An Empirical Approach for Quantifying Loop-Mediated Isothermal Amplification (LAMP) Using Escherichia coli as a Model System

Loop mediated isothermal amplification (LAMP) is a highly efficient, selective and rapid DNA amplification technique for genetic screening of pathogens. However, despite its popularity, there is yet no mathematical model to quantify the outcome and no well-defined metric for comparing results that are available. LAMP is intrinsically complex and involves multiple pathways for gene replication, making fundamental modelling nearly intractable. To circumvent this difficulty, an alternate, empirical model is introduced that will allow one to extract a set of parameters from the concentration versus time curves. A simple recipe to deduce the time to positive, Tp - a parameter analogous to the threshold cycling time in polymerase chain reaction (PCR), is also provided. These parameters can be regarded as objective and unambiguous indicators of LAMP amplification. The model is exemplified on Escherichia coli strains by using the two gene fragments responsible for vero-toxin (VT) production and tested against VT-producing (O157 and O45) and non-VT producing (DH5 alpha) strains. Selective amplification of appropriate target sequences was made using well established LAMP primers and protocols, and the concentrations of the amplicons were measured using a Qubit 2.0 fluorometer at specific intervals of time. The data is fitted to a generalized logistic function. Apart from providing precise screening indicators, representing the data with a small set of numbers offers significant advantages. It facilitates comparisons of LAMP reactions independently of the sampling technique. It also eliminates subjectivity in interpretation, simplifies data analysis, and allows easy data archival, retrieval and statistical analysis for large sample populations. To our knowledge this work represents a first attempt to quantitatively model LAMP and offer a standard method that could pave the way towards high throughput automated screening.