What parts/topics/branches of computer science are really utilized in biotech/genetic engineering?

Image processing, machine learning

Some of these topics are on the boundaries of CS/EE/Physics/Math, but they are all important in biology research. Signal processing theory - Fourier transforms/series, etc. These topics are immensely important in determining the structures of proteins in particular and in analyzing systems where you can't directly measure the quantity you are really interested in. Good examples: mass spectrometry, NMR and protein crystallography all produce data that are very indirect measurements of the quantities that researchers are interested in. Optimization theory - can't overstate how important this stuff is. Applications of optimization range from regression to modeling metabolic systems and DNA base-pairing. Often used to find the most-likely states of a system when there is a physical model of the relevant biology available. Computer simulations - often used when the physical model is too complex to optimize or you are interested in the dynamics of a biological process, e.g. the binding of a GPCR to a ligand, etc. Machine learning - often used to build predictive or discriminatory models for systems for which there isn't an obvious physical model. For example, which peptides will bind to the MHC. Image processing/analysis - related to signal processing and machine learning, used to handle large volumes of microscopy data which could not be processed manually. Distributed computing - biology is now a domain where (for some problems) there is more data that you could plausibly manage or process on a single computer. Genomics and microscopy are examples of domains where this really matters.