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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
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.
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
Crowd Funded Human Microbiome – American Gut
4
>$800,000
8450 56
www.americangut.org
Almaden may 6th 2014 gilbert
House 1 Dynamic Bayesian Network
Predicting Interactions between people and
surfaces
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
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.
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.
The Hospital Microbiome shifts towards a human
microbiome following arrival of patients and staff
-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
Mapping human and building microbiota
US-EC Workshop on Marine Genomics: Next Generation Scientists for Next Generation Sequencing
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
Mapping air, water and green-site microbiota
US-EC Workshop on Marine Genomics: Next Generation Scientists for Next Generation Sequencing
Mapping air, water and green-site microbiota
US-EC Workshop on Marine Genomics: Next Generation Scientists for Next Generation Sequencing
Array
of
Things
Array of Things – Air Microbiome
Temperature
Humidity
Light
Sound
CO2
IR
Motion
Ultrasonic (proximity)
Precipitation
Anemometer
...
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
Current 30 node prototype
A 30-node
prototype is
being
developed for
deployment in
summer 2014
with internal
funding from
Argonne
National
Laboratory.
Business and
Tourism
Dense
Commerci
al
Neighborhood
s and
recreational
corridors
Vision for 2015*
* Funding Permitting
Vision for 2016*
Neighborhood
s and
recreational
corridors
Business and
Tourism
Dense
Commerci
al
* Even More Funding Permitting
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
Predicting the microbiome across all cities
Josh Ladau, Katie Pollard
23
Predicting Historical Changes in the Microbiome:
Facilitating Forecasting
Haiyen Chu, Josh Ladau
Research TeamInvesting Partners
(engineering team)
Charlie Catlett, Rob Jacob, Raj Sankaran,
Cristina Negri, Julian Gordon, Syed Hashsham,
Aaron Packman, etc.

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Almaden may 6th 2014 gilbert

  • 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.
  • 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.