Aim To develop an online biofilm calculation tool (Biofilm Classifier), which calculates the optical density cut off value and accordingly determines the biofilm forming categories for the tested isolates by standardized formulas, as well as to compare the results obtained by Biofilm Classifier to manual calculations and the use of predefined values. Methods The biofilm forming capacity of tested strains was evaluated using tissue culture plate method in 96 well plates, and optical density (OD) value of the formed biofilm was measured on an ELISA Microplate reader at 595 nm on a total of 551 bacterial isolates from clinical specimen. Results Comparative analysis indicated that the manual calculation was 100% in accordance with results obtained by the designed software as opposed to the results obtained by use of predefined values for biofilm categorization. When using predefined values compared to manual biofilm categorization for the determination of biofilm positive and biofilm negative strains the specificity was 100%, sensitivity 97.81%, positive predictive value 100%, negative predictive value 96.04% and accuracy 98.57%. Conclusion Considering obtained results, the use of the designed online calculator would simplify the interpretation of biofilm forming capacity of bacteria using tissue culture plate method.

Aim To develop an online biofilm calculation tool (Biofilm Classifier), which calculates the optical density cut off value and accordingly determines the biofilm forming categories for the tested isolates by standardized formulas, as well as to compare the results obtained by Biofilm Classifier to manual calculations and the use of predefined values. Methods The biofilm forming capacity of tested strains was evaluated using tissue culture plate method in 96 well plates, and optical density (OD) value of the formed biofilm was measured on an ELISA Microplate reader at 595 nm on a total of 551 bacterial isolates from clinical specimen. Results Comparative analysis indicated that the manual calculation was 100% in accordance with results obtained by the designed software as opposed to the results obtained by use of predefined values for biofilm categorization. When using predefined values compared to manual biofilm categorization for the determination of biofilm positive and biofilm negative strains the specificity was 100%, sensitivity 97.81%, positive predictive value 100%, negative predictive value 96.04% and accuracy 98.57%. Conclusion Considering obtained results, the use of the designed online calculator would simplify the interpretation of biofilm forming capacity of bacteria using tissue culture plate method.

Today’s modelling approaches in Systems Medicine are increasingly multiscale, containing two or more submodels, where each operates on different temporal and/or spatial scales. In addition, as these models become increasingly sophisticated, they tend to be run as multiscale computing applications using computational infrastructures such as clusters, supercomputers, grids or clouds. Constructing, validating and deploying such applications is far from trivial, and communities in different scientific disciplines have chosen very diverse approaches to address these challenges. Within this paper we reflect on the use of Multiscale Computing within the context of Systems Medicine. Multiscale Computing is widely applied within this area, and instead of summarizing the field as a whole we will highlight a set of challenges that we believe are of key relevance to the Systems Medicine community.