Stochastic Gene Expression
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Gene expression is the production of macromolecules from a DNA sequence. This process always involves transcription and may include mRNA processing, translation, and further post-translational protein modifications. The regulation of each of these steps essentially controls information flow from DNA to RNA to protein: the central dogma in molecular biology. A stochastic process involves a factor which cannot be predicted and therefore is best described as a probability distribution rather than having a measured, defined value. The current concept of stochasticity is based on quantum mechanics, where stochastic models have been usefully applied to the understanding and manipulation of subatomic particles. Stochastic gene expression is the expression of a macromolecule from a DNA sequence where the process involves a random factor that cannot be determined. In this thesis I will argue that while a stochastic element to gene expression may be present, it has not yet been unequivocally demonstrated. Although our understanding of biological processes has greatly expanded in the last century, it is still incomplete. This lack of knowledge results in assumptions being made when creating models of complex cellular processes. Deterministic models require the measurement of all the factors involved, including concentrations, reaction rates and sub-cellular localizations. Stochastic models circumvent this need by adding a random element to the model to account for the unknown. If the model can then fit the data it has been assumed that an inherently stochastic process is at work. While the development of sophisticated stochastic models are extremely useful, using such a model in an attempt to prove that a mechanism is inherently stochastic is flawed circular logic. As modern biology moves from a genomics to a systems biology paradigm the fidelity of information flow from DNA to protein to networks is an important factor to consider. If we are to discover how the cell functions, basing this understanding on solid scientific data is of the utmost importance.