With our QCM tools we are able to analyse massive amounts of data related to the DNA and its structure. Our goal is to synthesise the first Complexity Map of the human DNA. Mapping genome complexity is crucial towards establishing the relationship between function and its structure. While we know that the structure of the DNA is highly complex, this complexity has never actually been quantified. This is our goal - to measure the complexity of the DNA and to rank its constituents in term of their relative contribution to the overall DNA complexity. Read more.
Protein folding simulations are very CPU-intensive and are crucial towards new protein discovery. We propose to incorporate complexity in the conventional methods of folding analysis and simulation. Based on the dynamics of the folding process, which takes milliseconds, or even less, we are able to measure the evolution of the complexity of the sequence of amino-acids over time. Our initial experiments indicate that folded proteins tend to maximise the value of the complexity function - see plot. The reason for this may reside in the fact that complexity measures the amount of structured information within a given system (expressed in complexity bits or cbits). We postulate that each sequence of amino-acids folds in a way which maximises the amount of information that it encodes. This is because functionality is proportional to information. View complexity map of a protein.
A major concern shared by all drug manufacturers is that of drug toxicity. There exist essentially two approaches to drug toxicity determination: knowledge-based and the QSAR (Quantitative Structure Activity Relationship) rule-based models, which relate variations in biological activity and molecular descriptors. Evidently, any expert of rule-based system will see its efficacy bounded by the quality and relevance of the employed rules. Because of the inability to predict successfully drug toxicity, drug manufacturers report billion-dollar losses every year.
We postulate that the toxic effects of a molecule are proportional to its complexity. In other words, we suggest that a more complex molecule has greater potential to do damage and over a broader spectrum and that higher complexity may also imply greater capacity to combine with other molecules. The underlying idea is to use complexity as a ranking and risk-stratification mechanism for molecules.