Academic journal article Journal of Multidisciplinary Research

Extending the Human Spatiotemporal Comfort Zone with CAVERN - Computer-Based Augmented Virtual Environment for Realizing Nature

Academic journal article Journal of Multidisciplinary Research

Extending the Human Spatiotemporal Comfort Zone with CAVERN - Computer-Based Augmented Virtual Environment for Realizing Nature

Article excerpt


An intelligent multimedia device dubbed CAVERN - Computer-based Augmented Virtual Environment for Realizing Nature - is proposed as a quantum leap in molecular biology research. CAVERN is a system that leverages state-of-the-art technologies that include CAVE, supercomputing, electron microscopy, conceptual modeling, and biological text mining. After discussing the acute problems biological research is facing, the chapter introduces the new notion of the human spatiotemporal comfort zone, and a fourth multimedia learning assumption: the limited spatiotemporal comfort zone. Within this zone, people can use their senses to follow and understand complex systems currently accessible only through indirect observations. CAVERN translates nano-level processes into scenarios of human-size interacting molecules. A brief enumeration of the potential benefits of CAVERN in biology, health, and education is followed by a conceptual blueprint of CAVERN expressed via an Object-Process Methodology model. Finally, challenges and open problems in the way to achieving an operational CAVERN are presented.


CAVERN, spatiotemporal comfort zone, Object-Process Methodology, supercomputing


Science can be thought of as the process of reverse engineering nature. In recent years, we have witnessed an unprecedented increase in the number, variety, and complexity of information resources available to researchers, particularly in life sciences. We are in a pivotal moment in the study of life sciences, which is shifting from the study of single molecular processes to complete cellular pathways and the entire cell (Kitano, 2002).

The vast amount of data available also provides new opportunities; because searchable results are readily available (in databases such as Medline and PubMed), the data itself can confirm or refute conjectures, as many "future" predictions already have been tested through related experiments that were carried out for other reasons, and which "only" need to be revealed in the new context. Despite advances in the technologies available for sifting through data to retrieve new insights, the problem of how to fit the billions of known facts into a meaningful whole still remains unsolved.

Visualization and conceptual modeling are key ingredients in a possible solution for this problem. We need a holistic framework, an evolving conceptual model of the cell life system that can facilitate deep understanding through computer-graphics-based visualization. Current and future known facts and findings will be embedded into such a framework to foster insights on this incredibly complex cell life system. Armed with an adequate conceptual modeling language and methodology, biologists will be able to visualize, study, simulate, and analyze models of biological systems. The model will enable the mapping of knowledge gaps, closing them through the design and execution of wet laboratory experiments.

Conceptual modeling enables the process of making system-level sense of the vast number of pieces of information. The enormous number and variety of interactions among substances and processes in the cell primarily poses the qualitative problem of figuring out "what" and "how," which precedes the quantitative one.

A deep understanding of the processes within a biological system requires delving into each process and phenomenon, as most biological researchers have been doing. Comprehending how the system functions as a whole requires the integration of knowledge from the bottom up. A complementary, conceptual modeling approach to top-down modeling takes the cell life function as the system's main process and decomposes it into ever simpler processes down to the molecular level.

The Cognitive Assumptions

Humans assimilate data and information and convert it into meaningful knowledge and understanding of systems using words and pictures simultaneously. During eons of human evolution, the human brain was trained to capture and analyze images, in order for humans to escape predators and capture food. …

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