A Kernel-Based Model to Predict Interaction Between Methanotrophic

and Heterotrophic BacteriaMichiel Stock

KERMIT

What do you read next?

2

3 2 ? 5 ? ?

4 ? 3 0 ? 5

1 4 ? ? 3 2

? 3 ? 2 ? 4

Books

Read

ers

A general network:

Some biological applications...

EC 2.7.7.12

EC 4.2.3.90

EC ?.?.?.?EC 2.7.7.34

EC 4.6.1.11

EC 2.7.1.12

1

0

0

3

0

2

02

0

zondag, 13 mei 2012

Enzyme function

prediction

Food pairing

Protein-ligand interaction

3

Learning relations

⌦

( , )( , )( , )

...

( , )( , )

7.663.415.019.763.78

SVMRLS

K-PCA...

LigandsProteins

Learning algorithm based on kernel matrices

Microbial interactions

How do heterotrophic microorganisms influence the growth of methanotrophs?

METHANOTROPHS HETEROTROPHS

METHANE

CARBONCOMPOUNDS

VITAMINS?ANTIBIOTICS?

5

Experimental setup

• 270 interactions

• 10 methanotrophs

• 27 heterotrophs

• Methane as sole carbon source

• Measurement of OD every 2 days for two weeks 6

Optical density time series

• Three types of labels• maximal optical density• maximal increase in optical density• time of maximal increase in optical density

0 5 10 15

0.00

0.05

0.10

0.15

0.20

0.25

0.30

Meth_5 and Hetero_2

Time (days)

OD

●

●

●

●●

●

●

●

max OD

max increasment OD

0 5 10 15

0.00

0.05

0.10

0.15

0.20

Meth_7 and Hetero_10

Time (days)O

D

● ● ● ●

●

●

●

●

max OD

max increasment OD

7

Quantification of interaction

M 4

M 8

M 2

M 7

M 6

M 3

M 9

M 1

M 5

H 15H 13H 10H 3H 8H 1H 25H 12H 19H 23H 22H 2H 14H 24H 20H 17H 21H 16H 18H 5H 11H 4H 7H 9H 6

Heat map of the log_2 of the relative max. OD

−4 −2 0 2Value

02

46

8

Color Keyand Histogram

Cou

nt

8

Matrix completion

• Key idea:• remove a subset

of interactions• predict missing

values• Using kernel

methods or probabilistic PCA

Measuredinteractions

Completed interaction

matrix

Learningalgorithm

9

Results matrix completion

False positive rate

Aver

age

true

posi

tive

rate

0.0 0.2 0.4 0.6 0.8 1.0

0.0

0.2

0.4

0.6

0.8

1.0

ROC curve for max. increase density missing values inference

10 % missing (AUC = 0.7482)25 % missing (AUC = 0.7315)50 % missing (AUC = 0.7253)

10

Take-home messages• Many biological

problems are about relations

• Data mining as a useful tool for analyzing experiments

• Predictive models to aid wet-lab experiments 11

Acknowledgments

• KERMIT• Willem Waegeman• Bernard De Baets

• LM UGent• Sven De Groeve• Kim Heylen

• Labmet• Frederiek-Maarten Kerckhof• Nico Boon

12