Are we satisfied with the rate of drug development? Are we happy with the drugs that come to market? Are we getting our money’s worth in spending for basic biomedical research?
In Translational Systems Biology, Drs. Yoram Vodovotz and Gary An address these questions by providing a foundational description the barriers facing biomedical research today and the immediate future, and how these barriers could be overcome through the adoption of a robust and scalable approach that will form the underpinning of biomedical research for the future. By using a combination of essays providing the intellectual basis of the Translational Dilemma and reports of examples in the study of inflammation, the content of Translational Systems Biology will remain relevant as technology and knowledge advances bring broad translational applicability to other diseases.
Translational systems biology is an integrated, multi-scale, evidence-based approach that combines laboratory, clinical and computational methods with an explicit goal of developing effective means of control of biological processes for improving human health and rapid clinical application. This comprehensive approach to date has been utilized for in silico studies of sepsis, trauma, hemorrhage, and traumatic brain injury, acute liver failure, wound healing, and inflammation.
- Provides an explicit, reasoned, and systematic approach to dealing with the challenges of translational science across disciplines
- Establishes the case for including computational modeling at all stages of biomedical research and healthcare delivery, from early pre-clinical studies to long-term care, by clearly delineating efficiency and costs saving important to business investment
- Guides readers on how to communicate across domains and disciplines, particularly between biologists and computational researchers, to effectively develop multi- and trans-disciplinary research teams
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About the Author
Gary An is a trauma/critical care surgeon at The University of Chicago engaged in translational computational research. His work on system-level simulations of trauma and sepsis led him to a more general concern about the ability of biomedical researchers to represent their knowledge and hypotheses in a form that can be “executed, so that the dynamic consequences of their hypotheses can be seen and evaluated. He is the author of over 59 primary papers and book chapters, is a Senior Fellow of the University of Chicago Computation Institute and current president of the Swarm Development Group for simulation software.
Table of Contents
I. Introduction and Overview 1.1 Interesting Times: The Translational Dilemma and the Need for Translational Systems Biology of Inflammation
II. The Current Landscape: Where It Came From, How We Got Here, And What Is Wrong 2.1 A Brief History of the Philosophical Basis of the Scientific Endeavor 2.2 A Brief History of Biomedical Research up to the Molecular Biology Revolution 2.3 Biomedical Reserach Since the Molecular Revolution: An Embarassment of Riches 2.3 Randomized Clinical Trials: A Bridge Too Far? 2.5 Complexity in Biomedical Research: Mysticism Versus Methods 2.6 Human Nature, Politics, and Translational Intertia
III. Translational Systems Biology: How We Propose To Fix The Problems Of The Current Biomedical Research Landscape 3.1 Towards Translational Systems Biology of Inflammation 3.2 Dynamic Knowledge Representation and the Power of Model Making 3.3 A Roadmap for a Rational Future: A Systematic Path for the Design and Implemetation of New Therapeutics
IV. Tools And Implementation Of Translational Systems Biology: This Is How We Do It 4.1 From Data to Knowledge in Translational Systems Biology: An Overview of Computational Approaches Across the Scientific Cycle 4.2 Data-Driven and Statistical Models: Everything Old is New Again 4.3 Mechanistic Modeling of Critical Illness Using Equations 4.4 Agent-Based Modeling and Translational Systems Biology: An Evolution in Parallel 4.5 Getting Science to Scale: Accelerating the Development of Translational Computational Models
V. A New Scientific Cycle for Translational Research and Healthcare Delivery 5.1 What is Old is New Again: The Scientific Cycle in the Twenty-First Century and Beyond