# State assignment problem in systems biology and medicine: on the importance of state interaction network topology

## TL;DR

This paper introduces a first principles framework for state assignment in biology and medicine, accounting for complex interactions among diseases and signs to improve diagnostic accuracy and early detection.

## Contribution

It develops a generic, principled model incorporating disease and sign interactions, demonstrated through theoretical case studies for medical diagnostics.

## Key findings

- Model effectively captures disease-sign dependencies
- Enables inference of disease probabilities from signs
- Facilitates early diagnosis and diagnostic flow chart construction

## Abstract

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.