1. A Kernel-Based Model to Predict
Interaction Between Methanotrophic
and Heterotrophic Bacteria
Michiel Stock
KERMIT

2. What do you read next?
Books
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A general network:
Readers
4 ? 3 0 ? 5
1 4 ? ? 3 2
? 3 ? 2 ? 4
2

3. Some biological applications...
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3 2
0
1
EC 4.2.3.90
0
0
0
EC 4.6.1.11
EC 2.7.7.12
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EC 2.7.1.12 Food pairing
Enzyme
3 mei 2012
function Protein-ligand
prediction interaction
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4. Learning relations
Proteins Ligands
Learning algorithm based
on kernel matrices
⌦
3.78
( , ) SVM
9.76
( , ) RLS
( , ) 5.01
K-PCA 3.41
...
( , ) ... 7.66
( , )

5. Microbial interactions
How do heterotrophic microorganisms
influence the growth of methanotrophs?
METHANOTROPHS HETEROTROPHS
CARBON
COMPOUNDS
METHANE
VITAMINS?
ANTIBIOTICS?
5

6. Experimental setup
• 270 interactions
• 10 methanotrophs
• 27 heterotrophs
• Methane as sole
carbon source
• Measurement of OD
every 2 days for two
weeks 6

7. Optical density time series
Meth_5 and Hetero_2 Meth_7 and Hetero_10
0.20
0.30
0.25
0.15
max OD ●
0.20
●
●
●
●
0.10
max OD
OD
OD
0.15
●
0.10
●
0.05
max increasment OD
●
0.05
max increasment OD
●
● ●
0.00
● ● ● ●
0.00
●
0 5 10 15 0 5 10 15
Time (days) Time (days)
• Three types of labels
• maximal optical density
• maximal increase in optical density
• time of maximal increase in optical density
7

8. Quantification of interaction
Color Key
and Histogram Heat map of the log_2
8
of the relative max. OD
Count
6
4
2
0
−4 −2 0 2
Value
H6
H9
H7
H4
H 11
H5
H 18
H 16
H 21
H 17
H 20
H 24
H 14
H2
H 22
H 23
H 19
H 12
H 25
H1
H8
H3
H 10
H 13
H 15
M4
M8
M2
M7
M6
M3
M9
M1
M5
8

9. Matrix completion
• Key idea:
Measured
interactions
• remove a subset
of interactions
Learning
• predict missing
algorithm
values
• Using kernel Completed
methods or interaction
matrix
probabilistic PCA
9

10. Results matrix completion
ROC curve for max. increase density
missing values inference
1.0
0.8
Average true positive rate
0.6
0.4
0.2
10 % missing (AUC = 0.7482)
25 % missing (AUC = 0.7315)
50 % missing (AUC = 0.7253)
0.0
0.0 0.2 0.4 0.6 0.8 1.0
10
False positive rate

11. 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

12. Acknowledgments
• KERMIT
• Willem Waegeman
• Bernard De Baets
• LM UGent
• Sven De Groeve
• Kim Heylen
• Labmet
• Frederiek-Maarten Kerckhof
• Nico Boon
12