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Forward Error Correcting Biosensors Modeling Algorithms and Fabrication Yang Liu Evangelyn Alocilja Shantanu Chakrabartty liuyang4 egr msu edu alocilja egr msu edu shantanu msu edu Department of Electrical and Computer Engineering Department of Biosystems Engineering Michigan State University East Lansing MI 48824 USA Abstract 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 Index Terms Forward error correcting biosensors factor graph sum product algorithm co detection I I NTRODUCTION Biosensors have emerged as important analytical tools for the rapid detection of food borne pathogens which according to The United States Department of Agriculture USDA cause approximately 5 000 deaths every year 1 A typical architecture of a biosensor consists of a biological recognition layer as a reactive surface in proximity to a transducer which converts the binding between the analyte and the recognition layer into a measurable electrical or optical signal 2 With advances in micro nano fabrication biosensor technology is now available that can integrate a large number of recognition and transducer sites on a portable device As a result a large number of simultaneous detection experiments can be conducted in parallel an example being nanoscale arrays reported in 3 At the same time detection technology has also seen significant improvements where analyte concentration levels ranging from pico molar pM to femto molar f M can now be detected 4 Both these trends are summarized in Fig 1 978 1 4244 2879 3 08 25 00 2008 IEEE Reliability error rate ity iv sit n Se 0 5 Microarray 1 pg mL ng mL Nanoarray g mL 2 mg mL 102 10 3 10 4 Fig 1 Array density probe mm 2 10 5 10 6 Summary of biosensor technology trends which also depicts another important performance parameter that is typically overlooked in biosensor design Unfortunately with miniaturization the reliability of biosensors typically detoriates as have been observed in large scale gene chip arrays Irrespective of the choice of the detection technology DNA aptamers or antibodies the biosensors artifacts can be categorized as 5 a systematic errors that were induced by sample handling errors variations in experimental conditions pH or temperature and errors introduced by device artifacts and b random errors introduced due to stochastic nature of biomolecular interactions From a modeling point of view these artifacts are similar to those that are observed in nanoscale storage and computing systems for which forward error correction FEC techniques have been successfully employed 6 The objective of this research is to replicate the success of FEC principles in designing reliable computing and storage systems towards designing reliable biosensors In this regard the study addresses some of the key challenges in this longterm goal The first step involves mathematical abstraction where simulation models are developed that capture the ex 249 Authorized licensed use limited to Michigan State University Downloaded on August 4 2009 at 13 31 from IEEE Xplore Restrictions apply perimentally measured response of the biosensor circuit These simulation models are then used to a understand the nature of the biosensor channel and in the process derive fundamental limits of biosensor FEC b rapid design and evaluation of different FEC encoding and decoding algorithms without resorting to painstaking experimental procedures Our final objective is to close the design loop where the reliability of the biosensor encoding and decoding algorithms are validated using in lab experiments In this paper we summarize some of the highlights of this research and present some of the key results 6 Sample pad Adsorption pad 5 Conjugate pad 1 4 Antibody capture line 2 3 Electrodes Fig 2 A visualization of multi pathogen biosensor that can implement the encoding methods II F UNDAMENTAL B UILDING B LOCKS FABRICATION AND M ODELING Even though the encoding and decoding methods being developed can be applied to any multi pathogen detection principle based on either antibody DNA or aptamers we have chosen a conductimetric lateral flow immunosensor as a model biosensor The immunosensor utilizes a polyaniline nanowire based transducer to convert the binding between a target antigen and its antibody into an electrical signal Fig 2 shows the visualization of a multi pathogen immunosensor and the principle of a single immunosensor is illustrated in Fig 3 which shows a cross sectional view of the immunosensor Before the sample is applied the gap between the electrodes in the capture pad is open Immediately after sample is applied to the sample pad the solution containing the antigen flows to the conjugate pad dissolves with the polyanilinelabeled antibody and forms an antigen antibody polyaniline complex The complex is transported using capillary action into the capture pad containing the immobilized antibodies A second antibody antigen reaction occurs and forms a sandwich Fig 3 Polyanilines in the sandwich extend out

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