Computational modelling of genetic processes

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There are many parallels between computer science and genetics.  The structures in both store information as lengthy sequences, sometimes in the form of zeros and ones or perhaps as DNA.  In both fields, we perform operations on those sequences through different mediums, such as electronic circuitry or proteins.  We now have an ever increasing wealth of genetic information at our disposal.  Consider that the genome of a single human being is over three billion nucleotides in length.  It is essential that we find interesting patterns amongst the plethora of data in order to understand the inner-workings of even a cell.  Computer science allows for a natural way to study these patterns, based on its similarity to biological structures and its ability to process massive amounts of information quickly.  By establishing new connections between the two fields, we can then use methods from computer science towards the study of analgous processes in biology.  Furthermore, we could uncover new methods of computation through the guise of biology.  For example, stichotrichous ciliates, a group of single-celled organisms, perform a descrambling process on their DNA to create genes capable of generating proteins.  Descrambling of strings is a mechanism well studied and understood in computer science.  By modelling this process in computational terms, we can create algorithms for studying ciliate genome data.  Moreover, we can attempt to comprehend the exact manner and machinery used for descrambling in a cell.  In parallel, this provides us with a new model of computation found in nature. My short term goals include the comprehension of the genome gymnastics of ciliates as well as the process of frameshifting, a twist on protein translation that allows for multiple proteins to be produced from a single gene. My long term goal is to use computational analysis to converge towards a deep scientific understanding of various biological processes.