title: Application of multi-objective optimization to pooled experiments of next generation sequencing for detection of rare mutations.
abstract: In this paper we propose some mathematical models to plan a Next Generation Sequencing experiment to detect rare mutations in pools of patients. A mathematical optimization problem is formulated for optimal pooling, with respect to minimization of the experiment cost. Then, two different strategies to replicate patients in pools are proposed, which have the advantage to decrease the overall costs. Finally, a multi-objective optimization formulation is proposed, where the trade-off between the probability to detect a mutation and overall costs is taken into account. The proposed solutions are devised in pursuance of the following advantages: (i) the solution guarantees mutations are detectable in the experimental setting, and (ii) the cost of the NGS experiment and its biological validation using Sanger sequencing is minimized. Simulations show replicating pools can decrease overall experimental cost, thus making pooling an interesting option.
authors.affiliation: Institute of Mathematics and Informatics, Vilnius University, Vilnius, Lithuania.
authors.forename: Julius
authors.initials: J
authors.lastname: Zilinskas
authors.affiliation: Institute of Mathematics and Informatics, Vilnius University, Vilnius, Lithuania.
authors.forename: Algirdas
authors.initials: A
authors.lastname: Lančinskas
authors.affiliation: High Performance Computing and Networking Institute, National Research Council, Naples, Italy.
authors.forename: Mario Rosario
authors.initials: MR
authors.lastname: Guarracino
mesh_terms: D000465:Algorithms; D003365:Costs and Cost Analysis; D004252:DNA Mutational Analysis / Q000191:economics / Q000379:methods*; D059014:High-Throughput Nucleotide Sequencing / Q000191:economics / Q000379:methods*; D006801:Humans; D009154:Mutation / Q000235:genetics*; D015203:Reproducibility of Results