p> From Figure 1, it can be seen that there are optimal values for both parameters where increasing them has no effect on lanosterol formation. This was further investigated by looking at each parameter individually and the effect on lanosterol formation over time. Consider the effect of \(\lambda\) by setting \(v_{0}\) and \(\gamma\) as the suggested values, <a href="#eq7">(7)</a> and <a href="#eq10">(10)</a><!--This links to an equation allowing the reader to refer to it quickly.-->.</p>  
+
<p> From Figure 1, it can be seen that there are optimal values for both parameters where increasing them has no effect on lanosterol formation. This was further investigated by looking at each parameter individually and the effect on lanosterol formation over time. Consider the effect of \(\lambda\) by setting \(v_{0}\) and \(\gamma\) as the suggested values, <a href="#eq7">(Eq 7)</a> and <a href="#eq10">(Eq 10)</a><!--This links to an equation allowing the reader to refer to it quickly.-->.</p>  
 
<a class="anchor" id="modelfig2"></a>
 
<a class="anchor" id="modelfig2"></a>
 
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</center>
 
</center>
 
<br><!--Adds a line break-->
 
<br><!--Adds a line break-->
<p>Figure 2 suggests that the concentration of lanosterol does not increase after \(\lambda = 0.28\). Therefore if the value of \(\lambda\) is smaller than the suggested value, \(\lambda = 0.64061499\), but larger than \(\lambda = 0.28\) the same concentration of lanosterol will be formed. Recall that \(\lambda = \frac{K_{1}SE_{0}}{K_{2}}\), therefore the initial concentration of squalene epoxide or the binding ratio can be less than the suggested value. The effect of \(\gamma\) on lanosterol formation can be considered by setting \(v_{0}\) and \(\lambda\) as the suggested values, <a href="#eq7">(7)</a> and <a href="#eq10">(10)</a>.</p>  
+
<p>Figure 2 suggests that the concentration of lanosterol does not increase after \(\lambda = 0.28\). Therefore if the value of \(\lambda\) is smaller than the suggested value, \(\lambda = 0.64061499\), but larger than \(\lambda = 0.28\) the same concentration of lanosterol will be formed. Recall that \(\lambda = \frac{K_{1}SE_{0}}{K_{2}}\), therefore the initial concentration of squalene epoxide or the binding ratio can be less than the suggested value. The effect of \(\gamma\) on lanosterol formation can be considered by setting \(v_{0}\) and \(\lambda\) as the suggested values, <a href="#eq7">(Eq 7)</a> and <a href="#eq10">(Eq 10)</a>.</p>  
 
<a class="anchor" id="modelfig3"></a>
 
<a class="anchor" id="modelfig3"></a>
 
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</center>
 
</center>
 
<br><!--Adds a line break-->
 
<br><!--Adds a line break-->
<p>Figure 3, similar to Figure 2, suggests that the concentration of lanosterol does not increase after \(\gamma = 0.4\). Therefore if the value of \(\gamma\) is smaller than the suggested value, \(\gamma = 1.000223733\), but larger than \(\gamma = 0.4\) the same concentration of lanosterol will be formed. Recall that \(\gamma = \frac{K_{3}}{K_{2}}\), therefore the binding rate ratio can be less than the suggested value. The effect of \(v_{0}\) on lanosterol formation can be considered by setting \(\gamma\) and \(\lambda\) as the suggested values, <a href="#eq10">(10)</a>.</p>  
+
<p>Figure 3, similar to Figure 2, suggests that the concentration of lanosterol does not increase after \(\gamma = 0.4\). Therefore if the value of \(\gamma\) is smaller than the suggested value, \(\gamma = 1.000223733\), but larger than \(\gamma = 0.4\) the same concentration of lanosterol will be formed. Recall that \(\gamma = \frac{K_{3}}{K_{2}}\), therefore the binding rate ratio can be less than the suggested value. The effect of \(v_{0}\) on lanosterol formation can be considered by setting \(\gamma\) and \(\lambda\) as the suggested values, <a href="#eq10">(Eq 10)</a>.</p>  
 
<a class="anchor" id="modelfig4"></a>
 
<a class="anchor" id="modelfig4"></a>
 
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\frac{dLS}{dt}&=&K_{2}PC - K_{1}LS\cdot SE, \nonumber \\
 
\frac{dLS}{dt}&=&K_{2}PC - K_{1}LS\cdot SE, \nonumber \\
 
\frac{dSE}{dt}&=&K_{2}PC - K_{1}LS\cdot SE, \nonumber \\
 
\frac{dSE}{dt}&=&K_{2}PC - K_{1}LS\cdot SE, \nonumber \\
\frac{dPC}{dt}&=&K_{1}LS \cdot SE - K_{2} PC- K_{3}PC,\\
+
\frac{dPC}{dt}&=&K_{1}LS \cdot SE - K_{2} PC- K_{3}PC,\tag{Eq 1}\\
 
\frac{dLa}{dt}&=&K_{3} PC. \nonumber
 
\frac{dLa}{dt}&=&K_{3} PC. \nonumber
 
\end{eqnarray}
 
\end{eqnarray}
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LS(0)&=&LS_{0}, \quad \mu M \nonumber \\ \nonumber
 
LS(0)&=&LS_{0}, \quad \mu M \nonumber \\ \nonumber
 
SE(0)&=&SE_{0}, \quad \mu M\\  
 
SE(0)&=&SE_{0}, \quad \mu M\\  
PC(0)&=&0, \quad \mu M\\   
+
PC(0)&=&0, \quad \mu M \tag{Eq 2}\\   
 
La(0)&=&0. \quad \mu M \nonumber
 
La(0)&=&0. \quad \mu M \nonumber
 
\end{eqnarray}
 
\end{eqnarray}
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<p>
 
<p>
The parameters were estimated by considering the steady state of the system. Setting the left hand side of <a href="#eq1">(1)</a> to zero gives:</p>
+
The parameters were estimated by considering the steady state of the system. Setting the left hand side of <a href="#eq1">(Eq 1)</a> to zero gives:</p>
 
<a class="anchor" id="eq3"></a>
 
<a class="anchor" id="eq3"></a>
 
$$
 
$$
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\begin{eqnarray}
 
\begin{eqnarray}
 
K_{2} PC&=&K_{1} LS \cdot SE, \nonumber \\
 
K_{2} PC&=&K_{1} LS \cdot SE, \nonumber \\
K_{1} LS \cdot SE&=&K_{2} PC - K_{3} PC.  
+
K_{1} LS \cdot SE&=&K_{2} PC - K_{3} PC. \tag{Eq 3}
 
\end{eqnarray}
 
\end{eqnarray}
 
}
 
}
 
$$
 
$$
 
<p>
 
<p>
Rearranging <a href="#eq3">(3)</a> gives:</P>
+
Rearranging <a href="#eq3">(Eq 3)</a> gives:</P>
 
<a class="anchor" id="eq4"></a>
 
<a class="anchor" id="eq4"></a>
 
$$
 
$$
 
\large{
 
\large{
 
\begin{equation}
 
\begin{equation}
\frac{PC}{LS \cdot SE}=\frac{K_{1}}{K_{2}}.  
+
\frac{PC}{LS \cdot SE}=\frac{K_{1}}{K_{2}}. \tag{Eq 4}
 
\end{equation}
 
\end{equation}
 
}
 
}
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\large{
 
\large{
 
\begin{equation}
 
\begin{equation}
LST=LS+PC.  
+
LST=LS+PC. \tag{Eq 5}
 
\end{equation}
 
\end{equation}
 
}
 
}
 
$$
 
$$
 
<p>
 
<p>
Now using <a href="#eq4">(4)</a> and <a href="#eq5">(5)</a> it can be written that:</p>
+
Now using <a href="#eq4">(Eq 4)</a> and <a href="#eq5">(Eq 5)</a> it can be written that:</p>
 
<a class="anchor" id="eq6"></a>
 
<a class="anchor" id="eq6"></a>
 
$$
 
$$
 
\large{
 
\large{
 
\begin{equation}
 
\begin{equation}
\frac{LS}{LST}=\frac{1}{\frac{K_{1}}{K_{2}} SE_{0} + 1}.  
+
\frac{LS}{LST}=\frac{1}{\frac{K_{1}}{K_{2}} SE_{0} + 1}. \tag{Eq 6}
 
\end{equation}
 
\end{equation}
 
}
 
}
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\large{
 
\large{
 
\begin{equation}
 
\begin{equation}
v_{0}= 2.561,
+
v_{0}= 2.561, \tag{Eq 7}
 
\end{equation}
 
\end{equation}
 
}
 
}
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\large{
 
\large{
 
\begin{equation}
 
\begin{equation}
\frac{LS}{LST}=\frac{1.561}{2.561}.  
+
\frac{LS}{LST}=\frac{1.561}{2.561}. \tag{Eq 8}
 
\end{equation}
 
\end{equation}
 
}
 
}
 
$$
 
$$
 
<p>
 
<p>
By substituting <a href="#eq8">(8)</a> into equation <a href="#eq6">(6)</a> the ratio between \(K_{1}\) and \(K_{2}\) can be found:</p>
+
By substituting <a href="#eq8">(Eq 8)</a> into equation <a href="#eq6">(Eq 6)</a> the ratio between \(K_{1}\) and \(K_{2}\) can be found:</p>
 
<a class="anchor" id="eq9"></a>
 
<a class="anchor" id="eq9"></a>
 
$$
 
$$
 
\large{
 
\large{
 
\begin{equation}
 
\begin{equation}
\frac{K_{1}}{K_{2}}= 0.7810323048\quad \mu  M^{-1}.  
+
\frac{K_{1}}{K_{2}}= 0.7810323048\quad \mu  M^{-1}. \tag{Eq 9}
 
\end{equation}
 
\end{equation}
 
}
 
}
 
$$
 
$$
 
<p>
 
<p>
For <a href="#eq3">(3)</a>, <a href="#eq6">(6)</a> and <a href="#eq8">(8)</a> can be used to find the ratio between \(K_{3}\) and \(K_{2}\):</p>
+
For <a href="#eq3">(Eq 3)</a>, <a href="#eq6">(Eq 6)</a> and <a href="#eq8">(Eq 8)</a> can be used to find the ratio between \(K_{3}\) and \(K_{2}\):</p>
 
<a class="anchor" id="eq10"></a>
 
<a class="anchor" id="eq10"></a>
 
$$
 
$$
 
\large{
 
\large{
 
\begin{equation}
 
\begin{equation}
\frac{K_{3}}{K_{2}}=1.000223733.  
+
\frac{K_{3}}{K_{2}}=1.000223733. \tag{Eq 10}
 
\end{equation}
 
\end{equation}
 
}
 
}
 
$$
 
$$
 
<p>
 
<p>
From Goodman's 1964 paper, it can be calculated that the suggested initial concentration of squalene is: \(SE_{0}=\) 0.8202157402 \(\mu M\). It is then assumed that this will be a reasonable estimate for the initial concentration of squalene epoxide. Therefore, from <a href="#eq9">(9)</a> and <a href="#eq10">(10)</a> the estimated values for \(\lambda\) and \(\gamma\) are found to be:</p>
+
From Goodman's 1964 paper, it can be calculated that the suggested initial concentration of squalene is: \(SE_{0}=\) 0.8202157402 \(\mu M\). It is then assumed that this will be a reasonable estimate for the initial concentration of squalene epoxide. Therefore, from <a href="#eq9">(Eq 9)</a> and <a href="#eq10">(Eq 10)</a> the estimated values for \(\lambda\) and \(\gamma\) are found to be:</p>
 
<a class="anchor" id="eq11"></a>
 
<a class="anchor" id="eq11"></a>
 
$$
 
$$
 
\large{
 
\large{
 
\begin{equation}
 
\begin{equation}
\lambda=0.64061499, \qquad \gamma=1.000223733.  
+
\lambda=0.64061499, \qquad \gamma=1.000223733.\tag{Eq 11}
 
\end{equation}
 
\end{equation}
 
}
 
}
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<p>
 
<p>
By running the ode23 solver over one hundred different values for both parameters and the ratio \(v_{0}\), sensitivity analysis can be performed. The range of values has the mean as the estimated values, <a href="#eq7">(7)</a> and <a href="#eq11">(11)</a>. The results are shown in <u><a href="#modelfig1">Figure 1</a></u>, where the centre of the plot represents the suggested concentration of complex formed when the suggested binding rates are used.</p>
+
By running the ode23 solver over one hundred different values for both parameters and the ratio \(v_{0}\), sensitivity analysis can be performed. The range of values has the mean as the estimated values, <a href="#eq7">(Eq 7)</a> and <a href="#eq11">(Eq 11)</a>. The results are shown in <u><a href="#modelfig1">Figure 1</a></u>, where the centre of the plot represents the suggested concentration of complex formed when the suggested binding rates are used.</p>
 
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<br><!--Adds a line break-->
 
<b>References</b>
 
<b>References</b>

Revision as of 14:04, 8 September 2015

Fingerprint Ageing


Analysis and Modelling

Overview


Principal component analysis (PCA) was used to work with a set of fingerprint data obtained from an Australian PhD thesis. The two aims were to find which lipid compound has the most distinct degradation rate and to find the general degradation behaviour of compounds within fingerprints. PCA is a statistical observation tool that reduces multivariate data to its principal components in order to visualize hidden correlations in data which otherwise contains too many dimensions to view these correlations. The results of PCA can be visualised through plots and graphs produced by MATLAB.


Using a method similar to that of the FluID models, the binding of lanosterol synthase and squalene epoxide to form lanosterol can be investigated. The aim of creating a model describing lanosterol formation is to find the optimum binding rates and optimum initial concentration of lanosterol synthase required in the fingerprint ageing device. Ordinary differential equations were used to investigate the concentration of each of the substances over time, and sensitivity analysis was used to find the optimum conditions. Click below to find out more about each section.





Principal Component Analysis

Consider principal component analysis and how it was implemented.

Squalene Epoxide Model

Consider the binding between squalene epoxide and lanosterol synthase.

Principal Component Analysis


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Click here to see MATLAB code Back to overview Back to start of PCA

Squalene Epoxide and Lanosterol Synthase Binding Model


Aim

The aim of a model describing the binding between squalene epoxide and lanosterol synthase is to find the optimum concentration and binding rates that we require for visual detection of squalene epoxide in the fingermark sample from the crime scene. The more squalene epoxide and lanosterol synthase that bind the more likely it will be that squalene epoxide will be visually detected.


Results

Squalene epoxide is an intermediate in cholesterol synthesis. Squalene epoxide is degraded into lanosterol, a precursor for cholesterol by lanosterol synthase. These reactions can be described by the schematic:

$$ \large{ \ce{LS + SE<=>[K_{1}][K_{2}] PC ->[K_{3}] La}. } $$

Where \(LS\) is the concentration of lanosterol synthase, \(SE\) is the concentration of squalene epoxide, \(PC\) is the concentration of the 1st intermediate, protosterol cation, and \(La\) is the concentration of lanosterol, the full complex. \(K_{1}\), \(K_{3}\) are the forward reaction rates, and \(K_{2}\) is the reverse reaction rate.

The initial concentration of squalene epoxide was defined to be \(SE_{0}\) and two parameters of the system were defined as:

$$ \large{ \begin{equation*} \lambda=\frac{K_{1}}{K_{2}} SE_{0}, \qquad \gamma=\frac{K_{3}}{K_{2}}. \end{equation*} } $$

The initial concentration of lanosterol synthase was defined to be \(LS_{0}\) and the ratio between initial concentrations was defined as:

$$ \large{ \begin{equation*} v_{0}=\frac{LS_{0}}{SE_{0}}. \end{equation*} } $$ Sensitivity analysis was performed to find the optimum values for the two parameters, \(\gamma\) and \(\lambda\), and the ratio, \(v_{0}\) which give the highest concentration of the final complex, lanosterol. Consider \(\lambda\) and \(\gamma\) first by setting \(v_{0}\) as the suggested value from (Eq 7) and setting a range of values for \(\gamma\) and \(\lambda\). The range of values chosen has the maximum value as twice the suggested value from (Eq 10).


Figure 1: Sensitivity analysis for the binding parameters of squalene epoxide and lanosterol synthase binding.

From Figure 1, it can be seen that there are optimal values for both parameters where increasing them has no effect on lanosterol formation. This was further investigated by looking at each parameter individually and the effect on lanosterol formation over time. Consider the effect of \(\lambda\) by setting \(v_{0}\) and \(\gamma\) as the suggested values, (Eq 7) and (Eq 10).


Figure 2: Lanosterol formation with increasing \(\lambda\).

Figure 2 suggests that the concentration of lanosterol does not increase after \(\lambda = 0.28\). Therefore if the value of \(\lambda\) is smaller than the suggested value, \(\lambda = 0.64061499\), but larger than \(\lambda = 0.28\) the same concentration of lanosterol will be formed. Recall that \(\lambda = \frac{K_{1}SE_{0}}{K_{2}}\), therefore the initial concentration of squalene epoxide or the binding ratio can be less than the suggested value. The effect of \(\gamma\) on lanosterol formation can be considered by setting \(v_{0}\) and \(\lambda\) as the suggested values, (Eq 7) and (Eq 10).


Figure 3: Lanosterol formation with increasing \(\gamma\).

Figure 3, similar to Figure 2, suggests that the concentration of lanosterol does not increase after \(\gamma = 0.4\). Therefore if the value of \(\gamma\) is smaller than the suggested value, \(\gamma = 1.000223733\), but larger than \(\gamma = 0.4\) the same concentration of lanosterol will be formed. Recall that \(\gamma = \frac{K_{3}}{K_{2}}\), therefore the binding rate ratio can be less than the suggested value. The effect of \(v_{0}\) on lanosterol formation can be considered by setting \(\gamma\) and \(\lambda\) as the suggested values, (Eq 10).


Figure 4: Lanosterol formation with increasing \(v_{0}\).

Figure 4, similar to Figure 2 and 3, suggests that the concentration of lanosterol does not increase after \(v_{0} = 1.5\). Therefore if the value of \(v_{0}\) is smaller than the suggested value, \(v_{0} = 2.561\), but larger than \(v_{0} = 1.5\) the same concentration of lanosterol will be formed. Recall that \(v_{0} = \frac{LS_{0}}{SE_{0}}\), and \(SE_{0}=0.8202157402 \mu M\). From this the best concentration of lanosterol synthase to have in the fingerprint ageing device is:

$$ \large{ \begin{equation*} LS_{0}=1.23032361 \mu M. \end{equation*} } $$

The results of the model describing lanosterol formation has been passed on to the lab to be used in future decision making.


Method

Using the law of mass action (Guldeberg and Waage, 1879) the binding reaction schematic was written as a system of ordinary differential equations (ODEs):

$$ \large{ \begin{eqnarray} \frac{dLS}{dt}&=&K_{2}PC - K_{1}LS\cdot SE, \nonumber \\ \frac{dSE}{dt}&=&K_{2}PC - K_{1}LS\cdot SE, \nonumber \\ \frac{dPC}{dt}&=&K_{1}LS \cdot SE - K_{2} PC- K_{3}PC,\tag{Eq 1}\\ \frac{dLa}{dt}&=&K_{3} PC. \nonumber \end{eqnarray} } $$

with initial conditions:

$$ \large{ \begin{eqnarray} LS(0)&=&LS_{0}, \quad \mu M \nonumber \\ \nonumber SE(0)&=&SE_{0}, \quad \mu M\\ PC(0)&=&0, \quad \mu M \tag{Eq 2}\\ La(0)&=&0. \quad \mu M \nonumber \end{eqnarray} } $$

The parameters were estimated by considering the steady state of the system. Setting the left hand side of (Eq 1) to zero gives:

$$ \large{ \begin{eqnarray} K_{2} PC&=&K_{1} LS \cdot SE, \nonumber \\ K_{1} LS \cdot SE&=&K_{2} PC - K_{3} PC. \tag{Eq 3} \end{eqnarray} } $$

Rearranging (Eq 3) gives:

$$ \large{ \begin{equation} \frac{PC}{LS \cdot SE}=\frac{K_{1}}{K_{2}}. \tag{Eq 4} \end{equation} } $$

Considering the first binding reaction, it was found that the total concentration of lanosterol synthase, \(LST\), will be equal to:

$$ \large{ \begin{equation} LST=LS+PC. \tag{Eq 5} \end{equation} } $$

Now using (Eq 4) and (Eq 5) it can be written that:

$$ \large{ \begin{equation} \frac{LS}{LST}=\frac{1}{\frac{K_{1}}{K_{2}} SE_{0} + 1}. \tag{Eq 6} \end{equation} } $$

It is known that the ratio between lanosterol synthase and squalene epoxide is:

$$ \large{ \begin{equation} v_{0}= 2.561, \tag{Eq 7} \end{equation} } $$

and that they bind at a 1:1 ratio (Boutaud, 1992). Therefore the ratio of free lanosterol synthase to total lanosterol synthase will be:

$$ \large{ \begin{equation} \frac{LS}{LST}=\frac{1.561}{2.561}. \tag{Eq 8} \end{equation} } $$

By substituting (Eq 8) into equation (Eq 6) the ratio between \(K_{1}\) and \(K_{2}\) can be found:

$$ \large{ \begin{equation} \frac{K_{1}}{K_{2}}= 0.7810323048\quad \mu M^{-1}. \tag{Eq 9} \end{equation} } $$

For (Eq 3), (Eq 6) and (Eq 8) can be used to find the ratio between \(K_{3}\) and \(K_{2}\):

$$ \large{ \begin{equation} \frac{K_{3}}{K_{2}}=1.000223733. \tag{Eq 10} \end{equation} } $$

From Goodman's 1964 paper, it can be calculated that the suggested initial concentration of squalene is: \(SE_{0}=\) 0.8202157402 \(\mu M\). It is then assumed that this will be a reasonable estimate for the initial concentration of squalene epoxide. Therefore, from (Eq 9) and (Eq 10) the estimated values for \(\lambda\) and \(\gamma\) are found to be:

$$ \large{ \begin{equation} \lambda=0.64061499, \qquad \gamma=1.000223733.\tag{Eq 11} \end{equation} } $$

By running the ode23 solver over one hundred different values for both parameters and the ratio \(v_{0}\), sensitivity analysis can be performed. The range of values has the mean as the estimated values, (Eq 7) and (Eq 11). The results are shown in Figure 1, where the centre of the plot represents the suggested concentration of complex formed when the suggested binding rates are used.


References
  • Boutaud, O., Dolis, D., & Schuber, F. (1992). Preferential cyclization of 2, 3 (S): 22 (S), 23-dioxidosqualene by mammalian 2, 3-oxidosqualene-lanosterol cyclase. Biochemical and biophysical research communications, 188(2), 898-904.
  • Goodman, D. S. (1964). Squalene in human and rat blood plasma. Journal of Clinical Investigation, 43(7), 1480.
Click here to see MATLAB code Back to top Back to start of model