Hypothesizing about Causal Networks with Positive and Negative Effects by Meta-level Abduction

Meta-level abduction discovers missing links and unknown nodes from incomplete networks to complete paths for observations. In this work, we extend applicability of meta-level abduction to deal with networks containing both positive and negative causal effects. Such networks appear in many domains including biology, in which inhibitory effects are important in signaling and metabolic pathways. Reasoning in networks with inhibition is inevitably nonmonotonic, and involves default assumptions in abduction. We show that meta-level abduction can consistently produce both positive and negative causal relations as well as invented nodes. Case studies of meta-level abduction are presented in p53 signaling networks, in which causal rules are abduced to suppress a tumor with a new protein and to stop DNA synthesis when damage is occurred.

[1]  Ivan Bratko,et al.  GenePath: a system for automated construction of genetic networks from mutant data , 2003, Bioinform..

[2]  Stephen Muggleton,et al.  Elements of computational systems biology , 2010 .

[3]  Oliver Ray,et al.  Logic-Based Steady-State Analysis and Revision of Metabolic Networks with Inhibition , 2010, 2010 International Conference on Complex, Intelligent and Software Intensive Systems.

[4]  Katsumi Inoue,et al.  Induction as Consequence Finding , 2004, Machine Learning.

[5]  Tatsuya Akutsu,et al.  Completing Networks Using Observed Data , 2009, ALT.

[6]  Stephen Muggleton,et al.  Theory Completion Using Inverse Entailment , 2000, ILP.

[7]  Ron Shamir,et al.  Chain functions and scoring functions in genetic networks , 2003, ISMB.

[8]  David Poole,et al.  A Logical Framework for Default Reasoning , 1988, Artif. Intell..

[9]  Katsumi Inoue,et al.  Discovering Rules by Meta-level Abduction , 2009, ILP.

[10]  Katsumi Inoue,et al.  A Consequence Finding Approach for Full Clausal Abduction , 2007, Discovery Science.

[11]  Katsumi Inoue,et al.  Consequence finding and computing answers with defaults , 2006, Journal of Intelligent Information Systems.

[12]  Edward R. Dougherty,et al.  Probabilistic Boolean networks: a rule-based uncertainty model for gene regulatory networks , 2002, Bioinform..

[13]  Ramón P. Otero,et al.  Induction of the Indirect Effects of Actions by Monotonic Methods , 2005, ILP.

[14]  Katsumi Inoue,et al.  SOLAR: A Consequence Finding System for Advanced Reasoning , 2003, TABLEAUX.

[15]  Torsten Schaub,et al.  Metabolic Network Expansion with Answer Set Programming , 2009, ICLP.

[16]  Katsumi Inoue,et al.  Minimal Answer Computation and SOL , 2002, JELIA.

[17]  Katsumi Inoue,et al.  Linear Resolution for Consequence Finding , 1992, Artif. Intell..

[18]  Stephen Moyle,et al.  Using Theory Completion to Learn a Robot Navigation Control Program , 2002, ILP.

[19]  C. Prives,et al.  The p53 pathway , 1999, The Journal of pathology.

[20]  J. Pearl.  Causality: Models, Reasoning and Inference , 2000 .

[21]  Roy Dyckhoff.  Automated Reasoning with Analytic Tableaux and Related Methods , 2000, Lecture Notes in Computer Science.

[22]  Katsumi Inoue,et al.  SOLAR: An automated deduction system for consequence finding , 2010, AI Commun..

[23]  Katsumi Inoue,et al.  In defense of PDDL axioms , 2003, Artif. Intell..

[24]  Stephen Muggleton,et al.  Machine Invention of First Order Predicates by Inverting Resolution , 1988, ML.

[25]  Krzysztof R. Apt,et al.  Logic Programming , 1990, Handbook of Theoretical Computer Science, Volume B: Formal Models and Sematics.

[26]  Stephen Muggleton,et al.  Application of abductive ILP to learning metabolic network inhibition from temporal data , 2006, Machine Learning.

[27]  Gerhard Schurz,et al.  Patterns of abduction , 2008, Synthese.

[28]  Frank Wolter,et al.  Semi-qualitative Reasoning about Distances: A Preliminary Report , 2000, JELIA.

[29]  Katsumi Inoue,et al.  Integrating Abduction and Induction in Biological Inference Using CF‐Induction , 2010 .

[30]  Katsumi Inoue,et al.  Inducing Causal Laws by Regular Inference , 2005, ILP.

[31]  Raymond Reiter,et al.  A Logic for Default Reasoning , 1987, Artif. Intell..

[32]  Chitta Baral,et al.  Hypothesizing about signaling networks , 2009, J. Appl. Log..

[33]  Stephen Muggleton,et al.  Developing a Logical Model of Yeast Metabolism , 2001, Electron. Trans. Artif. Intell..

[34]  Christopher H. Bryant,et al.  Functional genomic hypothesis generation and experimentation by a robot scientist , 2004, Nature.