Advertisement

The Rationale and Implementation of Translational Systems Biology as a New Paradigm for the Study of Inflammation

Chapter

Abstract

The contents of this book represent a new way of looking at the role of inflammation in the balance between health and disease and present a series of advanced computational methods applied with an awareness of system-level consequences towards an explicit translational goal. Grounded in the fundamentals of scientific investigation, translational systems biology brings with it the reminder that the quest to improve human health involves being able to effectively intervene on the processes that generate health and disease. Furthermore, the foundational properties of inflammation that challenge the traditional and neotraditional research community, namely complexity and nonlinearity of structure and dynamics, also necessarily scale to the level of that community itself. As a result, the implementation of Translational Systems Biology not only provides a pathway towards discovering and engineering new and better therapies for specific disease processes but also represents a therapeutic paradigm shift for an increasingly ailing and insufficient biomedical research structure.

Keywords

Biomedical Research Dynamic Equilibrium State Data Snapshot Actual Clinical Trial Dynamic Network Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

We would like to thank all the authors who have joined us in this book. Our Translational Systems Biology work was supported in part by the National Institutes of Health grants R01GM67240, P50GM53789, R33HL089082, R01HL080926, R01AI080799, R01HL76157, R01DC008290, and UO1DK072146; National Institute on Disability and Rehabilitation Research grant H133E070024; National Science Foundation grant 0830-370-V601; a Shared University Research Award from IBM, Inc.; and grants from the Commonwealth of Pennsylvania, the Pittsburgh Life Sciences Greenhouse, and the Pittsburgh Tissue Engineering Initiative/Department of Defense.

References

  1. 1.
    Alberts B (2013) Am I, wrong? Science 339(6125):1252PubMedCrossRefGoogle Scholar
  2. 2.
    An G (2010) Closing the scientific loop: bridging correlation and causality in the petaflop age. Sci Transl Med 2:41ps34PubMedCrossRefGoogle Scholar
  3. 3.
    An G, Bartels J, Vodovotz Y (2011) In silico augmentation of the drug development pipeline: examples from the study of acute inflammation. Drug Dev Res 72:1–14CrossRefGoogle Scholar
  4. 4.
    Namas R, Zamora R, Namas R, An G, Doyle J, Dick TE, Jacono FJ, Androulakis IP, Chang S, Billiar TR et al (2012) Sepsis: something old, something new, and a systems view. J Crit Care 27:314e1–314e11CrossRefGoogle Scholar
  5. 5.
    An G, Namas R, Vodovotz Y (2012) Sepsis: from pattern to mechanism and back. Crit Rev Biomed Eng 40:341–351PubMedCrossRefGoogle Scholar
  6. 6.
    An G, Nieman G, Vodovotz Y (2012) Computational and systems biology in trauma and sepsis: current state and future perspectives. Int J Burns Trauma 2:1–10PubMedGoogle Scholar
  7. 7.
    An G, Nieman G, Vodovotz Y (2012) Toward computational identification of multiscale tipping points in multiple organ failure. Ann Biomed Eng 40:2412–2424CrossRefGoogle Scholar
  8. 8.
    An G (2012) Small to large, lots to some, many to few: in silico navigation of the Translational Dilemma. Crit Care Med 40(4):1334–1335PubMedCrossRefGoogle Scholar
  9. 9.
    Mi Q, Li NYK, Ziraldo C, Ghuma A, Mikheev M, Squires R, Okonkwo DO, Verdolini Abbott K, Constantine G, An G et al (2010) Translational systems biology of inflammation: potential applications to personalized medicine. Per Med 7:549–559PubMedCrossRefGoogle Scholar
  10. 10.
    Paul SM, Mytelka DS, Dunwiddie CT, Persinger CC, Munos BH, Lindborg SR, Schacht AL (2010) How to improve R&D productivity: the pharmaceutical industry’s grand challenge. Nat Rev Drug Discov 9(3):203–214PubMedGoogle Scholar
  11. 11.
    An G, Mi Q, Dutta-Moscato J, Solovyev A, Vodovotz Y (2009) Agent-based models in translational systems biology. Wiley Interdiscip Rev Syst Biol Med 1:159–171PubMedCrossRefGoogle Scholar
  12. 12.
    Glaser B (2010) Genetic analysis of complex disease – a roadmap to understanding or a colossal waste of money. Pediatr Endocrinol Rev 7(3):258–265PubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Department of SurgeryUniversity of ChicagoChicagoUSA
  2. 2.Department of SurgeryUniversity of PittsburghPittsburghUSA

Personalised recommendations