Computational Methods in Systems Biology

A Stochastic Model for the Formation of Spatial Methylation Patterns 173

Index of pattern Z

0 102030405060

P(Z)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Wild-typePrediction (neighborhood dependent)

Prediction (neighborhood independent)

(^0) 0 102030405060
0.01
0.02
0.03
0.04
0.05
(a) Afp
Index of pattern Z
0 102030405060
P(Z)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
(^0) 0 102030405060
0.01
0.02
0.03
0.04
0.05
(b) L1
Index of pattern Z
0 102030405060
P(Z)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
(^0) 0 102030405060
0.01
0.02
0.03
0.04
0.05
(c) IAP
Index of pattern Z
0 102030405060
P(Z)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
(^0) 0 102030405060
0.01
0.02
0.03
0.04
0.05
(d) Tex13
Index of pattern Z
0 102030405060
P(Z)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
(^0) 0 102030405060
0.01
0.02
0.03
0.04
0.05
(e) mSat
Fig. 6.The figures show an example for the predicted (neighborhood dependent and
neighborhood independent) and the measured pattern distribution for each locus. The
inset shows a zoomed in version of the distribution.
of the parameter vector, a hierarchical model based on beta distributions is pro-
posed. Another difference to our model is the distinction between de novo rates
for parent and daughter strand. However, this can easily be included in future
work. A density-dependent Markov model was proposed [ 14 ]. In this model, the
probabilities of (de-)methylation events may depend on the methylation den-
sity in the CpG neighborhood. In addition, a neighboring sites model has been
developed, in which the probabilities for a given site are directly influenced by