Difference between revisions of "Team:Aix-Marseille/Collaborations"

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<p>For the rates of change of the internal state vector then we propose for the bacterial maturity to extend the development presented in Shene et al. [?] to include 2 plasmids and incorporate cell maturity as a state variable. This gives:</p>
 
<p>For the rates of change of the internal state vector then we propose for the bacterial maturity to extend the development presented in Shene et al. [?] to include 2 plasmids and incorporate cell maturity as a state variable. This gives:</p>
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<div lang="latex">
 
In 1967 Fredrickson \textit{et al.} \cite{Fredrickson1967} studied mathematically development of a
 
bacterial population, under the assumptions of a large population of independant bacteria in a well
 
mixed solution of constant volume.
 
The large population ensures that for the population the expectation value is a good estimate of the
 
average.
 
The bacteria being independant ensures that the behaviour of each individual depends only on its internal
 
state $\mathbf{z}$ and the conditions $\mathbf{c}$ which are the same for all individuals.
 
The volume is well mixed so the conditions $\mathbf{c}$ which are the same everywhere.
 
The volume is constant so that the population caracteristics can be evaulated by integration over the volume.
 
In their development $\mathbf{z}$ and $\mathbf{c}$ are considered to be arbitrary vector quantities.
 
 
From this starting point they develop a pair of \textit{master equations of change} to describe the evolution
 
of the population:
 
\begin{multline}
 
\frac{\partial}{\partial t} W_{\mathbf{Z}}(\mathbf{z},t)
 
+ \nabla_{\mathbf{Z}} \cdotp [(\mathbf{\beta} \cdotp \bar{\mathbf{R}}(\mathbf{z,c}) W_{\mathbf{Z}}(\mathbf{z},t)] \\
 
= 2 \int \sigma (\mathbf{z',c}) p(\mathbf{z,z',c}) W_{\mathbf{Z}}(\mathbf{z'},t)\mathrm{d}v'
 
- (D+\sigma (\mathbf{z,c})) W_{\mathbf{Z}}(\mathbf{z},t)
 
\end{multline}
 
\begin{equation}
 
\frac{d\mathbf{c}}{dt}
 
= D(\mathbf{c_f} - \mathbf{c} ) + \mathbf{\gamma}
 
\cdotp \int  \bar{\mathbf{R}}(\mathbf{z,c}) W_{\mathbf{Z}}(\mathbf{z},t)\mathrm{d}v
 
\end{equation}
 
In these equations the various symbols are as follows:\\
 
 
\begin{tabular}{p{0.10\linewidth}p{0.75\linewidth}}
 
 
$\mathbf{z}                          $& Vector for internal state of a bacteria.\\
 
$\mathbf{c}                          $& Time dependant vector for conditions.\\
 
$W_{\mathbf{Z}}(\mathbf{z},t)        $& Distribution of bacteria in $\mathbf{z}$ space at time $t$.\\
 
$\bar{\mathbf{R}}(\mathbf{z,c})      $& The expected value or the reaction rate vector function of $\mathbf{z}$ and $\mathbf{c}$.\\
 
$\sigma (\mathbf{z',c})              $& Rate of fision for bacteria as a scalar function of $ \mathbf{z,c} $.\\
 
$p(\mathbf{z,z',c})                  $& Partitioning probability of generating a child in state $\mathbf{z}$ from a parent
 
                                        in state $\mathbf{z'}$ given the conditions $\mathbf{c}$.\\
 
$\nabla_{\mathbf{Z}}\cdot \mathbf{V} $& $\sum \frac{\partial}{\partial z_i}\mathbf{V}_i $\\
 
$\textrm{d}v'                        $& Integral over state space $v'$ .\\
 
$D                                  $& Dilution rate of the culture (for fermenters). \\
 
$\beta                              $& Stochiometric matrix for cellular substances.\\
 
$\gamma                              $& Stochiometric matrix for extra-cellular substances.\\
 
 
\end{tabular}\\
 
\\With these relations:\\
 
\begin{tabular}{p{0.09\linewidth}p{0.16\linewidth}p{0.63\linewidth}}
 
$\bar{\dot{\mathbf{V}}}(\mathbf{z,c})$ &$= \mathbf{\beta} \cdotp \bar{\mathbf{R}}(\mathbf{z,c}) $&The expected internal state change rate vector.\\
 
&$ -\mathbf{\gamma} \cdotp \bar{\mathbf{R}}(\mathbf{z,c}) $&The expected consumation of substances in the environment by a cell in state $\mathbf{z}$ .\\
 
\end{tabular}
 
\\Thus for a particular problem in hand it is necessary to chose $\mathbf{z}$ and $\mathbf{c}$ that represent the state of cells and the media.
 
Then the matrices and functions $\beta$, $\gamma$, $\bar{\mathbf{R}}(\mathbf{z,c})$, $\sigma (\mathbf{z',c})$ and $p(\mathbf{z,z',c}) $ need to be defined for the problem considered.
 
Finally the inital conditions $W_{\mathbf{Z}}(\mathbf{z},t)$ and $\mathbf{c}_0 $ and growth conditions $D$ and $\mathbf{c}_f $ need to be fixed.
 
 
For the problem in hand, plasmid maintenance during growth with 2 different plasmids, and attempting to find a simple solution
 
to the problem we propose a 3 variable internal state vector:\\
 
$$
 
\mathbf{z} =  \begin{bmatrix} z_0 \\ z_1 \\ z_2 \end{bmatrix} =
 
\begin{bmatrix}\textrm{Cell maturity} \\ \textrm{count of plasmid 1} \\ \textrm{count of plasmid 2} \end{bmatrix}
 
$$ \\
 
 
In this internal state vector:
 
$z_0$ is a mesure of the growth of the bacteria, encompassing such things as size, number of chromosomes and mass;
 
$z_1$ and $z_2$ represent the number of copies of each plasmid.
 
For the external conditions we propose simply the substrate concentration $S$.
 
The maturity parameter has a minimum value of 1 and must increase to 2 before division can occur.
 
 
For the rates of change of the internal state vector then we propose for the bacterial maturity to extend the development presented in Shene
 
\textit{et al.} \cite{Shene2003} to include 2 plasmids and incorporate the cell maturity as a state variable. This gives:
 
\begin{equation}
 
\dot{z}_0 (\mathbf{z},S) = \mu = \mu _{max} \frac{S}{K_S+S} \frac{K_{z_1}}{K_{z_1}+z_1^{m_1}} \frac{K_{z_2}}{K_{z_2}+z_2^{m_2}}
 
\end{equation}
 
Here $ \mu _{max}$ is the maximum growth rate $hr^{-1}$:
 
$\mu (\mathbf{z,S})$ the growth rate ;
 
$K_S$ is the Monod constant in $g/l$ for the substrate;
 
$K_{z_1}$ is the inhibition constant for plamid number 1 in (plasmids per cell)$^{m_1}$,
 
and $m_1$ the Hill coefficient for the cooperativity of inhibition.
 
$K_{z_2}$ and $m_2$ represent the same parameters for plasmid 2.
 
 
For plasmid replication rate we propose, again following Shene \textit{et al.} \cite{Shene2003}, the empirical relationship :
 
\begin{equation}
 
\dot{z}_1 (\mathbf{z},S) =
 
  \begin{cases}
 
  k_1 \frac{\mu (\mathbf{z},S)}{K_1 + \mu (\mathbf{z},S)} ( z_{1_{max}} - z_1 ) & \text{if } z_1 \geq 1.0 \\
 
  0 & \text{if } 0.0 \leq z_1 < 1.0 \\
 
  \end{cases}         
 
\end{equation}
 
This relation, and equivalent one for plasmid number 2 $\dot{z}_2 (\mathbf{z,S})$ is designed to satisfy the boundary conditions
 
of no reproduction if there is less than 1 plasmid in the cell, and a maximum copy number of $z_{1_{max}}$. This introduces the
 
parameters $k_1$ and $K_1$ which are respectively the plasmid replication rate (in $hr^{-1}$) and the inhibition constant (also in $hr^{-1}$).
 
The inhibition constant reduces plasmid replication rate at slower growth rates.
 
 
Notice that here we have directly defined $\bar{\dot{\mathbf{V}}}(\mathbf{z,c})$ rather than $\beta $ and $\bar{\mathbf{R}}(\mathbf{z,c})$.
 
For the growth yield we propose :
 
\begin{equation}
 
\gamma \cdotp \bar{\mathbf{R}}(\mathbf{z,c}) = \alpha \mu(\mathbf{z},S)
 
\end{equation}
 
Where alpha is the growth yield in $g/l/cell$. The remaining functions and parameters in equations 1 and 2 are the
 
division rate $\sigma (\mathbf{z,c})$ and the partitioning function $p(\mathbf{z,z',c}) $. There is less consensus in the
 
litterature for an at least empirically appropriate form for these equations. To remain simple we propose:
 
\begin{equation}
 
\sigma (\mathbf{z,c}) = \sigma \times H[2.0] =
 
\begin{cases}
 
  0      & \text{if } z_0 < 2.0 \\
 
  \sigma & \text{if } z_0 \geq 2.0
 
\end{cases}
 
\end{equation}
 
Here we assume that there is a fixed rate of division $\sigma $ once cells are big enough to divide ($H[]$ is the Heaviside function).
 
\begin{equation}
 
p(\mathbf{z,z',c}) = p(z_0,z'_0) \times p(z_1,z'_1) \times p(z_2,z'_2)
 
\end{equation}
 
\begin{equation}
 
p(z_0,z'_0) = \delta_{z_0,\frac{z'_0}{2}} = \begin{cases}
 
                1 & \text{if } z_0 = z'_0/2.0 \\
 
                0 & \text{if } z_0 \ne z'_0/2.0.
 
              \end{cases}
 
\end{equation}
 
\begin{equation}
 
p(z_1,z'_1) = 0.5^{z'_1} \begin{pmatrix} z_1 \\ z'_1 \\ \end{pmatrix} = 0.5^{z'_1} \frac{z'_1 !}{(z'_1-z_1)! z_1 !}
 
\end{equation}
 
In these equations we assume that the partitioning of the three internal state variables are independant.
 
That cells divide exactly in half, that is the maturity parameter is exactly halved when the cells divide
 
($\delta$ is a Kronecker delta function).
 
That the two plasmids segregate independantly and as individual plasmids according to a binomial distribution.
 
These assumptions are probably the most suspect in the model.
 
 
This initial version of the model has no contention, that is $z_1$ and $z_2$ do not influence the growth rate $\mu $.
 
In order to develop the model for the system envisaged this needs to be introduced.
 
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Revision as of 15:14, 17 October 2016