  A key challenge in systems biology is the elucidation of the underlying
principles, or fundamental laws, which determine the cellular phenotype.
Understanding how these fundamental principles are altered in diseases like
cancer is important for translating basic scientific knowledge into clinical
advances. While significant progress is being made, with the identification of
novel drug targets and treatments by means of systems biological methods, our
fundamental systems level understanding of why certain treatments succeed and
others fail is still lacking. We here advocate a novel methodological framework
for systems analysis and interpretation of molecular omic data, which is based
on statistical mechanical principles. Specifically, we propose the notion of
cellular signalling entropy (or uncertainty), as a novel means of analysing and
interpreting omic data, and more fundamentally, as a means of elucidating
systems-level principles underlying basic biology and disease. We describe the
power of signalling entropy to discriminate cells according to differentiation
potential and cancer status. We further argue the case for an empirical
cellular entropy-robustness correlation theorem and demonstrate its existence
in cancer cell line drug sensitivity data. Specifically, we find that high
signalling entropy correlates with drug resistance and further describe how
entropy could be used to identify the achilles heels of cancer cells. In
summary, signalling entropy is a deep and powerful concept, based on rigorous
statistical mechanical principles, which, with improved data quality and
coverage, will allow a much deeper understanding of the systems biological
principles underlying normal and disease physiology.
