Biotechnology has many applications in health care, agriculture, industry and the environment. By using renewable raw materials, biotechnology contributes to lowering greenhouse gas emissions and moving away from a petro-based towards a circular sustainable economy. However, major developments are still needed to make industrial biotechnology an economic alternative to conventional processes for fuels, specialty and/or bulk chemicals production.Process integration is a holistic approach to process design, which emphasizes the unity of the process and considers the interactions between different unit operations from the outset, rather than optimizing them separately. Furthermore, it also involves the substitution of two or more unit operations by one single novel unit capable of achieving the same process goal.Conversely, process systems engineering (PSE) deals with the analysis, design, optimization, operation and control of complex process systems, as well as the development of model-based methods and tools that allow the systematic development of processes and products across a wide range of systems involving physical and chemical change. Mature tools and applications are available for chemical technology and steps have been taken to apply PSE principles also to bioprocess technology. This perspective paper argues that an interdisciplinary approach is needed towards integrated bio-processing in order to link basic developments in biosciences with possible industrial applications. PSE can foster the application of existing and the development of new methods and tools for bioprocess integration that could promote the sustainable production of bio-/chemical products. The inclusion of PSE principles and methods in biochemical engineering curricula and research is essential to achieve such goals.
The (13)C-labeling technique was introduced in the field of metabolic engineering as a tool for determining fluxes that could not be found using the 'classical' method of flux balancing. An a priori flux identifiability analysis is required in order to determine whether a (13)C-labeling experiment allows the identification of all the fluxes. In this article, we propose a method for identifiability analysis that is based on the recently introduced 'cumomer' concept. The method improves upon previous identifiability methods in that it provides a way of systematically reducing the metabolic network on the basis of structural elements that constitute a network and to use the implicit function theorem to analytically determine whether the fluxes in the reduced network are theoretically identifiable for various types of real measurement data. Application of the method to a realistic flux identification problem shows both the potential of the method in yielding new, interesting conclusions regarding the identifiability and its practical limitations that are caused by the fact that symbolic calculations grow fast with the dimension of the studied system.
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