Predicting The Design Of Pathogens DNA

Studying physics means that you get a good grasp of basic chemistry as well. Although there are many complicated exceptions that chemists specialise in and I would never claim to even begin to understand a fraction of all chemical knowledge there is something reassuring about physical chemistry; the way it can all be explained with interacting forces and electron movement and how inorganic chemistry follows the basic rule that everything must end in a lower energy state. When it comes to organic chemistry it begins to pass out of my understanding and by the time biochemistry is reached I am completely lost. It seems to me almost impossible to create the link between chemicals reacting and massive molecules with different reactions at different points along its chain with the ability to coil and move with the help of other molecules.

DNA spends most of its time moving itself into different configurations so that it can be copied, replicated, modified and checked by a vast array of proteins build for these purposes. Even though these processes are extremely complicated and intricate there is still a possibility for a holistic understanding by looking at the thermodynamics of the process, of course to do this some modelling is required. The model designed took a statistical approach to understanding the DNA behaviour by breaking the DNA down into a series of consecutive sets of 150 base pairs at a time.

This model, and a set of prediction algorithms that came with it, were able to recreate the nucleotide formations at give temperatures and with given concentrations of base pairs. The DNA that was being and then compared to was that of bacteria as microbial DNA represents some of the simplest structures that DNA creates. By looking at the mechanical qualities of the bacteria DNA at with different volumes of guanine and cytosine and also how these qualities vary with bond enthalpy provides the data which can then be used to verify the veracity of the model. The study ends by roughly showing that the predictions made by this model can also be applied to that of other bacterium’s genomes. It is also possible that the algorithms generated could provide recommendation for various conditions to optimise the outcome of synthetic genetic experiments in the future.

Paper links: Superstatistical model of bacterial DNA architecture


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