Team:Calgary/Modelling/MC/Intro
From 2009.igem.org
Introduction to Our Membrane Computing Approach
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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.
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].
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