Coupling of multiphase fluid dynamics and biological growth is a common area of application in process engineering. At the forefront, environmental conditions and rising energy cost force companies to optimize reactor systems and processes. In optimization problems virtual methods play a major role. Models form the basis of virtual methods. Differences in time scales for computational fluid dynamics and biological growth make direct co-simulation infeasible. A sequential method has to be applied. This work proposes the sequential co-simulation in application to baker’s yeast production in a 200 L bubble column reactor. The method combines a module for fluid dynamics implemented in CFX by Ansys and a module for biological growth implemented in Matlab and Simulink by Mathworks. Data interfaces are defined for module data is mapped to a reduced set of elements by the neural gas algorithm. Computational demand for sequences of growth is significantly reduced. Validation of this method follows two steps. First, computational fluid dynamics are validated with experimental data for axial liquid velocity. Second, growth model and the sequential method are validated with cultivation data. Experimental data has been determined in the same bubble column reactor. Target prediction for the sequential co-simulation deviates less than three percent from measured values.