First of all today is my birthday.
It is about the time that we have to write about what we have been doing over this summer. In order to do so, we have planned to cover the following sections in the paper:
-Abstract
-Introduction to quorum sensing
-Introduction to MC (membrane computing):
a -P systems in general
b-P system in this framework
-membrane structure, objects (agents), and interaction rules
-Gillespie's Algorithm :
a- Modifications and Implementations
b-Rate of reactions (reactions' constant)
c-Time (global clock)
-Results
-Conclusion
-References
To give you an idea how would be the content of this paper, I post the abstract:
In this article, we present a membrane computing model of the quorum sensing system in genetically engineered Escherichia coli (E.coli) bacteria. Quorum sensing is a way of communication between unicellular bacteria. In this way of communication, bacteria release signaling molecules to their environment. Other bacteria are able to receive and recognize the signals. Measuring the accumulation of signals in the environment of a bacterium, enables it to sense the number of cells (cell density) around it. Many species of bacteria use the information obtained to coordinate their gene expression in response to the size of their population. The regulation of genes according to the cell density is called Quorum Sensing. Moreover, membrane computing is a powerful model for computation of some features of living cells. MC is inspired by biological membranes structures and functions. In this model, Each bacterium has two membranes, where one of them is enclosed by the other one and therefore each bacterium has to enclosed regions. Each region is associated with a multiset of objects and a set of evolution rules is assigned to each membrane as well. The application of the rules is computed by a compartmentalized version of Gillespie's algorithm in a way that in each time step,only one rule is applied to the system. This model allows us to observe the behavior of each individual bacterium as well as the emergent properties of the whole population. It also enables us to manipulate some factors involved in the simulation to understand the effects of them in individual behaviors as well as colony behavior. Our simulation demonstrates that at low bacteria densities all the bacteria produce GFP protein which emits light but as the population grows by division, they regulate their gene expression and they do not produce GFP longer and after a while they turn dark.
This is what I have been doing this week and I will be busy doing it for next couple of weeks.
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