Team:Calgary Software/Project

From 2008.igem.org

(Difference between revisions)

Revision as of 04:19, 24 October 2008

Introduction

Evolutionary and genetic strategies have been a very useful method in modeling and have been coupled with agent-based designs. In this stream of modern heuristics, EvoGEM has been created, based on the registry of genetic parts provided by the iGEM competition. In this design methodology, the paradigm of evolution is harnessed in order to select for efficient designs and produce a product or output that is generated autonomously by the system. EvoGEM utilizes the strategies of genetics and evolution to simulate an environment inside of a prokaryotic cell. This entails various events and structures that are present inside of the organism such as RNA polymerase, messenger-RNA, ribosomes, as well as transcription and translation. By taking into account the essential processes necessary for the cell to function, generations are assembled from a selection of parts - retrieved from the iGEM registry - and genetic circuits are created. The circuits proliferate in each generation and the best possible combination of parts is selected. Eventually, the best circuit is obtained from the system and a desired functionality is returned. The powerful agent based logic which allows for minimal pre-assumptions about the overall behavior of the system, along with the empirically proven evolutionary design create superb system that is able to both emulate and develop iGEM circuits.

EvoGEM was briefly presented during the 2007 iGEM jamboree and has sparked quite a lot of interest amongst the different teams. This summer, our team plans to further develop the fitness function EvoGEM employs, introduce more complex pattern recognition, and test the system under a much larger search space than before. The final goal is to produce a system sophisticated enough to rebuild working designs from previous years' teams' projects, as well as intelligent enough to simulate successes and failures of working and non-working systems, respectively. The main focus of this project is to build perl scripts that will support EvoGEM's requirements of a flat file registry, create an Objective-C based graphical user interface (GUI) in order to make the software-user interaction easy for any potential users, develop the EvoGEM code to include the behaviors specified before, and create a simulation of the processes in the cell such as transcription and translation.

Principles of Evolution, Agents, and Modelling

Evolution involves the changes of inherited traits in a population from successive generations, one to another. Genetic information is carried through each generation and certain characteristics are expressed. Mutation enables manipulation of these traits as well as genetic recombination. Evolution is the result from the heritable traits becoming more prevalent or rare.

Agent-based modeling is a computational method of replicating the behavior and interaction of individuals within a network such that their overall effect on the system can be observed. This involves many different aspects, including game theory, evolutionary programming, complex, systems, and emergence. Multiple agents are simulated throughout an environment to emulate and hypothesize the actions of complex phenomena.

Data Retrieval and Storage

To improve EvoGEM as a model, organic compounds and biochemical reactions needed to be examined. Using a set of PERL scripts, key pieces of data is retrieved from the source information of the registry. For every part, the type must be known, its function, whether it codes for a protein, and how well the parts work. If a part codes for a protein, its DNA sequence is retrieved. The amino acid sequence is found (using the BLAST algorithm) from UniProt, which is a large database of proteins. If the protein has an associated prosthetic or a biochemical reaction, this is also examined. From there, additional information is retrieved from ChemSpider - a chemical database - to find the data that characterizes any compounds that are involved with that particular protein. Finally, the data is stored in a data base during run-time of EvoGEM.

Modeling

Figure 1.0 - EvoGEM in its initial stages. The RNA polymerase are the violet spheres, the long connected strand is the DNA, and additional metabolites are blue or green spheres

Initially, EvoGEM used an RNA polymerase class as the single device that had the functionality of the RNA polymerase, mRNA, and ribosome, as seen in Figure 1.0. Objects of this class were able to transcribe and translate the DNA BioBricks directly into protein molecules. This had provided the necessary level of functionality earlier, but in order to more correctly simulate the processes in a cell, we needed to implement new mRNA and ribosome classes. In addition, another class known as mRBS was implemented to allow for simultaneous transcription and translation processes.

Navigation

The Project

Home	The Team	Parts Submitted to the Registry	Modeling	Notebook

@@ Line 21: / Line 21: @@
 == Modeling ==
-[[Image:EvoGEM_modeling_pic1.jpg|frame|Figure 1.0 - EvoGEM in its initial stages. The RNA polymerase are the violet spheres, the long connected strand is the DNA, and additional metabolites are blue or green spheres]]
+[[Image:EvoGEM_Modeling_pic1.JPG‎|frame|Figure 1.0 - EvoGEM in its initial stages. The RNA polymerase are the violet spheres, the long connected strand is the DNA, and additional metabolites are blue or green spheres]]
-Intially, EvoGEM only had RNA polymerase (represented as purple spheres), a central DNA component, and additional metabolites produced by the system (shown as smaller green or blue speres).  It did not account for transcription and translation as separate processes.
+<div align=justify> Initially, EvoGEM used an RNA polymerase class as the single device that had the functionality of the RNA polymerase, mRNA, and ribosome, as seen in Figure 1.0. Objects of this class were able to transcribe and translate the DNA BioBricks directly into protein molecules. This had provided the necessary level of functionality earlier, but in order to more correctly simulate the processes in a cell, we needed to implement new mRNA and ribosome classes. In addition, another class known as mRBS was implemented to allow for simultaneous transcription and translation processes. </div>
 == Navigation ==