The modeling of biological systems often involves the estimation of model parameters. Estimation methods have been developed to model these systems in a boundederror context due to the uncertainty involved in biological processes. This application of bounded methods to nonlinear and higher-dimensional systems is computationally expensive resulting in excessive simulation times. One possible solution to this problem is parallelizing the computations of bounded-error estimation approaches using multiple processor cores. In this paper, we developed a method for use on a single multi-core workstation using POSIX threads to process subsets of the parameter space while access to shared information was controlled by mutex-locked linked lists. This approach allows the parallelized algorithm to run on easily accessible multicore workstations and does not require utilization of large supercomputers or distributed computing. Initial results of this method using 8 threads on an 8-core machine show speedups of 7.59 and 7.86 when applied to bounded parameter estimation problems involving the nonlinear Lotka-Volterra predator-prey model and SEIR infectious disease model, respectively.
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