This thesis concerns with Kinetically Constrained Models and Bootstrap Percolation, two topics in the intersection between probability, combinatorics and statistical mechanics. Kinetically constrained models were introduced by physicists in the 1980's to model the liquid-glass transition, whose understanding is still one of the big open questions in condensed matter physics. They have been studied extensively in the physics literature in the hope to shed some light on this problem, and in the last decade they have also received an increasing attention in the probability community. We will see that even though they belong to the well established field of interacting particle systems with stochastic dynamics, kinetically constrained models pose challenging and interesting problems requiring the development of new mathematical tools.Bootstrap percolation, on the other hand, is a class of monotone cellular automata, namely discrete in time and deterministic dynamics, the first example being the r-neighbor bootstrap percolation introduced in 1979. Since then, the study of bootstrap percolation has been an active domain in both the combinatorial and probabilistic communities, with several breakthroughs in the recent years.Though introduced in different contexts, kinetically constrained models and the bootstrap percolation, as we will see, are intimately related; and one may think of bootstrap percolation as a deterministic counterpart of kinetically constrained models, and of kinetically constrained models as the natural stochastic version of bootstrap percolation.