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Translational Systems Biology of Acute Inflammation: Addressing the Translational Dilemma by Avoiding Ill-Posed Questions. 2014 Multi-scale Modeling Consortium Meeting September 3, 2014 Bethesda, MD Gary An, MD Associate Professor of Surgery Department of Surgery
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Translational Systems Biology of Acute Inflammation:Addressing the Translational Dilemma by Avoiding Ill-Posed Questions 2014 Multi-scale Modeling Consortium Meeting September 3, 2014 Bethesda, MD Gary An, MD Associate Professor of Surgery Department of Surgery University of Chicago, Chicago, IL
U.S. FDA“Critical Path” Document • March 2004 “Innovation or Stagnation”
The Multi-scale Translational Challenge Barriers to Understanding Organism Organs Tissues Vertical and Parallel Coupling Cells Molecules Output Genes
Traditional Scientific Cycle Increasing Dimensionality of Data Scientific Cycle in Data-Rich, High-throughput Environment Increased Complexity “Systems Diseases”
Translational Dilemma as a Mapping Problem • Biomed Research uses experimental proxy systems (in vitro, in vivo) • What knowledge extracted at each level is conserved when you move to the next? • Mechanism => Phenotype? • Scientific Cycle does not Scale • Leads to Ill-Posed Questions!
Translational Systems Biology • Dynamic Computational Modeling explicitly directed at Clinically Relevant Phenotypes
Jorge Luis Borges: “On Exactitude in Science” “In that Empire, the Art of Cartography attained such Perfection that the map of a single Province occupied the entirety of a City, and the map of the Empire, the entirety of a Province. In time, those Unconscionable Maps no longer satisfied, and the Cartographers Guilds struck a Map of the Empire whose size was that of the Empire, and which coincided point for point with it. The following Generations, who were not so fond of the Study of Cartography as their Forebears had been, saw that the vast Map was Useless...”
“Far better an approximate answer to the right question, which is often vague, than an exact answer to the wrong question, which can always be made precise.”- John Wilder Tukey
Finding the Right Question in SIRS/MOF • What is the actual disease? • How to account for the fact we have to treat people? • How do we use pre-clinical biological proxy models (and recognize their limitations)? • How do our conceptual models actually work? => Make them go!
The Importance of “Dynamics” • Dynamic => System evolves over time • Mechanistic => Approximations of Cause and Effect • Need to capture movement from Health to Disease… Same underlying processes => Different conditions => Different behaviors => Different Phenomena => Heterogeneity • Disease as a specific Dynamic State
Dynamic Knowledge Representation with Agent-based Modeling An, Shock Oct, 2001 and An, Critical Care Medicine Oct, 2004 • ABMs of Global Systemic Inflammation, circa 1990 • Endothelial/Blood interface • Activation/Propagation of Inflammation • Endothelial Cells and White Blood Cells • Examine Overall Dynamics of SIRS • What are the Clinical Phenotypes of Interest?
Results from In Silico Trials • No mediator-based intervention led to statistically significant improvement • Outcome could be changed => Antibiotics did improve survival • Interventions led to short term effects that rapidly reversed => “Pebble in the stream” • The problem was not parallel pathway redundancy, rather temporal, structural robustness • Systems “died” because they could not clear initial damage • More vigorous response => Better survival (as long as cellular-based) => Fletcher, et al ScTM 2014, 6(249)
Qualitative Dynamic Knowledge Representation • Instantiation of conceptual models = “Thought Experiments” • Provide means of “pre-testing” hypotheses and conceptual models • Advances knowledge via => nullification of flawed hypotheses* => identification of “plausible” conceptual models • *Exclude whole classes of hypotheses at time of candidate discovery!
Managing the Incompleteness of Knowledge • Knowledge will always be incomplete • What extent of knowledge is sufficient? • What is the basis of the rules => what is the literature? • Incomplete Rules => Did you leave something out? • Modeler Bias => Why did you choose what you chose? • “All models are wrong, some are useful…”
Computational Modeling Assistant (CMA)Semi-automating Hypothesis Evaluation
What does this all get you? • If Model Behavior matches real world observations • The “Thought Experiment” is a Plausible representation of the “real world” • Look for ways to “break” it • If Model Behavior does not match real world observations • Re-examine underlying assumptions • Utilize Modularity for differential fitness • Science Progresses via Hypothesis Nullification “It ain't what you don't know that gets you into trouble. It's what you know for sure that just ain't so.” -- Mark Twain
“Knowledge Ecologies:” Science as Evolution An, Science Translational Medicine, 2010
