(664d) Modeling and Optimization of Biosensors Exploiting Restriction Fragment Length Polymorphism

Restriction fragment-length polymorphism is a mechanistic for several types of widely used methods for detecting the presence complex prokaryotes. Microfluidic technology holds promise for further extending these methods to develop a new class of bacterial sensors for low cost environment and medical diagnostics. Large databases of fragment lengths are now available from experimental data. However, bacterial organism identification is limited by the number of distinct fragment lengths and the experimental resolution of fragment lengths.

This paper develops and optimizes methodology for sensors based on restriction fragment length polymorphism (RFLP). The methodology includes two optimization problems. One is how to estimate the aposteriori probability distribution from limited measurements of distinct fragment lengths polymorphism following digestion with restriction enzymes. The synthesis problem is then to obtain maximum detectability by optimizing experimental protocols and the selection of restriction enzymes for a given bacterial library.

In this paper,we propose maximum entropy and relative information (information gain) as an appropriate metrics for the analysis RFLP techniques. Several different information entropy and relative information (information gain) are also compared. Our extensive computational study indicates that maximum entropy is an appropriate method for the computation of posteriori probability information. Information gain then establishes an upper bound for the sensor performance, where maximum information gain is a solvable convex, geometric programming. Two typical medical applications are used to illustrate the practical application of our analysis to biosensor design.