Natural computatoin with genetic processes

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Natural computing investigates models of computation inspired by nature. Major objectives of this area include improving our understanding of natural processes by studying computation, devising new methods for creating computation such as biocomputing, and creating algorithms to analyze data from exhibited characteristics. We are especially interested in biological processes in the area of DNA editing. Many changes occur to DNA over the course of evolution, ranging from small changes, to large sections called transposable elements which can copy/cut and paste from one position of a genome to another. These larger segments are important as they can cause harm by inserting themselves into necessary genes, and are thought to be a major factor in speciation. They make up 45% of the human genome, however they are not currently active. We will model their evolutionary movement and predict their activity and age by examining patterns that occur across different organisms including humans. We will also examine their ability to be used for custom computation as transposable elements are being lauded for their potential with gene therapy, which changes the DNA of a living organism, usually to treat disease. Using theoretical methods, we will explore more general types of biocomputation with transposable elements. It had previously been thought that DNA within a single individual was relatively static and did not change significantly within a lifespan or between multiple cells of the same individual. One exception to this is an organism called ciliates which are known to perform large amounts of DNA rearrangements, necessary for their survival. But this year, DNA rearrangements have been found to occur in fish. In fact, human DNA editing between different cells of the same individual has been discovered and is thought to be related to disease. Since it appears to be occurring more broadly, studying the established patterns in ciliates is beneficial. We are interested in understanding these rearrangements using natural computing, and predicting how they could occur biologically. We are creating computational models and simulations to perform such predictions.