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	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 :		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 :
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	<h2>Results</h2>		<h2>Results</h2>

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Revision as of 23:52, 17 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?

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.
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 :

Results

Conclusion

References

Other possible topologies

With auxin in the external medium:

With a specific receptor organ: