My 10 minute Talk at IBM Almalden's Sequencing the City on the Chicago Sequencing initiative.

1. Sequencing Chicago: Mapping Urban
Metabolism
Jack A Gilbert
@gilbertjacka
www.americangut.org
www.microbial-models.com
www.homemicrobiome.com
www.earthmicrobiome.org
www.hospitalmicrobiome.com

2. 400million city dwellers
China will add
221Chinese cities will have 1M
or more people.
And by 2030...
Rapid Urbanization in Developing Economies
of Chinese people will live in
cities with 1M or more people.
In 2025:
70%
....requiring the
construction of one New
York City every year for
several decades
Source: Foreign Policy Magazine, Sep/Oct
2010, “Megacities,” Richard Dobbs (McKinsey
Global Institute)
Landsat images of the Pearl River Delta in 1980 and 2005,
illustrating the impact of urbanization on the planet.
Between now and 2020, the Guangdong province will invest
$229B in 202 ongoing and 258 new transport infrastructure
projects to create a single 50M person city.

3. Produced by: S. Jiang, J. Ferreira, M. Gonzalez (2011) | Data Source: CMAP Travel Tracker Data, 2008.
Reference: Jiang, S., J. Ferreira, and M. González. 2012. Clustering Daily Patterns of Human Activities in the City. Data Mining and
Knowledge Discovery. Volume 25, Number 3, Pages 478-510
Mapping Megadata for Human Activity Patterns: survey data for 10,000
Chicago households on two weekdays in 2008

4. Crowd Funded Human Microbiome – American Gut
4
>$800,000
8450 56
www.americangut.org

6. House 1 Dynamic Bayesian Network
Predicting Interactions between people and
surfaces

7. Adding dogs into the mix make the interaction
space more complex.
US-EC Workshop on Marine Genomics: Next Generation Scientists for Next Generation Sequencing
House 4 Dynamic Bayesian Network

8. We can forensically identify physical
connections between people
Young Couple living with a lodger
- you can identify the ‘relationship’ from the microbiome
- you can also tell which parts of the house the lodger uses.
A young family (parents with 2 young boys) shows no such
delineation.

9. University of Chicago: Kim Handley, Simon Lax,
Daniel Smith, Kristen Starkey, John Alverdy,
Emily Landon, Jack Gilbert, etc.
Illinois Institute of Technology: Tiffanie Ramos,
Brent Stephens
University of Toronto: Jeff Siegel
Building science data summary
• 84 variables measured continuously every 5 minutes
• 100,000+ data points per variable
• 8.4 million+ data points collected
• over 8500+ hours of active data collection per variable
Microbial Community Analysis
• Bacterial, Fungal diversity and function over
12,000 samples
• Patients, Staff, Air, Water, Surfaces
Patient Records
• Age, Sex, disease burden, antibiotics, admission,
stay, blood tests, surgery, anesthesia, etc.

10. The Hospital Microbiome shifts towards a human
microbiome following arrival of patients and staff

11. -3 -2 -1 0 1 2
-2-1012
CCA1
CCA2
-101
F
DO
ALKALINITY
w_102
w_36
w_73
w_96
W_36, W_73
W_112, W_96
Chicago Area Waterways Project
112 36 96 73
0%
10%
20%
30%
40%
50%
60%
70%
fish mucus
human feces
Goose feces
Bird associated
Cat feces
mammal feces
animal skin
May June july Aug. Sept. May June July Aug. Sept. May Aug. Sept. May June July Aug. Sept.
Some samples were dominated by goose, human and animal feca
microbiota
• City Municipal
Water reclamation
Department Study
• $4M over 7 years
• Tracking sources of
impact
• Tracking impact of
water management
strategies

12. Mapping human and building microbiota
US-EC Workshop on Marine Genomics: Next Generation Scientists for Next Generation Sequencing

13. Mapping human and building microbiota
US-EC Workshop on Marine Genomics: Next Generation Scientists for Next Generation Sequencing
Homes,
Offices,
Hospitals,
Public Restrooms
Gyms,
Sports Stadiums,
Retail

14. Mapping air, water and green-site microbiota
US-EC Workshop on Marine Genomics: Next Generation Scientists for Next Generation Sequencing

15. Mapping air, water and green-site microbiota
US-EC Workshop on Marine Genomics: Next Generation Scientists for Next Generation Sequencing
Array
of
Things

16. Array of Things – Air Microbiome
Temperature
Humidity
Light
Sound
CO2
IR
Motion
Ultrasonic (proximity)
Precipitation
Anemometer
...

17. Array of Things – Air Microbiome
Temperature
Humidity
Light
Sound
CO2
IR
Motion
Ultrasonic (proximity)
Precipitation
Anemometer
...
Microbial community
Temperature
Carbon Dioxide
Carbon Monoxide
NOx
Humidity
Weather events
Wind speed
Wind direction
Bluetooth signals
Visibility
Noise level
Air quality
Air density
Local tweet mining

18. Current 30 node prototype
A 30-node
prototype is
being
developed for
deployment in
summer 2014
with internal
funding from
Argonne
National
Laboratory.

19. Business and
Tourism
Dense
Commerci
al
Neighborhood
s and
recreational
corridors
Vision for 2015*
* Funding Permitting

20. Vision for 2016*
Neighborhood
s and
recreational
corridors
Business and
Tourism
Dense
Commerci
al
* Even More Funding Permitting

21. Within 5 years: Automated Air Microbiome Detection
Rapid detection of:
• Pathogens
• Microbial imbalance
• Allergens
• Pollution
Influence policy:
• Urban planning
• Threat response
• Medical surveillance
• Pollution management
In all Environments:
• Air
• Water (rivers, lakes)
• Soil (parks, agriculture)
• Human bodies

22. Predicting the microbiome across all cities
Josh Ladau, Katie Pollard

23. 23
Predicting Historical Changes in the Microbiome:
Facilitating Forecasting
Haiyen Chu, Josh Ladau

24. Research TeamInvesting Partners
(engineering team)
Charlie Catlett, Rob Jacob, Raj Sankaran,
Cristina Negri, Julian Gordon, Syed Hashsham,
Aaron Packman, etc.