Simulation and Analysis of RNA Polymerase Motion on the DNA
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RNA Polymerase is an enzyme that is frequently used to clone genetic material. However, backtracking seems to turn the process remarkably inefficient, because it leads to a lot of waste of raw material, making the cost of production less profitable.In this thesis, an already existing model that simulates the movement of RNA Polymerase transcription over the DNA with the occurrence of backtracking is analyzed and simulated in MATLAB. The objective of the thesis was to improve the model to fit with the experimental data that was available, such that the productive yield could be predicted from various strands of genes. If a model that describe the natural phenomena is found, it is assumed that it could be used to find a method of regulating this process.RNA Polymerase in an important enzyme that is present in all parts of the synthesis of RNA. It is responsible for transcribing strands of RNA that contains genetic information, which is sent to the ribosomes for translation into functioning amino acids. Backtracking is a phenomenon where the RNAP releases nascent strands of RNA that are not used for coding, and repeat the process over from the transcription start site. The model is classified as a Brownian ratchet type model and is based on a hypothesis that the movement of the RNA Polymerase is solely dependent on the forces acting on it. It is thought that thermal fluctuations create oscillations by the trigger loop, which is a component in the RNA Polymerase. This, in addition to other forces, is thought to influence the transcription and decide whether the RNA Polymerase will backtrack or not. A sensitivity analysis was conducted on the parameters of the model that were thought to be uncertain using the available data, to find a set of parameters to use as a basis for the parameter estimation. The parameter estimations were performed using the values found from the sensitivity analysis, using a stochastic global optimization algorithm, in order to find a set of parameters that would make the model fit the experimental data. No parameter sets were found, that improved the prediction success of the model, where the best fitness of any of the parameter sets obtained was found to be 0.16.Some of the assumptions that the model is based on were also analyzed and simulated, to improve the overall fitness of the model. The free energy calculations that are central to the calculation of forces were remodeled and tested, and the significance of the trigger loop and its role in the model were analyzed and discussed. A model that predicted the experimental data was not obtained. However, some aspects of the model that were analyzed were determined to be correct compared with the hypothesis, and further development of the model can possibly produce better results.