To what extent is industrial biotechnological process optimally designed? Do the biological entities (enzymes, cells) involved in the biotechnological process operate efficiently? What could be done to improve the efficacy? Answers to the questions are often not quite simple. Primarily, it is dictated by large-scale and expensive experimental investigations characterizing phenomenon from various positions. However, it is only one side of the problem because results of these investigations should be integrated in the sole description and correctly interpreted to address the questions posed.

The first part of the problem does not usually cause any complications, because lots of experimental information characterizing different biotechnological subjects (strains, enzymes or fermenters) has been collected and published. However, the other part of the problem consisting of integration and interpretation of the data could have serious troubles. For address the issue we propose to apply methods of computational systems biology to analyze  biotechnological experimental data.

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In the competitive pharmaceutical and biotech landscape, the market success of any new product depends on how well a company executes a clinical development program. Success depends on integrating scientific and clinical data to the development and commercialization of therapies.

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