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Inference of Gene Relations from Microarray Data by Abduction

Inference of Gene Relations from Microarray Data by Abduction. Irene Papatheodorou & Marek Sergot Imperial College, London UK. Gene Regulation & Microarrays Abductive Logic Programming Proof Procedure Model of Gene Interactions Applications & Tests Evaluation & Further Work. Outline.

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Inference of Gene Relations from Microarray Data by Abduction

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  1. Inference of Gene Relations from Microarray Data by Abduction Irene Papatheodorou & Marek Sergot Imperial College, London UK

  2. Gene Regulation & Microarrays Abductive Logic Programming Proof Procedure Model of Gene Interactions Applications & Tests Evaluation & Further Work Outline

  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. Mycobacterium tuberculosis experiments from CMMI Genomic Information-Background Knowledge Gene Relations- inhibits/induces Inference Method: Abduction Expression Data to Gene Relations

  6. Deduction model of howgenes work (in general) Organism A gene X regulates gene Ygene U inhibits gene V: observedgeneexpression + Infer the effect from rules

  7. Abduction model of howgenes work (in general) Organism A gene X regulates gene Ygene U inhibits gene V: observedgeneexpression + Inference from effect to cause

  8. 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 Abductive Inference

  9. Given an abductive logic theory (P, A, I), an abductive explanation for a query Q is a set Δ of ground abducible atoms on the predicates A such that: P ∪ Δ |=LPQ P ∪ Δ is consistent P ∪ Δ |=LPI. |=LP denotes some standard logical entailment relation of logic programming Abductive Explanation

  10. 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) Kakas-Mancarella Procedure

  11. Rules & Integrity Constraints of Gene Interactions Observables: increases_expression(Expt, Gene) reduces_expression(Expt, Gene) Abducibles: induces(GeneA, GeneB) inhibits(GeneA, GeneB) Gene Interaction Model

  12. 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

  13. Concept of gene interaction increases_expression(Expt, X) ← knocks_out(Expt, G), inhibits(G,X). The Model

  14. 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). The Model: Exceptions

  15. 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 Rules of Gene Interaction

  16. 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 Rules of Gene Interaction

  17. “Related Genes” & “Intermediate Genes” Focus search on different sets of genes Transcription factors Similar Function The Parameters

  18. 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). Integrity Constraints

  19. 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 M.tuberculosis: 1 Observation

  20. Observation: reduces_expression(sigH, ‘Rv2710’) Hypotheses: Hyp = [induces(‘Rv3223c’, ‘Rv2710’)] Hyp = [induces(‘Rv3223c’, ‘Rv1221’), induces(‘Rv1221’, ‘Rv2710’)] M.tuberculosis: 2 Hypotheses

  21. M.tuberculosis: Regulators

  22. General Method for Microarray Analysis Simple and Flexible Model Enables comparison of experiments Reduces Time of Analysis Evaluation

  23. Integrate output with pathway information Investigate different methods of formulating the problem Improve Performance of Abductive Interpreters Future Work Directions

  24. Gene Regulation & Microarrays Visualising Experiments Abductive Model for Gene Interactions Applications Future Work Summary

  25. Questions? Irene Papatheodorou ivp@doc.ic.ac.uk

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