  A fundamental problem in medicine and biology is to assign states, e.g.
healthy or diseased, to cells, organs or individuals. State assignment or
making a diagnosis is often a nontrivial and challenging process and, with the
advent of omics technologies, the diagnostic challenge is becoming more and
more serious. The challenge lies not only in the increasing number of measured
properties and dynamics of the system (e.g. cell or human body) but also in the
co-evolution of multiple states and overlapping properties, and degeneracy of
states. We develop, from first principles, a generic rational framework for
state assignment in cell biology and medicine, and demonstrate its
applicability with a few simple theoretical case studies from medical
diagnostics. We show how disease-related statistical information can be used to
build a comprehensive model that includes the relevant dependencies between
clinical and laboratory findings (signs) and diseases. In particular, we
include disease-disease and sign-sign interactions. We then study how one can
infer the probability of a disease in a patient with given signs. We perform
comparative analysis with simple benchmark models to check the performances of
our models. This first principles approach, as we show, enables the
construction of consensus diagnostic flow charts and facilitates the early
diagnosis of disease. Additionally, we envision that our approach will find
applications in systems biology, and in particular, in characterizing the
phenome via the metabolome, the proteome, the transcriptome, and the genome.
