Authors:
Gabriel Synnaeve
1
;
Katsumi Inoue
2
;
Andrei Doncescu
3
;
Hidetomo Nabeshima
4
;
Yoshitaka Kameya
5
;
Masakazu Ishihata
5
and
Taisuke Sato
5
Affiliations:
1
E-Motion Team at INRIA, France
;
2
National Institute of Informatics, Japan
;
3
LAAS-CNRS, France
;
4
University of Yamanashi, Japan
;
5
Tokyo Institute of Technology, Japan
Keyword(s):
Systems biology, Discretization, Metabolic pathways, Inductive logic programming, Abduction.
Related
Ontology
Subjects/Areas/Topics:
Algorithms and Software Tools
;
Bioinformatics
;
Biomedical Engineering
;
Data Mining and Machine Learning
;
Model Design and Evaluation
;
Pattern Recognition, Clustering and Classification
;
Systems Biology
Abstract:
This paper presents a method for enabling the relational learning or inductive logic programming (ILP) framework
to deal with quantitative information from experimental data in systems biology. The study of systems
biology through ILP aims at improving the understanding of the physiological state of the cell and the interpretation
of the interactions between metabolites and signaling networks. A logical model of the glycolysis and
pentose phosphate pathways of E. Coli is proposed to support our method description. We explain our original
approach to building a symbolic model applied to kinetics based on Michaelis-Menten equation, starting with
the discretization of the changes in concentration of some of the metabolites over time into relevant levels. We
can then use them in our ILP-based model. Logical formulae on concentrations of some metabolites, which
could not be measured during the dynamic state, are produced through logical abduction. Finally, as this results
in a large numbe
r of hypotheses, they are ranked with an expectation maximization algorithm working
on binary decision diagrams.
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