Modelling of multi-component immunoassay kinetics - A new node-based method for simulation of complex assays.

The behaviour of binding reactions in immunoassays can be predicted and studied by modelling methods. Simple antibody-analyte binding reaction kinetics can be simulated by e.g. a mechanistic assay model based on differential equations. However, the mathematical modelling becomes more complicated if multivalent-structured components are involved and the number of binding complexes increases. In this paper, a new node-based method to model complex binding reactions is introduced. The principle of this method is to construct a network of the initial components, reaction intermediates and end-products by forming a network of nodes. This network is then solved, node by node, breaking the initial problem into smaller partial problems, still obeying the laws of chemical reaction kinetics and without ignoring any parts of the problem. This method provides an easy and quick way to study complex binding reactions since simulation networks are simple to construct directly from the reaction scheme. This presented new "NODE"-method is compared with the well known mechanistic assay model.

Theoretical assessments of errors in rapid immunoassays-how critical is the exact timing and reagent concentrations?

The reliability of rapid immunoassay is a concern due to an incomplete incubation to a non-equilibrium state and is susceptible to different error factors causing variance. The most critical point in the process should be found in order to improve the accuracy, and reproducibility of immunoassays, and enhance the system robustness. In this paper, the behavior of rapid assays is predicted by simulations using mechanistic assay model, based on antibody-analyte binding reaction kinetics. This antibody-analyte binding reaction kinetics model was constructed for a generic three-component (immunometric) assay and the parameters were chosen to be those of a known surface binding assay. The effects of the exact incubation timing and the initial reagent concentrations were studied focusing on the early phase of incubation, the non-equilibrium state. The magnitudes of errors in the input parameters were estimated using knowledge from practical immunoassays. According to simulations, inaccurate incubation timing adds error in the results at very short incubation times, especially in low analyte concentrations. The inaccurate reagent concentrations increase variance at short incubation times, as well. The error decreases rapidly after the first few minutes of incubation.