Biopharmaceutical products emerged as the principal driver for innovation in the pharmaceutical industry. Although of high economic and social importance, the development of biopharmaceutical manufacturing processes is still driven by time-intensive empirical approaches, delaying the time-to-market of novel drugs and jeopardizing the economic competitiveness of manufacturing processes. A promising approach to speed up process development and to enable robust manufacturing is to build-up process knowledge to form well-characterized biopharmaceutical manufacturing platforms. This enables the efficient development of production processes for a broad spectrum of products while simultaneously drug product quality and economic manufacturing risks are reduced. Within this thesis, the development of a novel and highly versatile E. coli recombinant protein production platform is presented. The built-up of platform process knowledge is achieved using novel combinatory methods which combine cutting edge technologies such as first-principle soft-sensors, dynamic experimentation, mid infrared and dielectric spectroscopy as well multivariate data analysis and kinetic modeling. Major novelties include the presentation of highly automated methods for the extraction of strain specific parameters, information which is essential for science-based bioprocess design. Expression tuning on cellular level is demonstrated using solely process technological means, resulting in a high degree of processing flexibility for the intended pharmaceutical manufacturing platform. Furthermore, for the first time, generic control methods based on soft-sensors are presented, which allow controlling multiple physiological bioprocess parameters simultaneously and therefore result in a more robust manufacturing. This thesis can be considered a case study demonstrating how combinatory methods can be purposefully exploited for the fast development of an efficient bioprocessing platform. The methodological focus of this thesis allows leveraging the developed combinatory methods and the platform bioprocess to other biotechnological manufacturing tasks and will enable the development of more competitive and predictable bioprocesses.