Step 2: Use Steady State Data for Network Inference

Abstract

We say an organism is in steady state if the values of what we are measuring (e.g. gene expression) won’t change unless we change its conditions in some way. For example, the organism may be in one steady state in a low nutrient condition, another in a high nutrient condition, and still another after some mutation has occurred and we have waited until transient effects have died out. Steady-state data can arise from experiments in which one or more genes have been knocked out, overexpressed, or otherwise perturbed. If you know what happens to the network when a gene is missing or when it has been perturbed, it is easier to infer which genes it influences.