Advances in micro-nano-biosensor fabrication are enabling the integration of a large number of biological recognition elements within a single package. As a result, hundreds to millions of tests can be performed simultaneously and can facilitate rapid detection of multiple pathogens in a given sample. However, it is an open question as to how to exploit the highdimensional nature of the multi-pathogen testing for improving the detection reliability of typical biosensor systems. Our research over the past few years has addressed this question and in this paper we briefly summarize our approach. Our underlying principle is based on a forward error correcting (FEC) biosensor where redundant patterns are synthetically encoded on the biosensor. A decoding algorithm then exploits this redundancy to compensate for systematic errors due to experimental variations and for random errors due to stochastic biomolecular interactions. The key milestones in this research are : (a) fabrication and modeling of biomolecular circuit elements used for constructing the FEC biosensor; (b) development of a simulation environment for rapid evaluation of encoding/decoding algorithms and (c) development of a "co-detection" protocol that exploits non-linear interaction between different biomolecular circuit elements. As a proof-of-concept our study and experimental results have been based on a conductimetric lateral flow immunosensor that uses antigen-antibody interaction in conjunction with a polyaniline transducer to detect the presence or absence of pathogens in a given sample.