Uncovering Signaling Transduction Networks from PPI network by Inductive Logic Programming Woo-Hyuk Jang

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 inaccuracy. –Probabilistic model cannot provide biological explanation of the results.

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 STP PPI Network 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 Property occupation Pos. in brothers personality...

Case 1 (Marriage) Mode Declarations Male1 Property 10 억 Occupation 의사 Pos. in. brothers Youngest PersonalityVery good Male2 Property 100 억 Occupation 사업가 Pos. in. brothers Eldest Personalitygood Male3 Property0 Occupation 의사 Pos. in. brothers Youngest PersonalityVery bad Male4 Property0 Occupation 없음 Pos. in. brothers Eldest PersonalityVery good Female1 Property 5억5억 Occupation 의사 Pos. in. brothers Youngest PersonalityVery good Female2 Property 1억1억 Occupation 학생 Pos. in. brothers middle PersonalityVery good Female3 Property 10 억 Occupation 의사 Pos. in. brothers Youngest PersonalityVery bad Female4 Property0 Occupation 없음 Pos. in. brothers Middle PersonalityVery good

Background Knowledge

Positive & Negative Example Positive Example Person Male1Female1 Male2Female2 Male3Female3 Male4Female4 Negative Example Person Male1Female4 Male2Female3 Male3Female2 Male4Female1

Case 1 (Marriage) Mode Declarations Male1 Property 10 억 Occupation 의사 Pos. in. brothers Youngest PersonalityVery good Male2 Property 100 억 Occupation 사업가 Pos. in. brothers Eldest Personalitygood Male3 Property0 Occupation 의사 Pos. in. brothers Youngest PersonalityVery bad Male4 Property0 Occupation 없음 Pos. in. brothers Eldest PersonalityVery good Female1 Property 5억5억 Occupation 의사 Pos. in. brothers Youngest PersonalityVery good Female2 Property 1억1억 Occupation 학생 Pos. in. brothers middle PersonalityVery good Female3 Property 10 억 Occupation 의사 Pos. in. brothers Youngest PersonalityVery bad Female4 Property0 Occupation 없음 Pos. in. brothers Middle PersonalityVery good

Approach Again ILP STP PPI Network PreSPI Functional Patterns Corrects True Negative, False Positive path Features Reference Induced Rules segments evaluation

ILP modeling on STP We can rewrite STP as a sequence of protein pair STE2/3Gpa1Ste4/18Cdc42Ste20 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 ???