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

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

-2-1

01

2

CCA1

CC

A2

-10

1

F

DO

ALKALINITY

w_102

w_36

w_73

w_96

W_36, W_73

W_112, W_96Chicago 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 fecal 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

TemperatureHumidityLightSoundCO2IRMotionUltrasonic (proximity)PrecipitationAnemometer...

Array of Things – Air Microbiome

TemperatureHumidityLightSoundCO2IRMotionUltrasonic (proximity)PrecipitationAnemometer...

Microbial communityTemperatureCarbon DioxideCarbon MonoxideNOxHumidityWeather eventsWind speedWind directionBluetooth signalsVisibilityNoise levelAir qualityAir densityLocal 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

Commercial

Neighborhood

s and

recreational corridors

Vision for 2015*

* Funding Permitting

Vision for 2016*

Neighborhood

s and

recreational corridors

Business and Tourism

Dense

Commercial

* 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.