A viable technique for sensitivity analysis in highdimensional systems is described in the context of bio-inspired systems. The sensitivity analysis provides critical information about the system by indicating the dominant parameters that shape the output. This knowledge becomes particularly essential to have a better understanding of complex biological network of interactions. A notable feature of many high-dimensional systems is that a large portion of all parameters have little impact on the system outcome, thus yielding sparsity. The proposed algorithm leverages the sparse properties of systems analysed and is based on a heuristic two-stage elimination strategy. The implementation of the proposed algorithm yields substantial reduction of the total simulation cost by as much as 95% for a system composed of 562500 parameters over the conventional local sensitivity analysis while retaining its accuracy above 70%.