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CIS 4930/6930 – Recent Advances in Bioinformatics

Spring 2014

Network construction from RNAi data

Tamer Kahveci

Signaling Networks

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MAPK network

Signal reachability

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Receptor Reporter

Luciferase

Signaling and RNA Interference

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Receptor Reporter

Luciferase

X Not critical

X Critical

Signaling Network Reconstruction from RNAi data

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Receptor Reporter

Not criticalCritical

RNAi data and Reference Network

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Receptor Reporter

Not criticalCritical

Reference networkInsert

Delete

Not consistent !Consistent

!

Overview

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GR = (VR, ER)

Reference network

Constraints

1 10 0 0

GT = (VT, ET)

Target network

1 0

SiNeC (Signal Network Constructor)

S-SiNeC (Scalable Signal Network Constructor)

Given Fin

d

Goal: Minimize the number of edit

operations to make the reference

consistent.

NP-Complete !

SiNeC algorithm

Three steps

1. Order the critical genes left to right based on the topology of GR. [Sloan, 1986]

– v1, v2, …, vc

2. Edge deletion phase

3. Edge insertion phase

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Step 1: Order critical genes

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Receptor Reporter3

1

2

Prioritize based on distance to the reporter + degree

Step 2: Edge deletion

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Purpose: Eliminate detours around critical genes

Receptor Reportervi vkvj

• Find all (undesirable) paths between non-consecutive critical genes.

• i.e., Paths which go through only noncritical genes• Edges are weighted with the number of such paths they

belong to.• Remove greedily starting from the largest weight until al

paths are disrupted.

Bypassed !!!

Step 3: Edge insertion

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Purpose: Make sure that critical are connected + noncritical genes are consistent

Receptor Reportervi-1 vi+1vi

Insert an edge from vi-1 to vi if 1. There is no path from vi-1 to vi.2. There is a noncritical gene on all paths from vi-1

to vi.

Overview

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GR = (VR, ER)

Reference network

Constraints

1 10 0 0

GT = (VT, ET)

Target network

1 0

SiNeC (Signal Network Constructor)

S-SiNeC (Scalable Signal Network Constructor)

Given Fin

d

Finding all the paths can be too tim

e

consuming for large networks

S-SiNeC algorithm

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Edge insertion0 0 0 None0 0 1 None0 1 0 None0 1 1 A1

1 0 0 A2 + A3 + A4

1 0 1 A2 + A4

1 1 0 A3 + A4

1 1 1 A4

Criti

cal

Left

reac

habl

e

Righ

t rea

chab

le

Edge deletion

Reference network

vs vtvi

S-SiNeC: Edge insertion (A1)

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Reference network

vs vtvi

L R

Purpose: Make sure that noncritical genes are consistent

S-SiNeC: Edge insertion (A2)

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Reference network

vs vtvi

L R

Purpose: Make sure that critical genes are left reachable

S-SiNeC: Edge insertion (A3)

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Reference network

vs vtvi

L R

Purpose: Make sure that critical genes are right reachable

S-SiNeC: Edge insertion

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L/R e1 e2 e3

1 X2 X X3 X X4 X X5 X X6 X X7 X8 X

S-SiNeC: Edge deletion (A4)

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Reference network

vs vtvi

L R

Purpose: Make sure that no detours exist around critical genes

Solve minimum cut between L &

R

Dataset

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• Reference networks are obtained by random edge shuffling at 5% to 40% mutation rates.

• 200 references per target network & per mutation rate.

Average distance to the true network

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Accuracy based on edge class

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vs vt

Hot

Cold

Running time results

22SiNeC > 1 hour per reference network.

Success rate on constraints

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Accuracy

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Functional Enrichment of the Pathway

Last Remarks

• Constructing very large signaling networks from RNAi data is possible in practical running time.

• Both SiNeC and S-SiNeC are robust to errors in reference network.

• We recommend– S-SiNeC for very large OR dense networks.– SiNeC otherwise.

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Acknowledgements

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CCF - 0829867 IIS - 0845439260429