SPADA Docs

number of iterations required to discover a high-performing multiplex reaction. No modeling 546 algorithm is perfect, and there are many variables that are unknown even in the most 547 sophisticated design paradigm. However, using such sophisticated multiplex design should result 548 in many fewer design and experiment iterations and vastly superior assay performance.

549 550

5.0 Metrology for In Silico Analysis

551

Metrology is the science of measurement and serves an important, but often under- 552 appreciated role in the development and validation of in silico PCR assay design methods. 553 Measurement assurance concepts that help increase confidence and decrease uncertainty (the 554 error associated with a result) for experimental data (24) can also be applied to in silico 555 approaches.

556 557

558

5.1 Sources of measurement uncertainty

One of the key steps in PCR assay design is predicting the outcome of applying an assay to 559 one or more DNA templates. While the information needed to define a PCR assay depends on 560 the complexity of the computational model, relevant information can include: 1) primer and 561 probe oligo sequences and concentrations; 2) template sequences and concentrations; 3) salt 562 concentrations; 4) thermocycling times and temperatures; 5) nucleotide concentrations; 5) 563 polymerase concentration and properties (i.e. nucleotide extension rate); and 6) buffer 564 composition. All these parameters can affect the final outcome and therefore contribute to the 565 uncertainty that is the error associated with the model prediction. 566 However, it is often challenging to obtain accurate, quantitative measurements for many of 567 these parameters in practice. Some information, like polymerase properties and the buffer 568

31