“Quantifying Your Dynamic Human Body (Including Its Microbiome), Will Move Us From a Sickcare System to a Healthcare System” Invited Presentation Microbiology and the Microbiome and the Implications for Human Health Analytic, Life Science & Diagnostic Association (ALDA) 2016 Senior Management Conference Half Moon Bay, CA October 3, 2016 Dr. Larry Smarr Director, California Institute for Telecommunications and Information Technology Harry E. Gruber Professor, Dept. of Computer Science and Engineering Jacobs School of Engineering, UCSD 1

Conference Abstract “For the past several years, Dr. Smarr has been engaged in a computer-aided study of his body. Larry has been charting his bodily input and output, as well as taking periodic blood and stool tests for five years as part of a new generation of medical research that is focusing on early detection of disease states. Studying the microbiome is part of this area of medical research since there are 100 times as many genes on the microbial DNA as your human DNA and yet this is currently outside of medical practice. Larry believes that over the next years efforts like his will enable scientists to create computational models of your body, grounded in you and your microbiome's genome, and—using longitudinal time series of data refreshed continually with measurements from your body and collated with similar readings from millions of other similarly monitored bodies. Mining this enormous database, software will produce detailed guidance about diet, supplements, exercise, medication, or treatment—guidance based on a precise reading of your own body’s peculiarities and its status in real time. And, at that time, says Larry, you will have a scientific basis for medicine and the current US "Sickcare" system will be replaced by a true "Healthcare" system.

From One to a Trillion Data Points Defining Me in 15 Years: The Exponential Rise in Body Data Weight Blood Biomarker Time Series Human Genome SNPs Microbiome Metagenomic Time Series Improving Body Discovering Disease Human Genome Genomics Big Data Tsunami Imagine Following A Hundred Million Quantified People

Calit2 Has Been Had a Vision of “the Digital Transformation of Health” for 15 Years Next Step—Putting You On-Line! –Wireless Internet Transmission –Key Metabolic and Physical Variables –Model -- Dozens of Processors and 60 Sensors / Actuators Inside of our Cars Post-Genomic Individualized Medicine –Combine –Genetic Code –Body Data Flow –Use Powerful AI Data Mining Techniques The Content of This Slide from 2001 Larry Smarr Calit2 Talk on Digitally Enabled Genomic Medicine

Over the Last Decade, I Have Used a Variety of Personal Sensors To Quantify My Body & Drive Behavioral Change Withings/iPhone- Blood Pressure Zeo-Sleep Azumio-Heart Rate MyFitnessPal- Calories Ingested FitBit - Daily Steps & Calories Burned Withings WiFi Scale - Daily Weight

Wireless Monitoring Produced Time Series That Helped Me Improve My Health Since Starting November 3, 2011 Total Distance Tracked 6180 miles = Round Trip San Diego to Nome, Alaska Total Vertical Distance Climbed 190,000 ft. = 6.5x Mt. Everest My Resting Heartrate Fell from 70 to 40! Elliptical Walking Sunday January 17, I Increased Walking, Aerobic, and Resistance Training, All of Which Have Health Benefits

From Measuring Macro-Variables to Measuring Your Internal Variables

As a Model for the Precision Medicine Initiative, I Have Tracked My Internal Biomarkers To Understand My Body’s Dynamics My Quarterly Blood Draw Calit2 64 Megapixel VROOM

Only One of My Blood Measurements Was Far Out of Range Complex Reactive Protein (CRP) is a Blood Biomarker for Detecting Presence of Inflammation Doctor: “Come Back When You Have a Symptom” Normal Range <1 mg/L

First Peak Was an Early Warning Sign of Developing Internal Disease State Normal Range <1 mg/L 27x Upper Limit Complex Reactive Protein (CRP) is a Blood Biomarker for Detecting Presence of Inflammation Episodic Peaks in Inflammation Followed by Spontaneous Drops

Longitudinal Time Series Revealed Oscillatory Behavior in an Immune Variable That is Antibacterial Normal Range <7.3 µg/mL 124x Upper Limit for Healthy Lactoferrin is a Protein Shed from Neutrophils - An Antibacterial that Sequesters Iron Typical Lactoferrin Value for Active Inflammatory Bowel Disease (IBD)

Time Series Reveals Oscillations in Immune Biomarkers Associated with Time Progression of Autoimmune Disease Immune & Inflammation Variables Weekly Symptoms Pharma Therapies Stool Samples Monitoring Your Body Would Have Suggested Intervention Now!

Descending Colon Sigmoid Colon Threading Iliac Arteries Major Kink Confirming the IBD (Colonic Crohn’s) Hypothesis: Finding the “Smoking Gun” with MRI Imaging I Obtained the MRI Slices From UCSD Medical Services and Converted to Interactive 3D Working With Calit2 Staff Transverse Colon Liver Small Intestine Diseased Sigmoid Colon Cross Section MRI Jan 2012 Severe Colon Wall Swelling

Why Did I Have an Autoimmune Disease like Crohn’s Disease? Despite decades of research, the etiology of Crohn's disease remains unknown. Its pathogenesis may involve a complex interplay between host genetics, immune dysfunction, and microbial or environmental factors. --The Role of Microbes in Crohn's Disease Paul B. Eckburg & David A. Relman Clin Infect Dis. 44: (2007) I Have Been Quantifying All Three

I Found I Had One of the Earliest Known SNPs Associated with Crohn’s Disease From SNPs Associated with CD Polymorphism in Interleukin-23 Receptor Gene — 80% Higher Risk of Pro-inflammatory Immune Response NOD2 IRGM ATG16L1

There May Be a Correlation Between CD SNPs and Where and When the Disease Manifests Me-Male CD Onset At 60-Years Old Il-23R Rs x Increased Risk Female CD Onset At 20-Years Old NOD2 (1) Rs x Increased Risk Subject with Ileal Crohn’s Subject with Colonic Crohn’s Source: Larry Smarr and 23andme

IBD is a “Spectrum” Disorder Stratified by a Personal Combination of the 163 Known SNP Loci Associated with IBD The width of the bar is proportional to the variance explained by that locus “Host–microbe interactions have shaped the genetic architecture of inflammatory bowel disease,” Jostins, et al. Nature 491, (2012) 23andme Has Collected 10,000 IBD Patient’s SNPs

Using Supercomputers and Deep Metagenomics to Discover the Shifts in Microbiome Ecology in Health and Disease

An Initial Study of the Variation of the Human Gut Microbiome Across Populations and Within an Individual Over Time 5 Ileal Crohn’s Patients, 3 Points in Time 2 Ulcerative Colitis Patients, 6 Points in Time “Healthy” Individuals Larry Smarr, Weizhong Li, Sitao Wu, UCSD Graphic Source: Jerry Sheehan, Calit2 Total of 27 Billion Reads Or 2.7 Trillion Bases Inflammatory Bowel Disease (IBD) Patients 250 Subjects 1 Point in Time 7 Points in Time Each Sample Has Million Illumina Short Reads (100 bases) Larry Smarr (Colonic Crohn’s)

To Map Out the Dynamics of Autoimmune Microbiome Ecology Couples Next Generation Genome Sequencers to Big Data Supercomputers Source: Weizhong Li, UCSD Our Team Used 25 CPU-years to Compute Comparative Gut Microbiomes Starting From 2.7 Trillion DNA Bases of My Samples and Healthy and IBD Subjects Illumina HiSeq 2000 at JCVI SDSC Gordon Data Supercomputer

The Supercomputer Converts Tens of Billions of DNA Fragments Into Relative Abundance of Hundreds of Microbial Species Average Over 250 Healthy People From NIH Human Microbiome Project Note Log Scale Clostridium difficile

We Found Major State Shifts in Microbial Ecology Phyla Between Healthy and Two Forms of IBD Most Common Microbial Phyla Average HE Average Ulcerative Colitis Average LS Colonic Crohn’s Average Ileal Crohn’s Collapse of Bacteroidetes Great Increase in Actinobacteria Explosion of Proteobacteria Hybrid of UC and CD High Level of Archaea

Metagenomic Sequencing the Stool of 300 Patients Sorted Out Their Health or Disease Type Source: Thomas Hill, Ph.D. Executive Director Analytics Dell | Information Management Group, Dell Software Healthy Ulcerative Colitis Colonic Crohn’s Ileal Crohn’s

Exploring the Dynamics of the Human Microbiome Ecology

The Human Gut as a Super-Evolutionary Microbial Cauldron Enormous Density –1000x Ocean Water Highly Dynamic Microbial Ecology –Hundreds to Thousands of Species Horizontal Gene Transfer Phages Adaptive Selection Pressures (Immune System) –Innate Immune System –Adaptive Immune System –Macrophages and Antimicrobial proteins Constantly Changing Environmental Pressures –Diet –Antibiotics –Pharmaceuticals

Time Series Reveals Autoimmune Dynamics of Gut Microbiome by Phyla Therapy Six Metagenomic Time Samples Over 16 Months

Lessons From Ecological Dynamics I: Invasive Species Dominate After Major Species Destroyed ”In many areas following these burns invasive species are able to establish themselves, crowding out native species.” invasive species Source: Ponderosa Pine Fire Ecology

Almost All Abundant Species (≥1%) in Healthy Subjects Are Severely Depleted in Larry’s Gut Microbiome

Invasive Species Take Over Gut Microbiome in Disease State 152x 765x 148x 849x 483x 220x 201x 522x 169x 20 Most Abundant Species Source: Sequencing JCVI; Analysis Weizhong Li, UCSD LS December 28, 2011 Stool Sample Relative Abundance In Gut Microbiome

Lessons from Ecological Dynamics II: Gut Microbiome Has Multiple Relatively Stable Equilibria “The Application of Ecological Theory Toward an Understanding of the Human Microbiome,” Elizabeth Costello, Keaton Stagaman, Les Dethlefsen, Brendan Bohannan, David Relman Science 336, (2012)

We are Genomically Analyzing My Stool Time Series in a Collaboration with the UCSD Knight Lab Larry’s 40 Stool Samples Over 3.5 Years to Rob’s lab on April 30, 2015

LS Weekly Weight During Period of 16S Microbiome Analysis Abrupt Change in Weight and in Symptoms at January 1, 2014 Lialda Uceris Frequent IBD Symptoms Weight Loss Few IBD Symptoms Weight Gain Source: Larry Smarr, UCSD

My Microbiome Ecology Time Series Over 3 Years Source Justine Debelius, Knight Lab, UC San Diego

Coloring Samples Before (Blue) and After (Red) January 2014 Reveals Clustering Source Justine Debelius, Knight Lab, UC San Diego

An Apparent Sudden Phase Change Occurs Source Justine Debelius, Knight Lab, UC San Diego

My Gut Microbiome Ecology Shifted After Drug Therapy Between Two Time-Stable Equilibriums Correlated to Physical Symptoms Lialda & Uceris 12/1/13 to 1/1/14 12/1/13- 1/1/14 Frequent IBD Symptoms Weight Loss 7/1/12 to 12/1/14 Blue Balls on Diagram to the Right Principal Coordinate Analysis of Microbiome Ecology PCoA by Justine Debelius and Jose Navas, Knight Lab, UCSD Weight Data from Larry Smarr, Calit2, UCSD Weekly Weight Few IBD Symptoms Weight Gain 1/1/14 to 8/1/15 Red Balls on Diagram to the Right

My Fasting Glucose Level Seems to Have Also Shifted in January 2014 Glucose Best Range 70 to 100 Prediabetes Range 100 to 125 Weight gain started

From N=1 to a Population of People with Disease Inflammatory Bowel Disease Biobank For Healthy and Disease Patients Drs. William J. Sandborn, John Chang, & Brigid Boland UCSD School of Medicine, Division of Gastroenterology Over 300 Enrolled Announced November 7, 2014

To Expand IBD Project the Knight/Smarr Labs Were Awarded ~ 1 CPU-Century Supercomputing Time Smarr Gut Microbiome Time Series –From 7 Samples Over 1.5 Years –To 50 Samples Over 4 Years IBD Patients: From 5 Crohn’s Disease and 2 Ulcerative Colitis Patients to ~100 Patients –50 Carefully Phenotyped Patients Drawn from Sandborn BioBank –43 Metagenomes from the RISK Cohort of Newly Diagnosed IBD patients New Software Suite from Knight Lab –Re-annotation of Reference Genomes, Functional / Taxonomic Variations –Novel Compute-Intensive Assembly Algorithms from Pavel Pevzner 8x Compute Resources Over Prior Study N=1 Microbiome Time Series Compared to Populations of Healthy and Sick Using Machine Learning and Data Analytics

Toward Computational Models of the Interaction of the Human Host and Its Microbiome

Forty Years of Computing Gravitational Waves From Colliding Black Holes – One Billion Times Increase in Supercomputer Speed! 1977 L. Smarr and K. Eppley Gravitational Radiation Computed from an Axisymmetric Black Hole Collision 40 Years 2016 LIGO Consortium Spiral Black Hole Collision MegaFLOPS PetaFLOPS Holst, et al. Bull. Amer. Math. Soc 53, (1916)

Complexity of Computing First Gut Microbiome Dynamics Versus First Dynamics of Colliding Black Holes My 1975 PhD Dissertation –Solving Einstein’s Equations of General Relativity for Colliding Black Holes and Grav Waves –CDC 6600 Megaflop/s –Hundred Hours of Computing Rob Knight & Smarr Gut Microbiome Map Using 800,000 Core-Hours on SDSC’s Comet –Mapping From Illumina Sequencing to Taxonomy and Gene Abundance Dynamics –Comet Petaflop/s –Comet Core is 40,000x CDC6600 Speed –~Million Core-Hours –10,000x Supercomputer Time Gut Microbiome Takes ~ ½ Billion Times the Compute Power of Early Solutions of Dynamic General Relativity

NCSA Numerical Astrophysics Group Used NCSA Supercomputers to Explain Cosmic Phenomena Mike Norman, Charles Evans, Roger Ove, John Hawley, Dean Sumi, Rob Wolff, Larry Smarr Gas Accretion Onto a Black Hole Creates “Exhaust Channels” Cosmic Jets Emerge from Galactic Centers Collision of Neutron Stars

“A Whole-Cell Computational Model Predicts Phenotype from Genotype” A model of Mycoplasma genitalium, 525 genes Using 1,900 experimental observations From 900 studies, They created the software model, Which requires 128 computers to run

Early Attempts at Modeling the Systems Biology of the Gut Microbiome and the Human Immune System

How Automobiles Went From a Sickcare System to a Healthcare System

The Transformation in Automobile Healthcare Gives Us Insight into the Human Healthcare Shift to Come

Modern Cars Have Massive Sensor Arrays Which Record Time Series Enabling Computer Diagnostics For Early Warning Before the computer diagnostics technology, most car owners did not know something was wrong with the engine until something drastic happened, such as overheating or running out of gas.

The Transition from Car “Sickcare” to Car “Healthcare” Was Enabled by Pattern Recognition Using Big Data Analytics “… using IBM big data and analytics technology, all available data sources can be analyzed to discover patterns and anomalies to predict and anticipate maintenance needs.

From Reactive Repairs for “Chronic Disease” to Quantified Cars That “Keep Themselves Healthy” “In the not-too-distant future, analytics will help organizations prevent incidents from occurring, rather than just being a tool to rapidly react to incidents.” --Rich Radi, director, Driver Excellence for ARI, the world’s largest privately held family-owned fleet management company

The Planetary Computer Fed by a Trillion Sensors Will Drive a Global Industrial Internet www-bsac.eecs.berkeley.edu/frontpagefiles/BSACGrowingMEMS_Markets_%20SEMI.ORG.html Next Decade One Trillion GE’s Industrial Internet is Currently Generating 10,000 TB/Day

Toward a Future Healthcare System

Big Data Analytics is a Key Component of The Future of Supercomputing

Next Generation Telescopes Will Keep Track of the Entire Universe On-Line in Five Years, Tracks ~40B Objects, Creates 10M Alerts/Night Within 1 Minute of Observing 2x40Gbps NCSA Supercomputer

Artificial Intelligence (AI) is Advancing at a Amazing Pace: Deep Learning Algorithms Working on Massive Datasets Training on 30M Moves, Then Playing Against Itself Less Than 2 Years!

From Self-Driving Cars to Personalized Medical Assistants Deep Learning Will Provide Artificial Intelligence to Coach Us to Wellness Where Medicine Coaching is Now Where Wellness Coaching is Going January 10, 2014

Can a Planetary Supercomputer with Artificial Intelligence Transform Our Sickcare System to a Healthcare System? Using this data, the planetary computer will be able to build a computational model of your body and compare your sensor stream with millions of others. Besides providing early detection of internal changes that could lead to disease, cloud-powered voice-recognition wellness coaches could provide continual personalized support on lifestyle choices, potentially staving off disease and making health care affordable for everyone. ESSAY An Evolution Toward a Programmable Universe By LARRY SMARR Published: December 5, 2011

Thanks to Our Great Team! Future Patient Team Jerry Sheehan Tom DeFanti Joe Keefe John Graham Kevin Patrick Mehrdad Yazdani Jurgen Schulze Andrew Prudhomme Philip Weber Fred Raab Ernesto Ramirez JCVI Team Karen Nelson Shibu Yooseph Manolito Torralba Ayasdi Devi Ramanan Pek Lum UCSD Metagenomics Team Weizhong Li Sitao Wu SDSC Team Michael Norman Mahidhar Tatineni Robert Sinkovits UCSD Health Sciences Team David Brenner Rob Knight Lab Justine Debelius Jose Navas Gail Ackermann Greg Humphrey William J. Sandborn Lab Elisabeth Evans John Chang Brigid Boland Dell/R Systems Brian Kucic John Thompson