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A central concern of biopharmaceutical R&D is the production of sufficient quantities of recombinant products from manufacturing processes based on animal cell culture. The way in which bioprocess researchers have addressed this question experienced a tremendous shift over the years, progressing from almost empirical to more rational approaches. A step further is the application of systems biotechnology: recent technological advances for large-scale cell state characterization and creative methods for host cell modeling are becoming crucial for next-generation bioprocess optimization. Here we provide an overview of the main trends towards this goal, with a focus on metabolic models as central scaffolds for data integration and prediction of bioprocess outcomes.

International audience; This report highlights the potential of measurement, monitoring, modeling and control (M(3) C) methodologies in animal and human cell culture technology. In particular, state-of-the-art of M(3) C technologies and their industrial relevance of existing technology are addressed. It is a summary of an expert panel discussion between biotechnologists and biochemical engineers with both academic and industrial backgrounds. The latest ascents in M(3) C are discussed from a cell culture perspective for industrial process development and production needs. The report concludes with a set of recommendations for targeting M(3) C research toward the industrial interests. These include issues of importance for biotherapeutics production, miniaturization of measurement techniques and modeling methods.

Background Stoichiometric models constitute the basic framework for fluxome quantification in the realm of metabolic engineering. A recurrent bottleneck, however, is the establishment of consistent stoichiometric models for the synthesis of recombinant proteins or viruses. Although optimization algorithms for in silico metabolic redesign have been developed in the context of genome-scale stoichiometric models for small molecule production, still rudimentary knowledge of how different cellular levels are regulated and phenotypically expressed prevents their full applicability for complex product optimization. Results A hybrid framework is presented combining classical metabolic flux analysis with projection to latent structures to further link estimated metabolic fluxes with measured productivities. We first explore the functional metabolic decomposition of a baculovirus-producing insect cell line from experimental data, highlighting the TCA cycle and mitochondrial respiration as pathways strongly associated with viral replication. To reduce uncertainty in metabolic target identification, a Monte Carlo sampling method was used to select meaningful associations with the target, from which 66% of the estimated fluxome had to be screened out due to weak correlations and/or high estimation errors. The proposed hybrid model was then validated using a subset of preliminary experiments to pinpoint the same determinant pathways, while predicting the productivity of independent cultures. Conclusions Overall, the results indicate our hybrid metabolic flux analysis framework is an advantageous tool for metabolic identification and quantification in incomplete or ill-defined metabolic networks. As experimental and computational solutions for constructing comprehensive global cellular models are in development, the contribution of hybrid metabolic flux analysis should constitute a valuable complement to current computational platforms in bridging the metabolic state with improved cell culture performance.

Background Elementary flux modes (EFM) are unique and non-decomposable sets of metabolic reactions able to operate coherently in steady-state. A metabolic network has in general a very high number of EFM reflecting the typical functional redundancy of biological systems. However, most of these EFM are either thermodynamically unfeasible or inactive at pre-set environmental conditions. Results Here we present a new algorithm that discriminates the "active" set of EFM on the basis of dynamic envirome data. The algorithm merges together two well-known methods: projection to latent structures (PLS) and EFM analysis, and is therefore termed projection to latent pathways (PLP). PLP has two concomitant goals: (1) maximisation of correlation between EFM weighting factors and measured envirome data and (2) minimisation of redundancy by eliminating EFM with low correlation with the envirome. Conclusions Overall, our results demonstrate that PLP slightly outperforms PLS in terms of predictive power. But more importantly, PLP is able to discriminate the subset of EFM with highest correlation with the envirome, thus providing in-depth knowledge of how the environment controls core cellular functions. This offers a significant advantage over PLS since its abstract structure cannot be associated with the underlying biological structure.