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

1. “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
http://lsmarr.calit2.net
1

2. 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 10-20 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.

3. 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

4. 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
www.bodymedia.com
The Content of This Slide from 2001 Larry Smarr
Calit2 Talk on Digitally Enabled Genomic Medicine

5. 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

6. 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, 2016
137
42
I Increased
Walking,
Aerobic, and
Resistance
Training,
All of Which
Have Health
Benefits

7. From Measuring Macro-Variables
to Measuring Your Internal Variables
www.technologyreview.com/biomedicine/39636

8. 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

9. 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

10. 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

11. 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)

12. Time Series Reveals Oscillations in Immune Biomarkers
Associated with Time Progression of Autoimmune Disease
Immune &
Inflammation
Variables
Weekly
Symptoms
Pharma
Therapies
Stool
Samples
2009 20142013201220112010 2015
Monitoring Your Body
Would Have Suggested
Intervention Now!

13. 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

14. 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. 44:256-262 (2007)
I Have Been Quantifying All Three

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

16. 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
Rs1004819
1.8x Increased Risk
Female
CD Onset
At 20-Years Old
NOD2 (1)
Rs2066844
2.08x Increased Risk
Subject with
Ileal Crohn’s
Subject with
Colonic Crohn’s
Source: Larry Smarr and 23andme

17. 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, 119-124 (2012)
23andme Has Collected
10,000 IBD Patient’s SNPs

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

19. 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 100-200 Million Illumina Short Reads (100 bases)
Larry Smarr
(Colonic Crohn’s)

20. 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

21. 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

22. 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

23. 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

24. Exploring the Dynamics
of the Human Microbiome Ecology

25. 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

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

27. 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.”
Source: Ponderosa Pine Fire Ecology
http://cpluhna.nau.edu/Biota/ponderosafire.htm

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

29. 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

30. 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, 1255-62 (2012)

31. 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

32. 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

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

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

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

36. 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

37. 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

38. 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

39. 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

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

41. 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, 513-554 (1916)

42. 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

43. 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

44. “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

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

46. How Automobiles Went From
a Sickcare System to a Healthcare System

47. The Transformation in Automobile Healthcare
Gives Us Insight into the Human Healthcare Shift to Come
http://onlinelibrary.wiley.com/doi/10.1002/biot.201100495/abstract

48. Modern Cars Have Massive Sensor Arrays Which Record Time Series
Enabling Computer Diagnostics For Early Warning
http://blog.asautoparts.com/5-common-symptoms-of-faulty-car-sensors/
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.
www.thepeoplehistory.com/carelectronics.html

49. 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.

50. 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

51. The Planetary Computer Fed by a Trillion Sensors
Will Drive a Global Industrial Internet
www.tsensorssummit.org
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

52. Toward a Future Healthcare System

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

54. 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

55. 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!

56. 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

57. 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

58. Thanks to Our Great Team!
Calit2@UCSD
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