Many problems in omics are combinatorial in nature, i.e., the interrelationship amongst the entities is at par, if not more important, than the value of the entity themselves. Graphs are the most commonly used mathematical object to model such relationships. However, often it is important to capture higher order relationships as well. Topological data analysis provides a natural basis to model such interactions and the use of Logic enables extraction of signal patterns as logical expressions (or hypothesis) from noisy data. I will discuss a few applications of such models.