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	<li><span id="Drasdo">Course material Drik Drasdo : Modelling of multi cellular tissues, Paris VI lectures 2012</span></li>		<li><span id="Drasdo">Course material Drik Drasdo : Modelling of multi cellular tissues, Paris VI lectures 2012</span></li>
-	<li><span id="Atlas">Atlas of Xenopus Development G.Bernardini/ M.Prati / E.Bonetti/ G.Scari</span></li>	+	<li><span id="Atlas">Atlas of Xenopus Development G.Bernardini, M.Prati, E.Bonetti, G.Scari</span></li>
	<li><span id="Xenbase">Nieuwkoop & Faber (Xenbase.org) retrieved on 15 september 2012</span></li>		<li><span id="Xenbase">Nieuwkoop & Faber (Xenbase.org) retrieved on 15 september 2012</span></li>
	<li><span id="Pollet">Nicolas Pollet's data</span></li>		<li><span id="Pollet">Nicolas Pollet's data</span></li>

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Revision as of 14:14, 22 September 2012

Plasmid splitting

Overview

The idea of this model is to better understand the consequences of our experimental protocol
Our protocol consists in injecting a large amount of plasmid at the 1-cell stage, directly into the cytoplasm. When cells divide, the initial quantity of plasmid is split between daughter cells. Only a very infinitesimal proportion of plasmid will be integrated in the nucleus so most of the "effective" plasmids containing our constructs comes directly from this first injection.
This model has been created in order to answer critical questions about our experimental protocol :

• What is the average amount of plasmid we can expect to find in a cell at a given time?
• How uniform is the plasmid repartition among cells?
• Which known mechanisms in morphogenesis could play a role in the plasmid repartition?

Sketch view of plasmid repartition after several mitosis

Hypothesis

Various hypothesis are needed in order to model the plasmid repartition in time. Some of them are related to biological knowledge and will allow to get insight into the underlying mechanisms while others are more related to modelling choices and computational tractability.

1. Time between successive mitosis can be modelled using an Erlang distribution
The Erlang distribution with factor k is the sum of k exponential distributions with same mean. The use of this distribution is motivated by considering that biologically, a cell has to finish several elementary biological processes (such as replicating all its chromosomes) before being able to divide. Assuming (with over-simplification) that each of these processes has the same mean duration and follows an exponential law, as commonly assumed for Poisson processes, the overall time between two mitosis events will follow an Erlang distribution. (Ref : Drasdo 2012)
2. Plasmids repartition occurring at mitosis can be represented by a normal distribution
This seemed the more straightforward and natural choice of repartition. This hypothesis being closely related to the fundamental dynamics of mitosis during early cell divisions and to cytoplasm's physical properties, it will be further discussed in this page.
3. On the considered stages of development, only cell division occurs
This hypothesis is more for sake of simplicity than based on biological ground. The team obviously acknowledge the central role of cell death processes, and mainly apoptosis in morphogenesis, but this process is much more important for cell differentiation than it is for the overall growth rate (in terms of number of cells). Being mainly interested by the later, we will only consider cell growth.

Model description

Elementary events

Xenopus' embryogenesis is modelled as a Poisson stochastic process where two distinct but successive events can happen :

1. A given cell divides, giving birth to 2 daughter cells. These new cell will divide themselves after a lapse of time represented by an Erlang distribution of variable mean and factor k=12
2. The amount of plasmids initially present in the mother cell is split between daughters following a normal distribution

The values used to represent the mean time between mitosis and normal distribution parameters will be discussed in the results and calibration sections.

Simulation

Realisations of this stochastic process where simulated using the convenient variable time-step Gillespie Algorithm implemented in Matlab by our team.

Calibration

As this model has been made in order to better understand how our experimental choice of plasmid injection instead of more complex nucleus integration would affect the efficiency of our constructs, calibration is of much importance.

Growth rate

The first step after having implemented the algorithm was to tune its parameters in order to match experimental data. As the growth rate (or mean time between divisions, one being the inverse of the other) is a key parameter in order to have simulations with representative time scales, we carefully calibrated it. Using different available data about Xenopus' development, we were able to retrieve its growth in time, and along development stages (data at 25°C for X. Tropicalis) : (ref : Atlas of Xenopus Development, Xenbase, N. Pollet's data) Using this growth curve as target, we adjusted a piecewise linear growth rate function of time to match our simulation with data. Given that early divisions and most of morphogenesis is a very complex phenomenon, using a single growth rate was far too unrealistic. Moreover, differentiation is a key factor in explaining why the overall growth rate is to vary in time. In the end a well enough fitting growth rate function is given as an interpolation of : (Insert table with params)

Plasmid repartition characteristics

Another important parameter of our model is the plasmid repartition between daughter cells. We first considered a simple normal distribution centred on 50% plasmids in each cell with a variable standard deviation representing inhomogeneity in both plasmids' spatial repartition in the cytoplasm and unequal volumes of daughter cells. Focusing in the later phenomena (the former being very hard to capture and assuming the volumetric effect was preponderant) we measured roughly the differences in cells radii from microscopical data at different stages to retrieve volume disparities.
But experimental results where we had injected GFP carrying plasmids seemed to show the distribution was much more unequal and a bi-modal distribution could be more realistic. Anyway, even with our simple normal distribution simulations shows that quickly, the standard deviation in the average number of plasmid by cell becomes larger than the average amount of plasmid itself. This shows a strong inhomogeneity and could be sufficient to explain our observations.
Therefore, a precise quantification of plasmids, specifically in the very first stages would be necessary to go further. We now believe that cytoplasm is much more dense that we thought and that plasmids nearly don't diffuse at all in the early stages. This belief comes from observing that GFP tagged plasmid seemed to only be expressed in some randomly selected tissues or organs. By coupling this information with fatemaps, it could be possible to quantify precisely how many divisions occur before plasmids get split. This would radically change the repartition model as half or 3/4 (or more) of the organism could be totally plasmid free if the two first mitosis occur without diffusion of plasmids. At last, this could help to improve this otherwise convenient injecting technique, by performing multiple small injections rather than a single big one.

Initial plasmid quantity

In order to estimate how many plasmids are injected in the egg, we performed a back of the envelope calculation taking into account :

• The mass of plasmid injected
• The weight per base of a double stranded plasmid
• The average length of our plasmids in base

The final figure is : 3.10^7 plasmids

Results

Conclusion

References