Mathematical Biosciences and Engineering, 2016, 13(6): 1159-1168. doi: 10.3934/mbe.2016036.

Primary: 97M60, 92C50, 92C45; Secondary: 60K40, 92C40.

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Modelling random antibody adsorption and immunoassay activity

1. School of Mathematical Sciences, Dublin Institute of Technology, Kevin Street, Dublin 8
2. Biomedical Diagnostics Institute, Dublin City University, Glasnevin, Dublin 9

One of the primary considerations in immunoassay design is optimizingthe concentrationof capture antibodyin order to achieve maximal antigen binding and, subsequently, improved sensitivityand limit of detection.Many immunoassay technologies involve immobilizationof theantibody to solid surfaces.Antibodies are large molecules in whichthe position and accessibility of the antigen-binding sitedepend on their orientation and packing density.
   In this paper we propose a simple mathematical model, based on the theoryknown as random sequential adsorption (RSA), in order tocalculate how the concentration ofcorrectly oriented antibodies (active site exposed forsubsequent reactions) evolves during the deposition process.It has been suggested by experimental studies that high concentrationswill decrease assay performance, due to molecule denaturation andobstruction of active binding sites. However, crowding of antibodies can alsohave the opposite effect by favouring upright orientations.A specific aim of our model is topredict which of thesecompeting effects prevails under different experimental conditionsand study the existence of an optimalcoverage, which yields the maximum expectedconcentration of activeparticles (and hence the highest signal).
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Keywords Random sequential adsorption; antibody activity.; immunoassays; immobilized particles

Citation: D. Mackey, E. Kelly, R. Nooney. Modelling random antibody adsorption and immunoassay activity. Mathematical Biosciences and Engineering, 2016, 13(6): 1159-1168. doi: 10.3934/mbe.2016036

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This article has been cited by

  • 1. Dana Mackey, Eilis Kelly, Robert Nooney, , Progress in Industrial Mathematics at ECMI 2016, 2017, Chapter 103, 687, 10.1007/978-3-319-63082-3_103
  • 2. Lewis Roberts, Thom Griffith, Alan Champneys, Martina Piano, Janice Kiely, Richard Luxton, Mathematical modelling of a magnetic immunoassay, IMA Journal of Applied Mathematics, 2017, 82, 6, 1253, 10.1093/imamat/hxx034
  • 3. Dana Mackey, Eilís Kelly, Robert Nooney, Richard O'Kennedy, Direct immunoassays and their performance – theoretical modelling of the effects of antibody orientation and associated kinetics, Integrative Biology, 2018, 10.1039/C8IB00077H
  • 4. George Tsekenis, Marianneza Chatzipetrou, Maria Massaouti, Ioanna Zergioti, Comparative Assessment of Affinity-Based Techniques for Oriented Antibody Immobilization towards Immunosensor Performance Optimization, Journal of Sensors, 2019, 2019, 1, 10.1155/2019/6754398

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Copyright Info: 2016, D. Mackey, et al., licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

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