Analysis of Metabolic NetworksStructure, Properties, Visualization

Alexander Ullrich

Chair for BioinformaticsUniversity of Leipzig

TBI-Winterseminar, Bled, February 14-21

Outline

• Simulation

• Motivation

• Visualization

• General network analysis

• Metabolic network analysis

• Outlook

2 / 31

Simulation

• Bag of ribozymes.

• Algebraic chemistry model.

• Exchange of molecules with the environment.

3 / 31

Simulation

4 / 31

Simulation

cyanide, formaldehyde glycol; aldolcondensation, tautomerization

5 / 31

Simulation

Faulon, J-L, (2001) J Chem Inf Comput Sci 41:894-908

6 / 31

Motivation

• Study the early development of metabolism• Evolution of pathways (different scenarios)

?

• Analysis of the metabolic networks• network structure• network properties

7 / 31

Pathway Evolution

Retrograde Evolution

(b) Retro-evolution

End-product can be derived from more and more distantmetabolites

Example: glycolytic pathway, histidine biosynthesis

8 / 31

Pathway Evolution

Forward Evolution

more efficient extraction through deeper break-down of metabolitesExample: isoprene lipid pathway

9 / 31

Pathway Evolution

Patchwork Evolution

Enzyme Recruitment from other Pathways

10 / 31

Pathway Evolution

Comparisonred = older, blue = younger

Retrograde Evolution

Forward Evolution

Patchwork Evolution

11 / 31

Visualization

• bidirectional, bipartite graph

• nodes: metabolites, enzymes/reactions

• edges: participation in the same reaction

• dot layout: flow of mass downwards in the graph (if possible)

12 / 31

VisualizationRetrograde Evolution Forward Evolution Patchwork Evolution

13 / 31

Visualization

Flow Concentration

14 / 31

Visualization

Flow Concentration

15 / 31

Visualization

Flow Concentration

16 / 31

Visualization

Flow Concentration

17 / 31

Visualization

Flow Concentration

18 / 31

Visualization

Flow Concentration

19 / 31

Animation

20 / 31

General network analysis

• Connectivity Distribution• small vs big• early vs evolved

• Centrality, Entropy, ...• simulated vs real world

21 / 31

Connectivity Distribution

0

20

40

60

80

100

2 4 6 8 10 12

102030405060708090

100

22 / 31

Metabolic network analysis

We have sets of edges forming meaningful complex entities↓

pathways

• number of pathways → flexibilty

• change in case of single/multiple knockouts → robustness

• number of acceptable knockouts → robustness

23 / 31

Metabolic Pathway Analysis

24 / 31

Metabolic Pathway Analysis

25 / 31

Acceptable Knockouts

Minimal Knockout sets

{S → A} {A → B, A → C} {A → C} is not a MKS {B → D, C → D}

26 / 31

Knockout set size distribution

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

2 4 6 8 10 12 14 16 18 20

2030405060708090

100

27 / 31

Knockout effects

single R1 =

∑r

i=1 z i

r ∗ zdepletion R2 =

∑n

i=1 R i1

n

multiple R3(k) =

∑s(k)i=1 z i

s(k) ∗ zoverall R3(≤ K ) =

K∑

k=1

R3(k)pk

28 / 31

RobustnessRobustness = 0.51 Robustness = 0.67

0

0.2

0.4

0.6

0.8

1

2 4 6 8 10 12 0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

2 4 6 8 10 12

Robustness = 0.75 Robustness = 0.81

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

2 4 6 8 10 12 0

0.1

0.2

0.3

0.4

0.5

0.6

2 4 6 8 10 12

29 / 31

Future analysis methods

• Neutral network of a metabolic network (see RNA, GRN)

• Barrier trees of flux distributions (see xtof)

30 / 31

Acknowledgements

Christoph Flamm

Peter Stadler

Konstantin Klemm

Martin Mann

Markus Rohrschneider

31 / 31