Team:Calgary Software/Project

We use evolutionary and genetic strategies, which are useful modeling methods, especially when coupled with agent-based designs. We created EvoGEM, a design that is based on the registry of genetic parts provided by the iGEM competition.

This design uses the paradigm of evolution to select efficient designs and produce a product or output that is generated independently by the system. EvoGEM uses 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
• transcription and translation.

The program assembles generations from a selection of parts - retrieved from the iGEM registry - and creates genetic circuits. 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 allows for minimal assumptions about the behavior of the system, when coupled with the empirically proven evolutionary design, creates a superb system that is able to both simulate and develop iGEM circuits.

Evolution involves the changes of inherited traits in a population for successive generations. Each generation carries genetic information, expressing certain characteristics. Mutation enables manipulation of these traits as well as genetic recombination. Evolution is a result of heritable traits becoming more prevalent or rare. Agent-based modeling is a computational method that replicates the behavior and interaction of individual components of 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 - emergence

Multiple agents are simulated in an environment to simulate and hypothesize the actions of complex phenomena. .

For EvoGEM to improve as a model, it needed to examine organic compounds and biochemical reactions. Using a set of Perl scripts, we retrieved key pieces of data from the source information of the registry. For every part, we needed to find:

- its type. - its function. - whether it codes for a protein. - how well the parts work. If the part coded for a protein, we retrieved its DNA sequence. We, then, found the amino acid sequence (using the BLAST algorithm) from UniProt, which is a large database of proteins. If the protein catalyzed an associated prosthetic or a biochemical reaction, we retrieved additional information from ChemSpider - a chemical database - to find the data characterizing any compounds that the particular enzymatic protein catalyzes. Finally, we stored the data in a database during run-time of EvoGEM.

Initially, EvoGEM used an RNA polymerase class as the single device that functioned as an RNA polymerase, mRNA, and ribosome. 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, we implemented another class, known as mRBS, to allow for simultaneous transcription and translation.