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Team:Calgary/Modelling/MC/Intro
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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. | 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. | ||
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Revision as of 19:56, 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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Introduction to Our Membrane Computing Approach
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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. Membrane Computing (MC)
Membrane computing is a branch of natural computing that was introduced by Gheorghe Paun in 1998 [5]. This new field of computation is rapidly growing and its formalism is used in many research areas. It should be noted that this approach is also used in sciences other than biology such as economics and statistics. For instance, membrane computing is used to solve Boolean satisfiability problems (SAT) and the traveling salesman problem (TSP) in economics [5].
In regard to biological models, MC could be thought of as a framework for devising compartmentalized models that are used to simulate cell functioning. In fact, MC draws inspiration from nature (biology) to provide a more realistic architecture of biological systems in computational simulations by introducing abstract computing devices that are called P systems. A P system is a hierarchical arrangement of membranes. A skin membrane separates the system from its environment. There are also two other kinds of membranes defined in P systems: elementary membranes and non-elementary membranes. Membranes that do not have any membrane inside are called elementary membranes whereas those membranes that enclose others are referred to as non-elementary membranes. Each membrane defines a region that is indicated by the space that is enclosed by the membrane. For non-elementary membranes, regions are defined as the spaces between the non-elementary membranes and the membranes embedded in them [1, 5]. |
