Introduction

Cell growth and the accumulation of metabolic products are strongly influenced by medium compositions such as carbon sources, nitrogen sources, growth factors, and inorganic salts. It is difficult to search for the major factors and to optimize them for biotechnological processes including multivariables. The traditional ‘one-factor at a time’ technique used for optimizing a multivariable system not only is time-consuming, but also may result in wrong conclusions (Oh et al., 1995). Response surface methodology (RSM) is a collection of statistical techniques for designing experiments, building models, evaluating the effects of factors, and searching optimum conditions of factors for desirable responses. With the development of computing software and the wide application of personal computers, RSM has been successfully applied in many areas of biotechnology such as optimization of a cultural medium (Ooijkaas et al., 1999), enzyme synthesis (Ismail et al., 1998), gibberellic acid production (Escamilla et al., 2000), lactic acid esterification (Kiran et al., 1999), and aqueous two-phase extraction of bacteriocin (Li et al., 2001), but it has not yet been reported as a means to study and optimize conditions of a medium for bacteriocin production.