During the development of CAT-Seq we have developed three different in-depth mathematical models that have immensely helped us create a well-working system.
Mathematical description of droplet coalescence
Efficient reaction mix enrichment by droplet merging was an extremely important aspect of CAT-Seq as it is directly responsible for the accuracy and information retainment degree. Yet, the droplet merging was immensely inefficient, as droplet coalescence techniques contain multiple parameters that requires precise tuning. For that reason we have developed an in-depth mathematical description of the droplet merging. Explore the model by clicking here.
In-silico determination of Esterase mutants
In order to assess the CAT-Seq accuracy and precision, using in-silico methods we have derived CAT-Seq esterase mutants which had different activities, which would be later used for Catalytic Activity Sequencing performance benchmarking. Explore the model by clicking here.
Modified Nucleotide inhibitory effect model
While researching the literature we have found out that modified nucleotides (which we are using as substrate nucleotides) might sometimes affect the amplification efficiency of Phi29 polymerase. We were concerned by that fact because the amplification efficiency was important for us, as nanopore sequence requires long amplicons. For this reason we have derived a mathematical model to assess the modified nucleotide inhibitory effects on the DNA amplicon length. clicking here.