摘要： Models of biochemical reaction networks commonly contain a large number of parameters, while at the same time, there is only a limited amount of (noisy) data available for their estimation. As such, the values of many parameters are not well known as nominal parameter values have to be determined from the open scienti?c literature and a signi?cant number of the values may have been derived in different cell types or organisms than that which is modeled. There clearly is a need to estimate at least some of the parameter values from experimental data; however, the small amount of available data and the large number of parameters commonly found in these types of models require the use of regularization techniques to avoid over?tting. A tutorial of regularization techniques, including parameter set selection, precedes a case study of estimating parameters in a signal transduction network. Cross-validation results rather than ?tting results are presented to further emphasize the need for models that generalize well to new data instead of simply ?tting the current data.