Acknowledgements

5. Discussion

cellular level.

6. Conclusion

The concept of resilience is receiving increasing attention in chronic stress-related diseases. Resilience has been shown in clinical studies to play a protective role in patients with chronic conditions including osteoarthritis, breast and ovarian cancer, diabetes, and cardiovascular disease related to psychosocial dimensional levels. The purpose of this study is to explore the relationships between RNA-RNA interactions and to devise a measure of resilience at the

96 Applications of RNA-Seq and Omics Strategies - From Microorganisms to Human Health

5.1. Prospects, challenges, and limitations for resilience measure by variance in RNA-seq

5.2. Notion of the transcriptome as an information system

Although research on empirical indicators of robustness and resilience is rudimentary, there is already a fast-growing body of engineering modeling as well as empirical work in ecology. Nonetheless, major challenges remain in developing robust procedures for assessment of the transcriptome. A goal of systems biology is to analyze large-scale multidomain networks to reveal relationships between network structures and their biological function. While generally, it is not feasible to visualize and understand whole networks, a common analysis is to partition the network into subnetworks responsible for specific biological functions. Since biological functions can be carried out by particular groups of molecules, dividing networks into naturally grouped clusters can help investigate the relationships between function and topology of system networks or reveal hidden knowledge behind them. The expression in Eq. (8) for resilience is a measure of the size of network interactions possible within a transcriptome.

The body of this work considers the transcriptome as an information system modeling a dynamic system. A dynamic system is characterized by two concerns: the static structure and dynamic behavior. The structural elements of dynamic systems are those elements which may be identified from static snapshots of the problem space; while dynamic aspects involve those semantic elements of the system that exist over the time domain. While modeling the static aspects of an information system like RNA expression data, an understanding of the dynamic nature of information systems in the cell is low. Behavioral issues of large information systems are usually complex, consisting of many interactive sessions with the outside environment, tasks like coordination and collaboration among different entities. Dynamic systems can exhibit emergent properties that result from the dynamics, and which cannot be attributed to static structural factors. However, given any real world information system consisting of many multistream interactive processes, emergent properties are usually complex, without a common characteristic structure. Such emergent properties are beginning to be addressed with the transcriptome.

We show that the transcriptome can be modeled as an information system with emergent dynamic properties. The term nebula regulation is introduced to consider the regulatory effects This work is supported in part by 8U54MD007588, G12MD007602, P50 HL117929, and P30 HL107238 grants from NIH/National Institute on Minority Health and Health Disparities. The content is solely the responsibility of the author and does not necessarily represent official views of the respective institutions.
