An Application of Conditional Robust Calibration (CRC) to The Lotka-Volterra Predator-Prey model in computational systems biology: a comparison of two sampling strategies.

Mathematical modeling is a widely used technique for describing the temporal behavior of biological systems. One of the most challenging topics in computational systems biology is the calibration of nonlinear models, i.e. the estimation of their unknown parameters. The state of the art methods in this field are the frequentist and Bayesian approaches. For both of them, the performances and accuracy of results highly depend on the sampling technique employed. Here, we test a novel Bayesian procedure for parameter estimation, called Conditional Robust Calibration (CRC), comparing two different sampling techniques: uniform and logarithmic Latin Hypercube Sampling (LHS). CRC is an iterative algorithm based on parameter space sampling and on the estimation of parameter density functions. We apply CRC with both sampling strategies to the Lotka-Volterra model and we obtain a more precise and reliable solution through logarithmically spaced samples.