Model
Introduction
We conceived a model in order to handle questions concerning the following situation:
A bacterial growth is carried out in a bioreactor, continually supplied in substrate.
Bacteria can possess 2 type of plasmids:
- plasmids 1 carry the toxin A gene and the anti-toxin B gene
- plasmids 2 carry the toxin B gene and the anti-toxin A gene
So during the growth each bacterium can have no plasmids, either one type of plasmids, or both types.
The aim of the model is to assess the evolution of plasmids throughout the culture, to determine which parameters can matter in the loss of those plasmids, and to precise what are the probabilities for a bacteria to loose its plasmids during cell division.
As a plasmids can be a disadvantage for growth (energy spent into replicating processes) or a advantage (protection against a toxin) this question is hard to answer. But in this situation, where one type of plasmid can influence on the presence of the other type of plasmid in (and reciprocally) in the same bacteria, the question become too tough to answer and only a mathematical model can resolve such a interrogation!
Equations
Development of the model
In 1967 Fredrickson et al. [1] 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 *master equations of
change* to describe the evolution of the population:
\begin{equation}
\begin{gathered}
\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{gathered}
\end{equation}
\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:
| $\mathbf{z}$
|
Vector for internal state of a bacteria.
|
| $\mathbf{c}$
|
Time dependant vector for conditions.
|
| $W_\mathbf{z} (\mathbf{z},t)$
|
Distribution of bacteria in z, t space.
|
| $\overline{\mathbf{R}}(\mathbf{z},c)$
|
The expected value or the reaction rate vector in z, t space.
|
| $\sigma (\mathbf{z,c})$
|
Rate of fision for bacteria in z, c space.
|
| $p(\mathbf{z,z',c}))$
|
Partitioning probability of generating a child in state z from a parent in state z'.
|
| $\nabla_\mathbf{z}\cdot\mathbf{V}$
|
$\sum \frac{\partial}{\partial z_i}\mathbf{V}_i$
|
| $\mathrm{d}v'$
|
Integral over state space v'
|
| $D$
|
Dilution rate of the culture (for femrenters).
|
| $\beta$
|
Stochiometric matrix for cellular substances.
|
| $\gamma$
|
Stochiometric matrix for extra-cellular substances.
|
| $\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 $\mathbf{z}$ .
|
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 et
al. [2] 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 et
al. [2], 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}
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.
Substituting into the equations 1 and 2 we obtain:
\begin{multline}
\frac{\partial}{\partial t} W_{\mathbf{Z}}(\mathbf{z},t)
+ [\nabla_{\mathbf{Z}} \cdotp \bar{\dot{\mathbf{Z}}}(\mathbf{z},S)] W_{\mathbf{Z}}(\mathbf{z},t)
+ \sum_i \bar{\dot{z_i}} \times \frac{\partial}{\partial z_i} W_{\mathbf{Z}}(\mathbf{z},t) \\
= 2 \sigma \int_{z'_0>2.0} \delta_{z_0,\frac{z'_0}{2}} \times 0.5^{z'_1+z'_2} \times \begin{pmatrix} z_1 \\ z'_1 \\ \end{pmatrix}
\times \begin{pmatrix} z_2 \\ z'_2 \\ \end{pmatrix} \times
W_{\mathbf{Z}}(\mathbf{z'},t)\mathrm{d}v' \\
- (D+\sigma H[2.0] W_{\mathbf{Z}}(\mathbf{z},t))
\end{multline}
\begin{equation}
\frac{d\mathbf{c}}{dt}
= D(c_f - c ) - \alpha \int \mu (\mathbf{z},S) W_{\mathbf{Z}}(\mathbf{z},t)\mathrm{d}v
\end{equation}
Objectives
The aim of studying the behaviour of this model is to investigate how
growth conditions $D, S_f$ and time modulate the development of the population
in state space $W_{\mathbf{Z}}(\mathbf{z},t)$. In particular we are interested in
finding how the number of bacteria without plasmids $W_{\mathbf{Z}}([z_0,0,0],t)$ in
the culture progresses, and how this depends on the various parameters in the model.
Proposition: Adding contention
We need to introduce a modification to equation 3 (the state dependant
growth rate) in which the equilibrium between $z_1$ and $z_2$ features.
This modification is to take into account that each toxin should be
inhibited by anti-toxin for maximal growth. A possibility is that we
consider that for each toxin anti-toxin pair:
- the genes produce toxin at a constant rate dependant on the numberof copies $k_1\times z_1$,
- the anti-toxin genes produce anti-toxin at a rate dependant on the number of copies $k_2\times z_2$,
- anti-toxin instantly and irreversible kills the toxin in a stochiometric manner.
- undestroyed toxin disappears at a constant rate, by dilution and other pathways $k_3$,
- the concentration of toxin is at a dynamic steady state, i.e. the rates of production and disappearance are equal.
- growth in inhibited in an exponential manner by free toxin with a characteristic $IC_{50}$.
This model of toxin anti-toxin interaction takes into account the
bacteriostatic nature of most such toxins, and the presence of
measurable $IC_{50}$ values. Clearly a more realsitic model would need
to take into account cell volume, protein synthesis rates etc.
Nevertheless, this simple model gives:
$$
\begin{align}
\mu &= \mu_{0} \times e^{-\frac{[Toxin]}{IC_{50}}} \\
[Toxin] &= max(0,\frac{k_1z_1-k_2z_2}{k_3}) \\
\mu &= \mu_{0} \times min(1.0,e^{-k_a(z_1-k_bz_2)}) \\
k_a &= \frac{k_1}{k_3 \times IC_{50}} \\
k_b &= \frac{k_2}{k_1}
\end{align}
$$
Incorporating 2 toxin anti toxin pairs, if we assume
that the production rate ratio is the same for both, $k_b$, is
independant of the system we have:
\begin{equation}
\mu = \mu_{0} \times min(1,e^{-k_a(z_1-k_bz_2)})) \times min(1,e^{-k_a(z_2-k_bz_1)}))
\end{equation}
where $\mu_0$ is given by equation 3. For each toxin the efficiency
parameter is a measure of ratio of toxin accumulation in cells with one
gene copy and without anti-toxin to the $IC_{50}$.
Program
We made a program to simulate the model corresponding to these equations, using a bayesian algorithm.
For speed of execution, is programmed in C, using the GNU scientific library.
Plots were made using R with ggplot2.
[download]
Results
We can see that with D growing, corresponding to more bacterias growing, more bacterias tend to lose their plasmids.
Plasmids do not have enough time to be replicated
Bibliography
- ↑ Fredrickson AG, Ramkrishna D, Tsuchiya H (1967) Statistics and dynamics of correct pro- caryotic cell populations. Mathematical Biosciences 1: 327–374.
- ↑ 2.0 2.1 Shene, C., Andrews, B.A. & Asenjo, J.A. Bioprocess Biosyst Eng (2003) 25: 333. doi:10.1007/s00449-002-0313-x
