"
Team:Calgary/Modelling/MC/Intro
From 2009.igem.org
(Difference between revisions)
| Line 138: | Line 138: | ||
<br> | <br> | ||
| + | <br> | ||
</div> | </div> | ||
Revision as of 01:03, 22 October 2009
UNIVERSITY OF CALGARY
|
MODELLING INDEX
|
A TOUR OF THE UNIVERSITY OF CALGARY iGEM TEAM
We've reached modelling, the fifth stop on our tour! We've looked in to two different methods of modelling our system: Differential Equation Based Modelling and Membrane Computing. Here, you can explore the similarities and differences, as well as the functions of each method. As well, you can find the results of our characterization of the signalling pathway. Once you're done, we'll move on to the Second Life component of the project HERE. |
|
Introduction to Our Membrane Computing Approach
Copyright: "Spikey" is the logo of Wolfram Research Inc. Quorum sensing is the way bacteria communicate with each other; they release signaling molecules to their environment and other bacteria receive and recognize the signals. Many species of bacteria use the information obtained to coordinate their gene expression in response to the size of their population, which is known as Quorum Sensing. In this article, we present a novel model of a synthetic Autoinducer-2 signaling system in genetically engineered Escherichia coli (E.coli) bacteria using the recently proposed Membrane Computing (MC) framework. Membrane computing is a branch of natural computing that is inspired by biological membranes structures and functions and is used for modeling features of cells in biological systems. This model allows us to observe the behavior of each individual cell as well as the emergent properties of the whole population. It also enables us to manipulate factors involved in the simulation to understand their effects in individual as well as colony behaviors. Having defined our model in terms of compartments and interactions rules, any biological system that can be explained in terms of compartments and their corresponding rules can be sim- ulated with this platform. In other words, we have developed a biological language for modelling biological systems using MC framework. Why Model? During the past two decades, biology and computer science have been converging; many biologists use mathematical and computational models as powerful tools to gain a deeper understanding of biological systems [7]. Given that molecular biology experiments in vitro are very expensive and time consuming, building models of biological processes as a preliminary step helps to circumvent some of the drawbacks of performing hypothesis-testing in the wet lab. This is why we feel that computational modeling is important and useful. Particularly with the extent of synthetic biology, many of the biological systems that are being researched could not be found in nature because they are genetically engineered, so their behaviors are unknown and need to be characterized. For instance, in this project, a synthetic autoinducer-2 (AI-2) signaling system constructed in E.coli is taken from its natural counterpart in Vibrio harveyi, bypassing its small regulatory RNA networks. This engineered biological system shows new behaviors that are not observed in nature and need to be studied and characterized. Based on the reasons given above, using models could provide a faster and cheaper shortcut for biologists to gain a better under- standing of the newly engineered system. However, it should be stressed that models, regardless of their accuracy, could not be used as a replacement for vitro experiments; however, they could be used as a preliminary step for characterizing the system and as a shortcut for biologists to gain a better understanding of the newly engineered system. Emphasizing compartmentalization as a cornerstone feature of cells, membrane computing (MC) is a powerful approach for studying reactions in biological systems. The most important feature of this approach that we would like to emphasize is that it could create a common modeling language that is mathematical and precise and could be understood by biologists. MC allows the user not only to focus on interactions at the level of an individual cell, but also to observe the emergent properties of entire cell populations. The MC approach seems to be ideal for the construction of a quorum-sensing model since compartmentalization of the signal and the cascade proteins are critical features of this process. |
