The overall goal of this work was to develop and utilize microfluidic models for investigating bacterial chemotaxis and biofilm formation - phenotypes that play key roles in bacterial infections. Classical methods for investigating chemotaxis and biofilm formation have many limitations and drawbacks. These include being unsuitable for investigating the effect of chemorepellents, non-quantitative readouts, and not accounting for interaction between hydrodynamics and biofilm formation. The novel microfluidic model systems for chemotaxis and biofilm formation developed in this study addresses these drawbacks. Chemotaxis model system development was done in three stages. We first developed two static chemotaxis model systems - the two fluorophore chemotaxis agarose plug assay and the mu Plug assay - for rapidly determining the extent of chemotaxis in a qualitative manner. A key feature of these model systems was the incorporation of dead cells and differential labeling with green and red fluorescent proteins for partitioning the effects of movement due to fluid flow from chemotaxis. The static systems were used to rapidly screen a wide range of conditions for use in the flow-based mu Flow chemotaxis model system. The effect of four major variables - cell preparation method, gradient strength, flow rate in the device, and imaging position - that influence the chemotactic response in the mu Flow was characterized using the repellent taxis from Ni^2 gradients as the model chemoeffector. Using the mu Flow chemotaxis device, we investigated the chemotaxis of Escherichia coli RP437 to different signals that are present in the human gastrointestinal tract and are likely to be mediators of infection through their effect on chemotaxis. Our data show that the bacterial signal indole is a repellent, while the signals autoinducer-2 (AI-2) and isatin are attractants for E. coli RP437. However, cells exposed to a competing gradient of indole and either AI-2 or isatin, attracts E. coli. The ?Flow device was also used to refute a long-standing view on how the repellent Ni2 is sensed in E. coli. Our data show that only the Tar chemoreceptor is needed for sensing Ni^2 and the nickel binding protein, NikA, and the Ni^2 transport system proteins, NikB and NikC, are not required for repellent taxis from nickel. A microfluidic biofilm model was also developed in this study and used in conjunction with a mathematical model to investigate biofilm formation and quorum sensing in closed systems (where biofilm growth and hydrodynamics are interdependent). The mathematical model predictions were experimentally validated using Pseudomonas aeruginosa PA14 in a microfluidic biofilm system at various flow rates.