Chemical reactions link cellular components together to form a network. Although we can specify the chemical properties of links in biological networks, it is the way in which a multitude of such links form networks that determines phenotypic functions. These integrated network functions are also called functional states, and they correspond to the observed biological functions or phenotypic states that networks create. A functional state may be viewed as the outcome of the execution of the genetic program written in the DNA. In this chapter we detail the concept of a functional state of a genome-scale network and how it represents a physiologically observable state. The following chapters then describe the framework for computing functional states using the constraint-based approach.

Components vs. Systems

Components come and go Biological components all have a finite turnover time. Most metabolites turn over within a minute in a cell, mRNA molecules typically have two-hour half-lives in human cells [463], 3% of the extracellular matrix in cardiac muscle is turned over daily, and so forth. So a cell that you observe today, compared with the same cell yesterday, may only contain a small fraction of the same molecules.

Similarly, cells have finite lifetimes. The cellularity of the human bone marrow turns over every two to three days. The renewal rate of skin is on the order of five days to a couple of weeks. The lining of the gut epithelium has a turnover time of about five to seven days. Slower tissues, like the liver, turnover their cellularity approximately once a year. So a mammal that you observe today may only contain a small fraction of the same cells as the same mammal observed a year ago. Thus, the components of a biological system come and go, and their turnover takes place on multiple time scales.

However, the system remains Most of the cells that are contained in an individual today were not there just a few years ago.