The physicochemical events that underlie biological processes are inevitably either/or events. Either a growth factor molecule binds to a cell, or it doesn't. Either a site on a cyclin molecule is phosphorylated, or it isn't. Either a regulatory molecule binds to a DNA sequence, or it doesn't. These molecular either/or events lead to cellular either/or events. Either a cell divides, or it doesn't. Either a cell dies, or it doesn't. Either a cell turns on a particular gene, or it doesn't. Either a tumor cell stays where it is, or it forms a distant metastasis. By considering biological processes as the macroscopic aggregate results of these many individual microscopic either/or events, we can gain considerable insight into both normal and cancerous growth. In fact, as will be outlined here, such discrete modeling may allow us to see how the normal cellular populations of the body can grow to predictable sizes, at predictable times, and to predictable shapes. Such modeling can also allow us to gain insight into how normal cellular populations may become cancerous cellular populations. Indeed, such an approach allows us do a sufficiently good job of imitating the growth and spread of tumors as to be able to make estimates the most effective ways to both detect and treat cancer.