Translational Systems Biology

Translational Systems Biology

Concepts and Practice for the Future of Biomedical Research

1st Edition - October 1, 2014

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  • Authors: Yoram Vodovotz, Gary An
  • eBook ISBN: 9780123978905
  • Hardcover ISBN: 9780123978844

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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.

Key Features

  • 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


Biomedical researchers of complex diseases working on systems-driven approaches to clinical diagnosis; biomedical entrepreneurs looking for rational, cost-effective, and unified means of driving drug/device development from the pre-clinical stage to clinical trials and ultimate use in the marketplace.

Table of Contents

    • Dedication
    • Preface
    • Acknowledgments
    • Section I: Introduction and Overview
      • Chapter 1.1. Interesting Times: The Translational Dilemma and the Need for Translational Systems Biology of Inflammation
        • Primary Goal: Facilitate the Translation of Basic Biomedical Research to the Implementation of Effective Clinical Therapeutics
        • How to Approach This Book
        • References
    • Section II: The Current Landscape: Where It Came From, How We Got Here, and What Is Wrong
      • Chapter 2.1. A Brief History of the Philosophical Basis of the Scientific Endeavor: How We Know What We Know, and How to Know More
        • Models in the Cave
        • Earth at the Center: A Reasonable Mistake, and an Unreasonable Perpetuation
        • The Scientific Method of Francis Bacon
        • Newton: The (Justifiable) Origins of Physics Envy
        • The Problem of Induction: Hume’s Empiricism
        • Logic and Its True/False Promise: Logical Positivism, Godel and Popper
        • Charles Peirce Suggests Taking a Guess: The Abductive Approach
        • The Mapping Problem: Back to Plato?
        • Suggested Additional Readings
      • Chapter 2.2. A Brief History of Biomedical Research up to the Molecular Biology Revolution
        • Reference
        • Suggested Additional Readings
      • Chapter 2.3. Biomedical Research Since the Molecular Revolution: An Embarrassment of Riches
        • References
      • Chapter 2.4. Randomized Clinical Trials: A Bridge Too Far?
        • References
      • Chapter 2.5. Complexity in Biomedical Research: Mysticism Versus Methods
        • References
        • Suggested Additional Readings
      • Chapter 2.6. Human Nature, Politics, and Translational Inertia
        • Setting the Table with Bacon
        • An Embarrassment of Riches
        • Shibboleths in Science: The Problem with Pedigrees
        • Incentives and the Professionalization of Science
        • Deep Pockets, with Holes: The Pharma Conundrum
        • Reference
    • Section III: Translational Systems Biology: How We Propose to Fix the Problems of the Current Biomedical Research Landscape
      • Chapter 3.1. Towards Translational Systems Biology of Inflammation
        • Primary Goal: Facilitate the Translation of Basic Biomedical Research to the Implementation of Effective Clinical Therapeutics
        • References
      • Chapter 3.2. Dynamic Knowledge Representation and the Power of Model Making
        • References
      • Chapter 3.3. A Roadmap for a Rational Future: A Systematic Path for the Design and Implementation of New Therapeutics
        • Rational Evaluation of Drug Candidates: Knowing What Might Work, and More Importantly, What Won’t
        • In silico Clinical Trials: Crossing the “Bridge Too Far”
        • From Populations to Individuals: Personalizing Medical Care
        • References
    • Section IV: Tools and Implementation of Translational Systems Biology: This is How We Do It
      • Chapter 4.1. From Data to Knowledge in Translational Systems Biology: An Overview of Computational Approaches Across the Scientific Cycle
        • Patterns in Physiology: Is There a “There” There?
        • Patterns of Molecules
        • References
      • Chapter 4.2. Data-Driven and Statistical Models: Everything Old Is New Again
        • Traditional Statistical Approaches to Analyzing Data
        • Data-Driven Modeling in Systems and Computational Biology
        • Statistical and Data-Driven Modeling: A Place for Big Data in Translational Systems Biology?
        • References
      • Chapter 4.3. Mechanistic Modeling of Critical Illness Using Equations
        • Modeling Inflammation Using ODEs
        • References
      • Chapter 4.4. Agent-Based Modeling and Translational Systems Biology: An Evolution in Parallel
        • Things Doing Things and the Wisdom of Crowds
        • Properties of Agent-Based Models
        • Agent-Based Modeling of Inflammation and the Development of Translational Systems Biology
        • Initial Simulations of Clinical Populations and In Silico Clinical Trials
        • Providing New Perspectives on Clinical Conditions
        • Integration and Unification: Linking Disease Processes, Biological Knowledge and Clinical Phenotypes
        • Integration and Unification: Bringing Together Biomedical Knowledge by Putting Humpty Dumpty Together Again
        • Resources for Agent-Based Modeling and Suggested Reading
        • References
      • Chapter 4.5. Getting Science to Scale: Accelerating the Development of Translational Computational Models
        • The Structure of the CMA
        • Knowledge Bases in the CMA
        • Fulfilling the Goals of Translational Systems Biology and the Democratization of Biomedical Science
        • References
    • Section V: A New Scientific Cycle for Translational Research and Health-Care Delivery
      • Chapter 5.1. What Is Old Is New Again: The Scientific Cycle in the Twenty-First Century and Beyond
        • Everything Old is New Again
        • “Data” is not the Answer; Knowledge is
        • High-Throughput Dynamic Knowledge Representation: A Community Effort
        • Success Through Failure
        • A Case for Disruption of the Fragmented Continuum
        • References
    • Index

Product details

  • No. of pages: 178
  • Language: English
  • Copyright: © Academic Press 2014
  • Published: October 1, 2014
  • Imprint: Academic Press
  • eBook ISBN: 9780123978905
  • Hardcover ISBN: 9780123978844

About the Authors

Yoram Vodovotz

Yoram Vodovotz
Yoram Vodovotz, Ph.D., is currently the President of the Society for Complexity in Acute Illness. His research interests include the biology of acute inflammation in shock states, chronic inflammatory diseases, wound healing, malaria, and restenosis. His work utilizes mathematical modeling to unify and gain insight into the biological interactions that characterize these inflammatory conditions. As the Director of the Center for Inflammation and Regenerative Modeling (CIRM) at the University of Pittsburgh’s McGowan Institute for Regenerative Medicine, he has been involved in the mathematical modeling of acute inflammatory states (e.g. septic or hemorrhagic shock, wound healing), including cellular and physiological elements, as part of a large, interdisciplinary collaborative team. He is also a co-founder of Immunetrics, Inc., a company that is commercializing this mathematical modeling work.

Affiliations and Expertise

University of Pittsburgh, Pittsburgh, PA, USA

Gary An

Gary An
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.

Affiliations and Expertise

University of Chicago, Chicago, IL, USA

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