Inference of Gene Relations from Microarray Data by Abduction

1
Inference of Gene Relations from Microarray Data
by Abduction

• Irene Papatheodorou Marek Sergot
• Imperial College, London UK

2
Outline

• Gene Regulation Microarrays
• Abductive Logic Programming
• Proof Procedure
• Model of Gene Interactions
• Applications Tests
• Evaluation Further Work

3
Gene Regulation
Cell Response
Gene Regulation A B C
Gene Expression DNA/gene mRNA Protein
External Stimulus
Microarrays measure gene expression
4
Microarray Experiment
Measures levels of gene expression
A
A
B
B
Experiment gene mutation/environmental stress
5
(No Transcript)
6
Expression Data to Gene Relations

• Mycobacterium tuberculosis experiments from CMMI
• Genomic Information-Background Knowledge
• Gene Relations- inhibits/induces
• Inference Method Abduction

7
Deduction
model of howgenes work (in general)
Organism A gene X regulates gene Ygene U
inhibits gene V
observedgeneexpression

Infer the effect from rules
8
Abduction
model of howgenes work (in general)
Organism A gene X regulates gene Ygene U
inhibits gene V
observedgeneexpression

Inference from effect to cause
9
Abductive Inference

• Theory represented by (P, A, I)
• P is a logic program
• A is a set of abducible predicates-Do not occur
  in head of any clause in P
• I Integrity Constraints, logic rules

10
Abductive Explanation

• Given an abductive logic theory (P, A, I), an
  abductive explanation for a query Q is a set ?
  of ground abducible atoms on the predicates A
  such that
• P ? ? LP Q
• P ? ? is consistent
• P ? ? LP I.
• LP denotes some standard logical entailment
  relation of logic programming

11
Kakas-Mancarella Procedure

• Extension of basic resolution used in SLD SLDNF
  for ordinary logic programs
• Assumptions Negative Literals, Abducibles
• Abductive Derivation Adds abducible atoms
  encountered to assumptions in hypothesis
• Consistency Derivation Checks consistency of
  hypothesis.
• Implementation Alpha (R. Craven)

12
Gene Interaction Model

• Rules Integrity Constraints of Gene
  Interactions
• Observables
• increases_expression(Expt, Gene)
• reduces_expression(Expt, Gene)
• Abducibles
• induces(GeneA, GeneB)
• inhibits(GeneA, GeneB)

13
The Rules (Summary)
Increased expression in expt E
Knocked out in expt E
INHIBITS
GENE 1
GENE 2
Unless GENE 2 affected by another gene or GENE
2 affected by environmental stress
2 Parameters
Recursive rules
14
The Model

• Concept of gene interaction
• increases_expression(Expt, X) ?
• knocks_out(Expt, G),
• inhibits(G,X).

15
The Model Exceptions

• Top-level Base case rule
• increases_expression(Expt, X) ?
• knocks_out(Expt, G),
• inhibits(G,X),
• not affected_by_other_gene(Expt, G, X),
• not affected_by_EnvFactor(Expt, X).

16
Rules of Gene Interaction

• Top-level recursive rule
• increases_expression(Expt, X) ?
• knocks_out(Expt, G),
• candidate_gene(Expt,G1,G),
• reduces_expression(Expt,G1),
• inhibits(G,X),
• not affected_by_EnvFactor(Expt, X).
• Parameter candidate_gene/3

17
Rules of Gene Interaction

• affected_by_other_gene(Expt,G,X) ?
• increases_expression(Expt,Gx),
• Gx ? X, Gx ? G,
• related_genes(Gx, G),
• induces(Gx, X).
• Parameter related_genes/2

18
The Parameters

• Related Genes Intermediate Genes
• Focus search on different sets of genes
• Transcription factors
• Similar Function

19
Integrity Constraints

• Self-consistency
• False induces(G1,G2), inhibits(G1,G2).
• Consistency with prior knowledge
• False induces(a,G).
• False induces(G1,X), induces(G2,X),
• same_operon(G1,G2).
• Experimental Consistency
• False candidate_gene(E,G1,G2),
  mutates(E,G2), not affects(E,G1).

20
M.tuberculosis 1 Observation

• Observation
• increases_expression(hspR, Rv0350)
• Hypothesis
• Hyp inhibits(Rv0353, Rv0350)
• Rv0353 is mutated in hspR
• Rv0350 is not affected by Environmental Factor
• Rv0350 is not affected by other gene

21
M.tuberculosis 2 Hypotheses

• Observation
• reduces_expression(sigH, Rv2710)
• Hypotheses
• Hyp induces(Rv3223c, Rv2710)
• Hyp induces(Rv3223c, Rv1221),
• induces(Rv1221, Rv2710)

22
M.tuberculosis Regulators
23
Evaluation

• General Method for Microarray Analysis
• Simple and Flexible Model
• Enables comparison of experiments
• Reduces Time of Analysis

24
Future Work Directions

• Integrate output with pathway information
• Investigate different methods of formulating the
  problem
• Improve Performance of Abductive Interpreters

25
Summary

• Gene Regulation Microarrays
• Visualising Experiments
• Abductive Model for Gene Interactions
• Applications
• Future Work

26
Questions?

• Irene Papatheodorou
• ivp_at_doc.ic.ac.uk