We investigate how intracellular signaling affects large-scale population behavior in E.coli bacteria, using the example of chemotaxis. The chemotaxis signaling pathway of E.coli is a relatively simple system, exhibiting, however, complex features like memory, precise adaptation or ultra-sensitivity. We model the signaling pathway as a system of differential, stochastic differential and algebraic equations. We then use an agent-based approach to simulate large bacterial populations, where each individual incorporates the full description of the signaling pathway. Using this model we could show that stochastic fluctuations on the signaling level can induce Lévy-walk-like motion in the absence of chemoattractants. This type of motion is characterized by a power-law run length distribution and has been shown to be a very efficient search strategy. We analytically derive the power-law run length distribution using the model of the signaling pathway, and use simulations to study the effects of the fluctuations on chemotactic behavior. We show that chemotaxis is positively affected by signaling noise as well, for instance by higher motility of individuals. On the other hand, we also observe negative effects, like impaired adaptation and sensitivity. When we include local production of chemottractand in our model, we observe spontaneous formation of bacterial aggregates. The size of these aggregates was  believed to depend on availability of nutrients or division rates. More recently it was suggested that the cluster size might be determined by the bacterial memory, which is related to the (de)methylation process. Using our model we show how signaling pathway design, enzyme levels and the presence of fluctuations influence the dynamics of the pattern formation process and the form of the resulting spots.