Predictive Sciences

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It is necessary to have a holistic approach to understand and re-use or engineer complex natural phenomena. Integration of different sciences with use of modeling and simulation based methods is taking increased responsibility in understanding such phenomena. Rapid increasment of computational powers gave rise an emerging interdisciplinary field of predictive sciences. Application of domain specific knowledge and predictive powers of simulations made possibility to predict, validate and engineer the response of complex systems. Such ability helps to use more “personalized” access to many different problems.

Simulation and design of new and complex systems in a variety of applications, that not only include biological systems, healthcare, material technology, climate forecasting, new energy technologies, is needed to be done effectively. Such complex systems require integration of diverse disciplines and sophisticated methods of multi-scale, mutli-model and multi-compartmental simulations. There is a need for discovering, developing and teaching common principles, techniques and methods creating common basis of understanding. The future success requires common computational tools, algorithms and bridging frameworks for integrating models, computations and knowledge/idea sharing together.

Interdisciplinary leading edge education in predictive sciences are a critical part of the development of this field. Such education do not fit well into standard educational practices in the physical sciences, the life sciences or mathematics. Teachers, scientist and students from different scientific areas are needed to meet, communicate and share their knowledge and ideas to create community that will support predictive sciences and their integration into the current one.

Virtual Laboratories

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A virtual laboratory is a special simulation program with the following characteristics. It is designed as an implementation of a mathematical model and its behaviour is restricted by the validity of the model rules. The actions of model entities can be modified by changing the parameters of its environment and/or the entities interaction rules. Although realistic behaviour is not fundamental, parameterization helps in looking for biological plausibility. In addition, incremental parametrical changes allow researchers to identify important stable and dividing situations. For consistency in simulation it is determined that virtual experiments are always (ceteris paribus) repeatable with the same results. Visualization of the virtual laboratory output is absolutely necessary.

Moreover, the concept of the virtual laboratory is a natural way to explain an abstract experiment in an interpersonal communication. Using a number of prepared experiments allows a better understanding of our digital cell models, and facilitates both general identification with problems and model behaviour orientation. According to our experience, using a visual form of information is crucial for interdisciplinary communication.

Virtual laboratory design makes it possible for an experimenter to change model parameters easily, which with adequate visualization also enables the use of the laboratory as an educational tool. Either alone or under supervision, users can modify the parameters of an experiment and follow the corresponding changes in simulation behaviour. Collaboration in teams is supported and recommended.