In this case we might get results as shown below. The amount of adhesion differs between surfaces but each strain shows an identical pattern even though the amount of adhesion differs between strains, for example strain 1 always has the greatest adhesion. If there is an interaction these patterns will break down. For example, suppose surfaces 2 and 3 have very different effects on strain 4 compared to the other strains. Now the pattern for strain 4 is very different from the others.
Interactions can cause problems.
Many statisticians say that the interaction term should be tested first, i.e. find out if the associated F value is significant. If the interaction is found to be significant the analysis should stop. This is because the anova model assumes that the factor effects are additive, interaction implies that this assumption has been violated. Biologists don't usually impose this restraint, indeed interactions often provide important biological information. As usual the results are presented in an anova summary table.