Uncovering Signaling Transduction Networks from PPI network by Inductive Logic

Programming

Woo-Hyuk Jang2009. 3. 20

Contents

• Introduction

• Method– ILP system (ALEPH)– ILP modeling example (Marriage Case)

• ILP modeling of STP

• Challenges and Future Work

Introduction

• Most of Signaling Transduction Network (STN) prediction methods follow the sequences, 1) making integrative PPIs, 2) Finding rules from STN, 3) discovering STN components from PPIs.

• In addition, these methods generally adopt probabilistic model in each sequence.

• However,– Accumulation of even small noise may lead to big prediction inac

curacy.– Probabilistic model cannot provide biological explanation of the re

sults.

Related Work

• Steffen, et. al. (2002)– Integrating PPI and microarray data– Netsearch algorithm

• Yin Liu and Hongyu Zhao (2004)– Ordering proteins when all components of STN are already known.

• Jacob Scott, et. al. (2006)– A variant of the color coding algorithm – Yeast PPI

• Gurkan Bebek and Jiong Yang (2007)– Extract functional patterns from STP– PathFinder

• Xing-Ming Zhao, et. al. (2008)– PPI + gene expression profile– Integer linear programming

New Approach

ILP STPPPINetwork

PreSPI

Functional Patterns

Corrects True Negative, False Positive path

Features

Reference

Induced Rules

Method

• Inductive Logic Programming (ILP)– Programs that “generalize”

– Programs that follow the Specific General idea

Molecular structure of toxic and non-toxic chemicals, other props …

Chemical is toxic if it has a ring connected to… and a C atom in… …

Method

• Inductive Logic Programming (ILP)– A powerful representation language

• Express complex relationships easily

– Easy to provide background information• Including other analysis methods like regression etc

– We can easily integrate diverse features and their relations that may affect to PPI in STP

A Learning Engine for Proposing Hypotheses(ALEPH)

• ILP system that follows a very simple procedure that can be described in 4 steps:– 1. Select example– 2. Build most-specific-clause– 3. Search– 4. Remove redundant

• Background knowledge (*.b), Positive example (*.f), Negative example (*.n)

ILP modeling example Case 1 (Marriage)

• There are some features that have driven this couple to fall

in love

Propertyoccupation

Pos. in brothers personality

. . .

Case 1 (Marriage)

• Mode DeclarationsMale1

Property 10억

Occupation 의사

Pos. in. brothers

Youngest

Personality Very good

Male2

Property 100억

Occupation 사업가

Pos. in. brothers

Eldest

Personality good

Male3

Property 0

Occupation 의사

Pos. in. brothers

Youngest

Personality Very bad

Male4

Property 0

Occupation 없음

Pos. in. brothers

Eldest

Personality Very good

Female1

Property 5억

Occupation 의사

Pos. in. brothers

Youngest

Personality Very good

Female2

Property 1억

Occupation 학생

Pos. in. brothers

middle

Personality Very good

Female3

Property 10억

Occupation 의사

Pos. in. brothers

Youngest

Personality Very bad

Female4

Property 0

Occupation 없음

Pos. in. brothers

Middle

Personality Very good

Background Knowledge

Positive & Negative Example

• Positive Example

Person Person

Male1 Female1

Male2 Female2

Male3 Female3

Male4 Female4

• Negative Example

Person Person

Male1 Female4

Male2 Female3

Male3 Female2

Male4 Female1

Case 1 (Marriage)

• Mode DeclarationsMale1

Property 10억

Occupation 의사

Pos. in. brothers

Youngest

Personality Very good

Male2

Property 100억

Occupation 사업가

Pos. in. brothers

Eldest

Personality good

Male3

Property 0

Occupation 의사

Pos. in. brothers

Youngest

Personality Very bad

Male4

Property 0

Occupation 없음

Pos. in. brothers

Eldest

Personality Very good

Female1

Property 5억

Occupation 의사

Pos. in. brothers

Youngest

Personality Very good

Female2

Property 1억

Occupation 학생

Pos. in. brothers

middle

Personality Very good

Female3

Property 10억

Occupation 의사

Pos. in. brothers

Youngest

Personality Very bad

Female4

Property 0

Occupation 없음

Pos. in. brothers

Middle

Personality Very good

Approach Again

ILP STPPPINetwork

PreSPI

Functional Patterns

Corrects True Negative, False Positive path

Features

Reference

Induced Rules

segmentsevaluation

ILP modeling on STP

• We can rewrite STP as a sequence of protein pair

STE2/3 Gpa1 Ste4/18 Cdc42 Ste20

Pheromone response in MAPK STP

Interaction(STE2/3, GPA1).

Interaction(GPA1, STE4/18).

Interaction(STE4/18, CDC42).

Couple(person, person)

W_property(male1,10)Go_of(STE2/3, GOXXXXX).

GO_of(GPA1, GOXXXXX).

GO_of(STE4/18, GOXXXXX).

Feature Selection

• T. P. Nguyen and T. B. Ho, “Discovering Signal Transduction Networks Using Signaling Domain-Domain Interaction”, 2006, Genome Informatics.

Challenges and Future Work

• Refined feature selection

• Build parser for each biological DB

• Mode declaration– Build determination predicates

• Evaluation problem– Induced rule from MAPK extracting segments from PPI

compared to MAPK ???