Team:Paris Liliane Bettencourt/Project/Population counter/model
From 2010.igem.org
Introduction
We aim at setting up a biological device counting random events occuring in a microfluidic device (see figure 1). These events are recombinations in the cells of the device, stimulated by pulses of arabinose coming from the tunnel. Once a certain number of pulses have been triggered, we expect our device to start emitting green fluorescence. First, we shall describe the dynamics of the population of bacteria in our device as for instance its recombinations, which further lead to the rise of the concentration of produced AHL. AHL is a quorum sensing molecule that, once a certain concentration threshold is reached, triggers in our device the production of GFP. It then alerts us of the end of counting. To count how many recombinations (so how many events) are needed to get the quorum sensing response, we shall determine the role and assess the value of a few critical parameters in our model.The process
To be able to model the system, we first need to have a feeling of how the components of the system react and interact with one another. First, we send a pulse of arabinose through the tunnel which triggers, with a certain probability, the recombination of some bacteria. Those bacteria are then expected to become red (RFP expression) and start producing LuxI, which in turns promotes the production of AHL. AHL will play two roles:- it can bind with intracellular LuxR, which is a constitutively expressed protein so that its concentration inside the cell will be considered as constant. We call it LuxRf as long as it is free from AHL, LuxR* otherwise;
- it can cross the cell membrane to bind with LuxR of other bacteria in the device.
Figure 1: Scheme of the microfluidic device
The physical model
We can develop the information we have in a system of equations:-
LuxI
Once a bacteria has recombinated, there is necesarily a latent time before LuxI is actually synthetised. There is then some more time before a bacteria produces full rate. The amount of LuxI produced inside a bacteria stays in this very bacteria, and it is commonly accepted that the concentration of LuxI reaches a plateau and keep this value. According to the data found in the literature, we chose to set the latent time to 10 minutes. Then the concentration increases during 10 minutes linearly until the maximum concentration is reached. -
LuxR
Let's call R the constant concentration of LuxR in a cell (how "constant" is it?). Some of it is binded with AHL and we called it LuxR*, so this gives:
The complexation between LuxR and AHL gives a chemical equilibrium determined by the constant Kreac:
so at the equilibrium:
which gives:
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AHL
Following the paper "A synchronized quorum of genetic clocks" by Hasty and al., we define the intracellular concentration of AHL, [AHLi], and the external one, [AHLe].
The concentration of AHL in the room [AHLe] decreases as the microfluidic device evacuates it, which gives birth to a diffusion term in the equation caracterized by the kinetic constant kdiff. In parallel, the internal and external AHL concentrations change as AHL is produced by the cells and can go throuh the cell membrane (kinetic constant kI and kmembrane respectively). For now, we shall assume the diffusion of AHL inside the device is instantaneous.
The first term says that if the cell produces LuxI, then it produces AHL at a certain rate. The second term concerns the exchange between the cell and the medium, and is proportional to the difference of concentration.
where kdiff is the constant of diffusion, related to parameters of the fluid and of the device, kmembrane is the same as previously, but is now compensated by d, the cell density, so that the exchanges between cells and the medium is balanced. A note on diffusion further.
Here we consider only the case where the concentration of AHL is homogeneous, ie the concentration inside and outside the cell are the same. This is justified since the caracteristic time of diffusion of AHL between the outside and the inside of a cell is short compared kI. The contrary would lead to a discussion we started in paragraph \ref{modif_model}. We end up with this equation:
The synthesis of LuxI.
Above, we introduced directly the concentration of LuxI without giving more details. However, we need to model the production of LuxI to calibrate the counter. This means modelling how the bacteria evolve in the device and how they react to arabinose.- Arabinose is modeled simply in the model with a diffusion equation:
with the boundary condition at the border between the device and the tunnel corresponding to a positive value if a pulse is being sent, and 0 otherwise. Thus we get the profile of arabinose independently of everything else, except the pulse itself. - The device may have several sizes, however one device will not ever host more than 2.105 cells. We can indeed approximate the maximum number of bacteria as the ratio between the volume of a device and the volume of a cell. Moreover, considering both populations, switched and unswitched, we will consider the bacteria are in exponential growth, and their excess is evacuated through the path to the tunnel. Therefore, we need a model for the behaviour of the bacteria (movement and birth/death) that allows us to deal with a few cells \footnote{at the beginning, when we only have a few recombinated cells.} up to more than 105 cells. An individually-centered model would be computationnaly inefficient, while a continuous model would perform wrong computations for small numbers of bacteria.
I have implemented something relying on a binomial distribution: among n people, we pick a random number of them to move, or give birth; this random number being ruled by a probability p proportional to the gradient of concentration of bacteria. Discretising time and space, one can draw at every step time a random binomial number\footnote{X being non-negative if the first argument in the binomial law below is non-negative, negative otherwise}:
where...
Algorithm and simulations
In this paragraph, we use several constants suggested in the paper of Hasty et al. (see references), or sometimes some approximations. We shall mention these numerical values along this paragraph.Solving the diffusion equation using an implicit scheme
mainly refers to NRCStochastic Gillespie algorithm
else
For now, we use a simple spatial discretisation scheme, assuming lateral diffusion is instantaneous\footnote{this may make simulations inaccurate and thus yields a piece of the program to be improved}, so that diffusion is only considered along one axis (the one from the tunnel to the top of the device (see the first figure).
Discuss the existence of two phases, the critical parameters being the diffusion coefficient of AHL and the time between two pulses.
Although we have been able to count six pulses thanks to the previous graph of AHL, it is not always possible to do so. For instance, if we shorten the time between two pulses, keeping the same diffusion coefficient, then it is no longer possible to distinguish pulses:
References
- Hasty...- Numerical recipes in C, ??, CUP
- Gillespie...
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