We have developed new algorithms to model complex biological flows in integrated biodetection microdevice components. The proposed work is important because the design strategy for the next-generation Autonomous Pathogen Detection System at LLNL is the microfluidic-based Biobriefcase, being developed under the Chemical and Biological Countermeasures Program in the Homeland Security Organization. This miniaturization strategy introduces a new flow regime to systems where biological flow is already complex and not well understood. Also, design and fabrication of MEMS devices is time-consuming and costly due to the current trial-and-error approach. Furthermore, existing devices, in general, are not optimized. There are several MEMS CAD capabilities currently available, but their computational fluid dynamics modeling capabilities are rudimentary at best. Therefore, we proposed a collaboration to develop computational tools at LLNL which will (1) provide critical understanding of the fundamental flow physics involved in bioMEMS devices, (2) shorten the design and fabrication process, and thus reduce costs, (3) optimize current prototypes and (4) provide a prediction capability for the design of new, more advanced microfluidic systems. Computational expertise was provided by Comp-CASC and UC Davis-DAS. The simulation work was supported by key experiments for guidance and validation at UC Berkeley-BioE.