Because of the inherent complexity of bioprocesses, mathematical models are more and 1 more used for process design, control and optimization etc... These models are generally 2 based on a set of biochemical reactions. Model equations are then derived from mass balance, 3 coupled to empirical kinetics. Biological models are nonlinear and represent processes, 4 which by essence are dynamic and adaptive. The temptation to embed most of the biology 5 is high, with the risk that calibration would not be significant anymore. The most important 6 task for a modeler is thus to ensure a balance between model complexity and ease of use. 7 Since a model should be tailored to the objectives which will depend on applications and 8 environment, a universal model representing any possible situation is probably not the best 9 option. 10 Here are twelve tips to develop your own bioprocess model. For more details on bioprocess 11 modelling, the readers could refer to [1]. More tips concerning computational aspects can 12 be found in [2, 3].