Difference between revisions of "Team:Hong Kong HKUST/Modelling Stability Analysis"

 
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<h1 class="title text-center"><b>Stability Analysis</b></h1>
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<blockquote><p>In our tri-stable system, stability plays an indispensable role in determining the robustness of the system. What we learnt from the bi-stable switch is the variation in biological parts, e.g. promoters and ribosome binding sites (RBS), would cause the system to be unstable. Therefore, we performed stability analysis to assess the conditions and constraints that the system gives three stable states.</p></blockquote>
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<p style="font-size:1.2em;">Here we focus on examining the equilibrium points, at which all species-of-interest i.e. repressor proteins reach equilibrium and that their concentrations do not change with time (i.e. $\frac{dx}{dt}=\frac{dy}{dt}=\frac{dz}{dt}=0$). Then, we can determine whether the equilibrium points are stable or unstable. A stable point is defined as: given a slight perturbation to an equilibrium point, the system automatically returns to the original point subsequently; while an unstable point is an equilibrium point where the system changes dramatically upon any perturbation. Moreover, parameters, such as relative efficiency of the promoter and binding affinity of the repressors to DNA, are known to have great influence on the stability of the equilibrium points. Therefore, in order to achieve a stable system, it is crucial to investigate the suitable
 +
numerical range of the parameters and accordingly, this will provide insights on the selection of proper combinations of parts in the circuits.</p>
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                </div>
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        </section>
  
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<h1><b>Modelling Stability Analysis</b></h1>
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<h4>Lorem ipsum dolor sit amet, consectetur adipiscing elit. Aenean nec magna quis lacus malesuada pellentesque sit amet et quam. Nam lectus nunc, congue sed bibendum vitae, tincidunt vitae odio. Nullam porta posuere risus consequat hendrerit. Proin ante arcu, elementum in ullamcorper sed, tempor eget purus. Nullam convallis dui vitae quam condimentum, quis rutrum diam blandit. Nam consectetur, leo eu sollicitudin venenatis, ex metus luctus arcu, et commodo mauris elit ut mi. Donec luctus diam eu tortor fringilla pharetra.<br><br><br>Lorem ipsum dolor sit amet, consectetur adipiscing elit. Aenean nec magna quis lacus malesuada pellentesque sit amet et quam. Nam lectus nunc, congue sed bibendum vitae, tincidunt vitae odio. Nullam porta posuere risus consequat hendrerit. Proin ante arcu, elementum in ullamcorper sed, tempor eget purus. Nullam convallis dui vitae quam condimentum, quis rutrum diam blandit. Nam consectetur, leo eu sollicitudin venenatis, ex metus luctus arcu, et commodo mauris elit ut mi. Donec luctus diam eu tortor fringilla pharetra.</h4>
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<b><em>(I) Poincare-Perron Theorem</em></b>
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<br>
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<p style="color: white; font-size:1.2em;">Poincare-Perron Theorem was employed for the stability analysis.</p>
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<div class="well" style="color:black;">For the ODE system<br><center><img class="equation" src="https://static.igem.org/mediawiki/2016/b/b4/T--Hong_Kong_HKUST--Modelling_17.png"></center></div>
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<p style="color: white; font-size:1.2em;">Suppose $\overrightarrow{p_e}$ denotes an equilibrium concentration of the repressor proteins. Then $\overrightarrow{p_e}$ is stable or unstable as follows:</p>
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<ol style="font-size:1.2em;">
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  <li>If the eigenvalues of $\overrightarrow{f'}$($\overrightarrow{p_e}$) all have negative real part, then $\overrightarrow{y_e}$ is asymptoticallystable;</li>
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  <li>If any of the eigenvalues of $\overrightarrow{f'}$($\overrightarrow{p_e}$) has positive real part, then $\overrightarrow{y_e}$ is unstable.</li>
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</ol>
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<br>
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<p style="color: white; font-size:1.2em;">In order to obtain the equilibrium concentrations of the three repressor proteins, we have to solve $\frac{dx}{dt}=\frac{dy}{dt}=\frac{dz}{dt}=0$, such that:</p>
 +
<div class="well" style="color:black;"><center><img class="equation" src="https://static.igem.org/mediawiki/2016/8/8f/T--Hong_Kong_HKUST--Modelling_18.png"></center><br><center><img class="equation" src="https://static.igem.org/mediawiki/2016/f/ff/T--Hong_Kong_HKUST--Modelling_19.png"></center><br><center><img class="equation" src="https://static.igem.org/mediawiki/2016/7/70/T--Hong_Kong_HKUST--Modelling_20.png"></center></div>
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<p style="color: white; font-size:1.2em;">Where $x_s$, $y_s$ and $z_s$ denote the equilibrium concentration of the repressor proteins. However, solving a nonlinear ODE system analytically is hard and the solution is expected to be too lengthy and complex. We therefore employed piecewise-linear functions (Jutta,Gebert & Nicole,Radde & Gerhard-Wilhelm,Weber, 2007; Polynikis, Hogan, & Di Bernardo, 2009) to approximate the Hill’s term in the equation as follow:</p>
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<center>
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<img class="img-responsive" src="https://static.igem.org/mediawiki/2016/a/a3/T--Hong_Kong_HKUST--Modelling_pic1.png">
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<Figp style="font-size:14px; padding: 0 0.9em;"><b>Fig. 1. A graphical representation of the approximation of Hill’s function by piecewise-linear function.</b> (Polynikis et al., 2009)</Figp>
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</center>
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<br><br>
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<div class="well" style="color:black;"><center><img class="equation" src="https://static.igem.org/mediawiki/2016/5/5b/T--Hong_Kong_HKUST--Modelling_21.png"></center><br><center><img class="equation" src="https://static.igem.org/mediawiki/2016/1/13/T--Hong_Kong_HKUST--Modelling_22.png"></center></div>
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<div class="well" style="color:black;">Then, equation (14)-(16) are reduced into the following form:<br><br><center><img class="equation" src="https://static.igem.org/mediawiki/2016/4/42/T--Hong_Kong_HKUST--Modelling_23.png"></center><br><center><img class="equation" src="https://static.igem.org/mediawiki/2016/2/27/T--Hong_Kong_HKUST--Modelling_24.png"></center><br><center><img class="equation" src="https://static.igem.org/mediawiki/2016/c/c7/T--Hong_Kong_HKUST--Modelling_25.png"></center><br><br></div>
 +
<div class="well" style="color:black;">Using linear algebra, we solved the approximated analytical solutions:<br><br><center><img class="equation" src="https://static.igem.org/mediawiki/2016/d/db/T--Hong_Kong_HKUST--Modelling_26.png"></center></div>
 +
<div class="well" style="color:black;">We then apply the analytical solutions into a Jacobian Matrix of the system of ODEs to assess the stability:<br><br><center><img class="equation" src="https://static.igem.org/mediawiki/2016/c/c3/T--Hong_Kong_HKUST--Modelling_27.png"></center></div>
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<p style="color: white; font-size:1.2em;">After computation, we obtained the following results:</p>
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<th>$\begin{pmatrix}x_s\\y_s\\z_s\end{pmatrix}=\begin{pmatrix}a\\a\\0\end{pmatrix}$</th>
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<th>$\begin{pmatrix}x_s\\y_s\\z_s\end{pmatrix}=\begin{pmatrix}a\\0\\a\end{pmatrix}$</th>
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<th>$\begin{pmatrix}x_s\\y_s\\z_s\end{pmatrix}=\begin{pmatrix}0\\a\\a\end{pmatrix}$</th>
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<td>$\lambda_1$</td>
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<!--<div class="column full_size" >
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<h2> Welcome to iGEM 2016! </h2>
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<h5>Before you start: </h5>
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<p> Please read the following pages:</p>
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<p style="color: white; font-size:1.2em;">The result shows that all eigenvalues of the 3 solutions are smaller than 0, according to the criterion of Poincare-Perron Theorem, these three solutions are all stable equilibrium points only if $n\geq2$ and $a\geq1+\frac2n$.</p>
  
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</div>
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<b><em>(II) Phase Portrait</em></b>
</div>
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</h2>
 
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<p style="font-size: 1.2em;">Another method can be adopted to analyse the system's stability is phase portrait. (P, 2009) In order to give a clear picture, we begin with the illustration of the phase portrait of a simpler yet similar system, bistable switch. </p>
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<Figp style="font-size:14px; padding: 0 0.9em;"><b>Fig. 2. Phase portrait of bistable switch</b> (Principle of synthetic biology -- stability analysis.) </Figp>
 
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</center>
</div>  
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<br><br>
 
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<p style="font-size:1.2em;">The arrows on the plot represent the vector fields of the system, which indicate the direction that the system tends to evolve to. Specifically speaking, a direction to which vector fields point tells where the system tends to approach; conversely, when vector field points away from certain region, that region is where the system does not tend to stay. On the other hand, as shown in figure 2, there are two functions namely nullclines which are obtained by setting $\frac{dx}{dt}=0$ and $\frac{dy}{dt}=0$, have 3 intersections. The intersections, as defined above, represent the equilibrium points of the system. Nonetheless, according to the phase portrait, not all the points are stable; in fact only 2 points are stable to which the arrows points, while one of the intersections is unstable from which the arrows point away, while at the unstable point, all the repressor proteins’ concentrations are of equal concentration. And upon slight perturbation at this point, the system switches to either one of the stable points. This property is shown pictorially that when it is perturbed slightly away from the unstable point, the arrows point to one of the stable pointsAnd the other intersections are considered stable due to their recovery ability under perturbation.
 
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<br><br>
 
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As expected, phase portrait can be applied to tri-stable toggle switch, which provides us another way to analyze the system stability pictorially.</p>
 
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<center>
<div class="column half_size" >
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<img class="img-responsive" src="https://static.igem.org/mediawiki/2016/7/73/T--Hong_Kong_HKUST--Modelling_pic3.png" style="height:auto; width: 60%;">
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<Figp style="font-size:14px; padding: 0 0.9em;"><b>Fig. 3a. Phase portrait of tristable system, under the condition of Hill’s coefficient, n = 2 and a = 8. Red arrows indicate the 3 intersection points of the three surfaces.</b></Figp>
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+
<Figp style="font-size:14px; padding: 0 0.9em;"><b>Fig. 3b. Cross-sectional diagram of 3a. Red arrows indicate the intersection points of the three surfaces.
 
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<p style="font-size:1.2em;">We notice there are three intersections on the phase plane which corresponds to the three stable points identified by Poincare-Perron Theorem in the previous section: $\begin{pmatrix}x_s\\y_s\\z_s\end{pmatrix}=\begin{pmatrix}a\\a\\0\end{pmatrix}$ , $\begin{pmatrix}x_s\\y_s\\z_s\end{pmatrix}=\begin{pmatrix}a\\0\\a\end{pmatrix}$ and $\begin{pmatrix}x_s\\y_s\\z_s\end{pmatrix}=\begin{pmatrix}0\\a\\a\end{pmatrix}$. <br>Accordingly, it shows that the system has three steady states, which conforms to our original design.</p>
<ul>
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<li>State your accomplishments! Tell people what you have achieved from the start. </li>
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<Figp style="font-size:14px; padding: 0 0.9em;"><b>Fig. 4. Phase portrait of tristable system, under the condition of Hill’s coefficient, n = 1 and a < 1. Red arrow indicates the only one intersection point of the three surfaces.</b></Figp>
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<p style="font-size:1.2em;">Nonetheless, when Hill’s coefficient (n) is adjusted to a value smaller than 2 and ‘a’ is set to be smaller than 1, the three stable points disappear and reduce to a single intersection, which indicates that under the above condition, the system no longer exhibits tristability. We therefore conclude that for the Tristable Switch to attain tristability, the following constraints must be satisfied:</p>
<li>Start documenting your project as early as possible; don’t leave anything to the last minute before the Wiki Freeze. For a complete list of deadlines visit the <a href="https://2016.igem.org/Calendar">iGEM 2016 calendar</a> </li>
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<h5>Inspiration</h5>
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<li> <a href="https://2014.igem.org/Team:SDU-Denmark/"> 2014 SDU Denmark </a> </li>
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<li> <a href="https://2014.igem.org/Team:LMU-Munich">2014 LMU-Munich</a> </li>
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<li> <a href="https://2014.igem.org/Team:Michigan"> 2014 Michigan</a></li>
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<li> <a href="https://2014.igem.org/Team:ITESM-Guadalajara">2014 ITESM-Guadalajara </a></li>
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<li> <a href="https://2014.igem.org/Team:SCU-China"> 2014 SCU-China </a></li>
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<!--<div class="column full_size" >
 
 
<p>Here you can describe the results of your project and your future plans. </p>
 
 
<h5>What should this page contain?</h5>
 
<ul>
 
<li> Clearly and objectively describe the results of your work.</li>
 
<li> Future plans for the project </li>
 
<li> Considerations for replicating the experiments </li>
 
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<li><a href="https://2014.igem.org/Team:Paris_Bettencourt/Results">2014 Paris Bettencourt </a></li>
 
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Latest revision as of 03:20, 20 October 2016

Stability Analysis

In our tri-stable system, stability plays an indispensable role in determining the robustness of the system. What we learnt from the bi-stable switch is the variation in biological parts, e.g. promoters and ribosome binding sites (RBS), would cause the system to be unstable. Therefore, we performed stability analysis to assess the conditions and constraints that the system gives three stable states.



Here we focus on examining the equilibrium points, at which all species-of-interest i.e. repressor proteins reach equilibrium and that their concentrations do not change with time (i.e. $\frac{dx}{dt}=\frac{dy}{dt}=\frac{dz}{dt}=0$). Then, we can determine whether the equilibrium points are stable or unstable. A stable point is defined as: given a slight perturbation to an equilibrium point, the system automatically returns to the original point subsequently; while an unstable point is an equilibrium point where the system changes dramatically upon any perturbation. Moreover, parameters, such as relative efficiency of the promoter and binding affinity of the repressors to DNA, are known to have great influence on the stability of the equilibrium points. Therefore, in order to achieve a stable system, it is crucial to investigate the suitable numerical range of the parameters and accordingly, this will provide insights on the selection of proper combinations of parts in the circuits.

(I) Poincare-Perron Theorem


Poincare-Perron Theorem was employed for the stability analysis.

For the ODE system

Suppose $\overrightarrow{p_e}$ denotes an equilibrium concentration of the repressor proteins. Then $\overrightarrow{p_e}$ is stable or unstable as follows:

  1. If the eigenvalues of $\overrightarrow{f'}$($\overrightarrow{p_e}$) all have negative real part, then $\overrightarrow{y_e}$ is asymptoticallystable;
  2. If any of the eigenvalues of $\overrightarrow{f'}$($\overrightarrow{p_e}$) has positive real part, then $\overrightarrow{y_e}$ is unstable.

In order to obtain the equilibrium concentrations of the three repressor proteins, we have to solve $\frac{dx}{dt}=\frac{dy}{dt}=\frac{dz}{dt}=0$, such that:



Where $x_s$, $y_s$ and $z_s$ denote the equilibrium concentration of the repressor proteins. However, solving a nonlinear ODE system analytically is hard and the solution is expected to be too lengthy and complex. We therefore employed piecewise-linear functions (Jutta,Gebert & Nicole,Radde & Gerhard-Wilhelm,Weber, 2007; Polynikis, Hogan, & Di Bernardo, 2009) to approximate the Hill’s term in the equation as follow:

Fig. 1. A graphical representation of the approximation of Hill’s function by piecewise-linear function. (Polynikis et al., 2009)



Then, equation (14)-(16) are reduced into the following form:





Using linear algebra, we solved the approximated analytical solutions:

We then apply the analytical solutions into a Jacobian Matrix of the system of ODEs to assess the stability:

After computation, we obtained the following results:

Eigen-value \ Analytical solution $\begin{pmatrix}x_s\\y_s\\z_s\end{pmatrix}=\begin{pmatrix}a\\a\\0\end{pmatrix}$ $\begin{pmatrix}x_s\\y_s\\z_s\end{pmatrix}=\begin{pmatrix}a\\0\\a\end{pmatrix}$ $\begin{pmatrix}x_s\\y_s\\z_s\end{pmatrix}=\begin{pmatrix}0\\a\\a\end{pmatrix}$
$\lambda_1$ < 0 < 0 < 0
$\lambda_2$ < 0 < 0 < 0
$\lambda_3$ < 0 < 0 < 0

The result shows that all eigenvalues of the 3 solutions are smaller than 0, according to the criterion of Poincare-Perron Theorem, these three solutions are all stable equilibrium points only if $n\geq2$ and $a\geq1+\frac2n$.

(II) Phase Portrait

Another method can be adopted to analyse the system's stability is phase portrait. (P, 2009) In order to give a clear picture, we begin with the illustration of the phase portrait of a simpler yet similar system, bistable switch.

Fig. 2. Phase portrait of bistable switch (Principle of synthetic biology -- stability analysis.)


The arrows on the plot represent the vector fields of the system, which indicate the direction that the system tends to evolve to. Specifically speaking, a direction to which vector fields point tells where the system tends to approach; conversely, when vector field points away from certain region, that region is where the system does not tend to stay. On the other hand, as shown in figure 2, there are two functions namely nullclines which are obtained by setting $\frac{dx}{dt}=0$ and $\frac{dy}{dt}=0$, have 3 intersections. The intersections, as defined above, represent the equilibrium points of the system. Nonetheless, according to the phase portrait, not all the points are stable; in fact only 2 points are stable to which the arrows points, while one of the intersections is unstable from which the arrows point away, while at the unstable point, all the repressor proteins’ concentrations are of equal concentration. And upon slight perturbation at this point, the system switches to either one of the stable points. This property is shown pictorially that when it is perturbed slightly away from the unstable point, the arrows point to one of the stable points. And the other intersections are considered stable due to their recovery ability under perturbation.

As expected, phase portrait can be applied to tri-stable toggle switch, which provides us another way to analyze the system stability pictorially.

Fig. 3a. Phase portrait of tristable system, under the condition of Hill’s coefficient, n = 2 and a = 8. Red arrows indicate the 3 intersection points of the three surfaces.


Fig. 3b. Cross-sectional diagram of 3a. Red arrows indicate the intersection points of the three surfaces.


We notice there are three intersections on the phase plane which corresponds to the three stable points identified by Poincare-Perron Theorem in the previous section: $\begin{pmatrix}x_s\\y_s\\z_s\end{pmatrix}=\begin{pmatrix}a\\a\\0\end{pmatrix}$ , $\begin{pmatrix}x_s\\y_s\\z_s\end{pmatrix}=\begin{pmatrix}a\\0\\a\end{pmatrix}$ and $\begin{pmatrix}x_s\\y_s\\z_s\end{pmatrix}=\begin{pmatrix}0\\a\\a\end{pmatrix}$.
Accordingly, it shows that the system has three steady states, which conforms to our original design.

Fig. 4. Phase portrait of tristable system, under the condition of Hill’s coefficient, n = 1 and a < 1. Red arrow indicates the only one intersection point of the three surfaces.


Nonetheless, when Hill’s coefficient (n) is adjusted to a value smaller than 2 and ‘a’ is set to be smaller than 1, the three stable points disappear and reduce to a single intersection, which indicates that under the above condition, the system no longer exhibits tristability. We therefore conclude that for the Tristable Switch to attain tristability, the following constraints must be satisfied: