Team:Trieste/project/modeling

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        <h1 id="h1_lf" class="main_tit"><div>Modeling</div></h1>
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<h1 id="h1_lf" class="main_tit"><div>Modeling</div></h1>
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            <h1 id="h1_rt" class="main_tit"><div>More</div></h1>
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<h1 id="h1_rt" class="main_tit"><div>More</div></h1>
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                    <h2>Model description</h2>
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src="http://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML">
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<h2>Model description</h2>
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<p>
We chose to use a deterministic, empirical model because it gives simple,
We chose to use a deterministic, empirical model because it gives simple,
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clean solutions.  
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clean solutions.
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</p>
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</p>  
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                    <p>
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<p>
The model is composed of 6 differential equations, each one describing
The model is composed of 6 differential equations, each one describing
the concentration of a substance inside or outside the bacteria.
the concentration of a substance inside or outside the bacteria.
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</p>
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</p>
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<p>
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<p>
All the $\delta$ parameters represent the variable's decay over time.
All the $\delta$ parameters represent the variable's decay over time.
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</p>
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<p>
The first equation, describing the bacteria concentration in the media (bacteria growth) is based
The first equation, describing the bacteria concentration in the media (bacteria growth) is based
on the logistics function:
on the logistics function:
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$$
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\begin{equation}
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    \dot{x} = x (1-x)
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\dot{x} = x (1-x)
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$$
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\end{equation}
We added a term to model bacteria death due to toxin, so the complete equation
We added a term to model bacteria death due to toxin, so the complete equation
is:
is:
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$$
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\begin{equation}
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\dot{b} = k_1  b  (N - b) - k_2  b  (l_i + l_e)
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\dot{b} = k_1  b  (N - b) - k_2  b  (l_i + l_e)
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$$
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\end{equation}
There are three parameters that control this model's behaviour.
There are three parameters that control this model's behaviour.
The $N$ parameter specifies the maximum concentration of bacteria.
The $N$ parameter specifies the maximum concentration of bacteria.
The $k_1$ parameter controls the growth speed and the $k_2$ parameter sets how
The $k_1$ parameter controls the growth speed and the $k_2$ parameter sets how
powerful the toxin is.
powerful the toxin is.
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<p>
The next equation models the CymR concentration inside the bacteria.
The next equation models the CymR concentration inside the bacteria.
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$$
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\begin{equation}
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\dot{p} = k_3 - \delta_1 p - \alpha  p  c
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\dot{p} = k_3 - \delta_1 p - \alpha  p  c
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$$
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\end{equation}
The $k_3$ parameter determines the rate of synthesis of CymR and $\alpha$ the rate of cumate ligation.
The $k_3$ parameter determines the rate of synthesis of CymR and $\alpha$ the rate of cumate ligation.
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</p>
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<p>
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The cumate concentration function is in fact a parameter, because it is
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The p-cumate concentration function is in fact a parameter, because it is
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directly controllable by adding cumate into the bacteria solution.
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directly controllable by adding p-cumate into the bacteria solution.
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$$
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\begin{equation}
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\dot{c} = k_4 - \delta_2 c
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\dot{c} = k_4 - \delta_2 c
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$$
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\end{equation}
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The $k_4$ parameter models $p-cumate$ increase (adding $p-cumate$ at a steady rate).
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The $k_4$ parameter models p-cumate increase (adding p-cumate at a steady rate).
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</p>
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</p>
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                    <p>
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<p>
We use two equations to better model the toxin concentration, taking into
We use two equations to better model the toxin concentration, taking into
account the concentration of toxin both inside and outside the bacteria. The equation
account the concentration of toxin both inside and outside the bacteria. The equation
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concentration depends on bacteria death: when the bacteria dies, all the toxin
concentration depends on bacteria death: when the bacteria dies, all the toxin
it has produced is released outside.
it has produced is released outside.
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$$
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\begin{align}
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    \dot{l_i} &= \frac{A}{k_5 + p} -\delta_3  l_i \\  
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\dot{l_i} &= \frac{A}{k_5 + p} -\delta_3  l_i \\  
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    \dot{l_e} &= v (l_i + l_e) b l_i - \delta_4 l_e
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\dot{l_e} &= v (l_i + l_e) b l_i - \delta_4 l_e
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$$
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\end{align}
The $A$ parameter sets the maximum toxin concentration inside the bacteria, while
The $A$ parameter sets the maximum toxin concentration inside the bacteria, while
the $v$ parameter takes into account the toxin dilution in the bacteria
the $v$ parameter takes into account the toxin dilution in the bacteria
solution.
solution.
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</p>
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                    <p>
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<p>
Last but not least, the antibody density, which we suppose always increasing in
Last but not least, the antibody density, which we suppose always increasing in
time.
time.
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$$
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\begin{equation}
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\dot{a} = k_6 - \delta_5  a
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\dot{a} = k_6 - \delta_5  a
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$$
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\end{equation}
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<h2>Contact us</h2>
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<h2>Contact us</h2>
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<p>For other information, write to:</p>
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<p>For other information, write to:</p>
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Revision as of 20:16, 25 September 2012

Modeling

More

Model description

We chose to use a deterministic, empirical model because it gives simple, clean solutions.

The model is composed of 6 differential equations, each one describing the concentration of a substance inside or outside the bacteria.

All the $\delta$ parameters represent the variable's decay over time.

The first equation, describing the bacteria concentration in the media (bacteria growth) is based on the logistics function: \begin{equation} \dot{x} = x (1-x) \end{equation} We added a term to model bacteria death due to toxin, so the complete equation is: \begin{equation} \dot{b} = k_1 b (N - b) - k_2 b (l_i + l_e) \end{equation} There are three parameters that control this model's behaviour. The $N$ parameter specifies the maximum concentration of bacteria. The $k_1$ parameter controls the growth speed and the $k_2$ parameter sets how powerful the toxin is.

The next equation models the CymR concentration inside the bacteria. \begin{equation} \dot{p} = k_3 - \delta_1 p - \alpha p c \end{equation} The $k_3$ parameter determines the rate of synthesis of CymR and $\alpha$ the rate of cumate ligation.

The p-cumate concentration function is in fact a parameter, because it is directly controllable by adding p-cumate into the bacteria solution. \begin{equation} \dot{c} = k_4 - \delta_2 c \end{equation} The $k_4$ parameter models p-cumate increase (adding p-cumate at a steady rate).

We use two equations to better model the toxin concentration, taking into account the concentration of toxin both inside and outside the bacteria. The equation for the inside concentration is a Hill equation with a decay term. We used it because the concentration increases until the saturation is reached, and from then on the bacteria stops producing the toxin. The point of saturation is never reached, though, because the bacteria dies due to the toxin itself. The outside equation is the most complex one; the outside toxin concentration depends on bacteria death: when the bacteria dies, all the toxin it has produced is released outside. \begin{align} \dot{l_i} &= \frac{A}{k_5 + p} -\delta_3 l_i \\ \dot{l_e} &= v (l_i + l_e) b l_i - \delta_4 l_e \end{align} The $A$ parameter sets the maximum toxin concentration inside the bacteria, while the $v$ parameter takes into account the toxin dilution in the bacteria solution.

Last but not least, the antibody density, which we suppose always increasing in time. \begin{equation} \dot{a} = k_6 - \delta_5 a \end{equation}

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Contact us

For other information, write to:

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