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Real networks from 1,181 bacterial species The percolation of metabolism and bacterial lifestyle and evolvability Germán Plata, PhD & Dennis Vitkup, PhD Department of Systems Biology, Columbia University Medical Center, New York, NY 10032, [email protected] Defining metabolic network connectivity Conclusions Impact on genome architecture Can metabolic network diversity across bacteria be seen as a percolation process? If so, what are its consequences for microbial physiology, ecology and evolution? A percolation transition in metabolism Species interactions and evolvability Percolation in random networks 600 800 1000 1200 1400 1600 1800 0 20 40 60 Number of growth-suporting carbon sources Number of reactions 400 600 800 1000 1200 1400 1600 1800 0 2 4 6 8 10 Complex media ingredients Number of reactions 600 800 1000 1200 1400 1600 1800 0.0 0.2 0.4 0.6 0.8 1.0 Fraction obligate symbionts Number of reactions 500 2000 5000 20000 1000 10000 1 10 100 1000 Number of model reactions: Moving average Piecewise linear fit Number of transcription factors Number of genes 300 900 1800 600 800 1000 1200 1400 1600 1800 0 5 10 15 20 25 Reactions required for new carbon source Number of reactions 600 800 1000 1200 1400 1600 1800 0.0 0.2 0.4 0.6 0.8 1.0 Cross-feeding probability Number of reactions Metabolic networks were defined based on the ability of mass (carbon) to flow between metabolites (right) 1,181 networks were obtained from Kbase, each from a different bacterial genus 600 800 1000 1200 1400 1600 1800 0 50 100 150 200 250 Size of largest component Number of reactions Critical threshold at ~900 reactions or ~2,000 genes Less fastidious 1 More free-living 2 500 2000 5000 20000 1000 10000 200 500 100 1000 500 2000 5000 20000 1000 10000 50 200 500 2000 100 1000 500 2000 5000 20000 1000 10000 2 5 20 50 200 500 2000 10 100 1000 500 2000 5000 20000 1000 10000 20 50 200 500 2000 10 100 1000 Moving average Piecewise linear fit Information genes Total genes 300 900 1800 Number of model reactions: Metabolic genes Total genes Moving average Piecewise linear fit Signaling genes Total genes Moving average Piecewise linear fit Other genes Total genes Moving average Piecewise linear fit Condition-specific genes are lost more rapidly with decreasing genome size below percolation threshold How likely is one species to benefit from metabolic by- products of another? How many additional reactions are needed to grow on a new carbon source? More evolvable Random networks, same probability per reaction. Optimal networks, least number of reactions per carbon source supporting growth. Bacterial evolution avoids metabolic networks that are large and disconnected or small and connected. Differences in the connectivity of bacterial metabolic networks can be described as a percolation process Species with less than ~2,000 genes tend to be more fastidious, less easily evolvable, and less likely to cross- feed with a randomly chosen bacterium Differences are explained by metabolite interconversions supported by the giant component present in larger metabolic networks 1. Known growth media: komodo.modelseed.org 2. Lifestyle classification: Burstein et al. 2015. Nat Commun. 6, 8493 Funding: National Institute of General Medical Sciences GM079759 to DV Species 1 Species 2 A percolation process describes the changes in the connectivity of a network as nodes or edges are removed Each dot represents the metabolic network of a different bacterium Percolation threshold How many carbon sources support growth? Arrows indicate percolation thresholds Obligate parasitic or symbiotic lifestyles are common below the percolation threshold 250 500 750 1000 1250 1500 1750 2000 0 50 100 150 200 250 Size of largest component Number of reactions

Known growth media: komodo.modelseed.org Lifestyle ...gap2118/papers/IGCPoster.pdf · 2 4 6 8 10 ents Number of reactions 600 800 1000 1200 1400 1600 1800 0.0 0.2 0.4 0.6 0.8 onts

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Page 1: Known growth media: komodo.modelseed.org Lifestyle ...gap2118/papers/IGCPoster.pdf · 2 4 6 8 10 ents Number of reactions 600 800 1000 1200 1400 1600 1800 0.0 0.2 0.4 0.6 0.8 onts

Real networks from 1,181bacterial species

The percolation of metabolism and bacterial

lifestyle and evolvability Germán Plata, PhD & Dennis Vitkup, PhD

Department of Systems Biology, Columbia University Medical Center, New York, NY 10032, [email protected]

Defining metabolic network connectivity

Conclusions

Impact on genome architecture

Can metabolic network diversity across bacteria be seenas a percolation process? If so, what are its consequencesfor microbial physiology, ecology and evolution?

A percolation transition in metabolism

Species interactions and evolvability

Percolation in random networks

600 800 1000 1200 1400 1600 1800

0

20

40

60

Num

ber

of gro

wth

-suport

ing

carb

on s

ourc

es

Number of reactions

400 600 800 1000 1200 1400 1600 1800

0

2

4

6

8

10

Com

ple

x m

edia

ingre

die

nts

Number of reactions

600 800 1000 1200 1400 1600 1800

0.0

0.2

0.4

0.6

0.8

1.0

Fra

ctio

n o

blig

ate

sym

bio

nts

Number of reactions

500 2000 5000 200001000 10000

1

10

100

1000

Number of

model reactions:

Moving average

Piecewise linear fit

Num

ber

of tr

anscription facto

rs

Number of genes

300

900

1800

600 800 1000 1200 1400 1600 1800

0

5

10

15

20

25

Reactions r

equired for

new

carb

on s

ourc

e

Number of reactions

600 800 1000 1200 1400 1600 1800

0.0

0.2

0.4

0.6

0.8

1.0

Cro

ss-f

eed

ing p

rob

ab

ility

Number of reactions

• Metabolic networks were defined based on the abilityof mass (carbon) to flow between metabolites (right)

• 1,181 networks were obtained from Kbase, each from adifferent bacterial genus

600 800 1000 1200 1400 1600 1800

0

50

100

150

200

250

Siz

e o

f la

rgest com

ponent

Number of reactions

Critical threshold at ~900 reactions or ~2,000 genes

Less fastidious1More free-living2

500 2000 5000 200001000 10000

200

500

100

1000

500 2000 5000 200001000 1000050

200

500

2000

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500 2000 5000 200001000 100002

5

20

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500 2000 5000 200001000 10000

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Moving average

Piecewise linear fit

Info

rma

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n g

en

es

Total genes

300

900

1800Number of

model reactions:

Me

tab

olic

ge

ne

s

Total genes

Moving average

Piecewise linear fit

Sig

na

ling

ge

ne

s

Total genes

Moving average

Piecewise linear fit

Oth

er

ge

ne

s

Total genes

Moving average

Piecewise linear fit

Condition-specific genes are lost more rapidly withdecreasing genome size below percolation threshold

How likely is one species to benefit from metabolic by-products of another?

How many additional reactions are needed to grow on anew carbon source?

More evolvable

Random networks, same

probability per reaction.

Optimal networks, least

number of reactions per carbonsource supporting growth.

Bacterial evolution avoids metabolic networks that arelarge and disconnected or small and connected.

• Differences in the connectivity of bacterial metabolicnetworks can be described as a percolation process

• Species with less than ~2,000 genes tend to be morefastidious, less easily evolvable, and less likely to cross-feed with a randomly chosen bacterium

• Differences are explained by metabolite interconversionssupported by the giant component present in largermetabolic networks

1. Known growth media: komodo.modelseed.org 2. Lifestyle classification: Burstein et al. 2015. Nat Commun. 6, 8493 Funding: National Institute of General Medical Sciences GM079759 to DV

Species 1 Species 2

A percolation process describes the changes in theconnectivity of a network as nodes or edges are removed

Each dot represents the metabolicnetwork of a different bacterium

Percolation threshold How many carbon sourcessupport growth?

Arrows indicate percolationthresholds

Obligate parasitic or symbiotic lifestyles are commonbelow the percolation threshold

250 500 750 1000 1250 1500 1750 2000

0

50

100

150

200

250

Siz

e o

f la

rgest com

ponent

Number of reactions