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Team:Calgary/Modelling/MC/Intro
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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 bac- teria 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 com- puting 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. | 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 bac- teria 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 com- puting 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. | ||
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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. | 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. | ||
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Revision as of 18:39, 21 October 2009
UNIVERSITY OF CALGARY
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MODELLING INDEX
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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. |
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Abstract
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 bac- teria 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 com- puting 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.
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The Effect of Variation of AI-2 on the Production of GFP
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The Effect of Variation of LuxPQ on the Production of GFP
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