Biological or clinical phenotypes and the cellular response to stimuli arise from the biochemical state of a cell or tissue which, in turn, is the result of the composition of biomolecules and their organization in the cell. At present, there are neither a comprehensive theory nor computational models that generally predict phenotypes or cellular responses to signals. Nevertheless, such predictions are frequently attempted, particularly in clinical research, exemplified by personalized/precision medicine. It is therefore an important question which type(s) of molecular information, either by themselves or integrated with other data, will increase the ability to predict phenotypes from molecular measurements beyond what is possible today.
The biochemical literature indicates that proteins are particularly rich in biological information. To date, most proteomic studies have focused on the identification and quantification of proteins. However, most proteins associate with other proteins and/or other types of biomolecules and carry out their function as protein modules, and a multitude of such functional modules constitutes the biochemical state of the cell. We refer to a specific instance of proteome composition and organization as the proteotype.
In this presentation we will discuss computational and mass spectrometry based experimental methods to infer or measure the modular organization of the proteotype. We will then examine to what extent the proteotype correlates with quantitative phenotypes and how the cell reorganizes the proteotype in response to genetic or external signals. Selected examples will be used to illustrate the general concepts.