Sensitivity of biological models to errors in parameter estimates

Since A. M. Turing’s paper proposing a mathematical basis for pattern formation in developing organisms many mathematical approaches have been proposed to model biological phenomenon. Continued laboratory study and recent improvements in measurement capabilities have provided an immense quantity of raw gene expression data. The level of data now available demands the development of well-characterized and tested computational tools. Thus, we have examined one mathematical model’s sensitivity to errors in estimating its ’ parameters. Errors in parameter estimation can arise from noise in the laboratory measurements and recasting of laboratory data. We elected to examine the rulebased mathematical model of Mjolsness et al for its ’ sensitivity to errors in estimated parameters. We have used the technique of sensitivity equations as generally applied in nonlinear systems analysis