Application of abductive ILP to learning metabolic network inhibition from temporal data

• DOI: 10.1007/s10994-006-8988-x
• Publication Date: 2006-06-30T15:51:39Z
• Authors: Alireza Tamaddoni...; Raphael Chaleil; Antonis Kakas; Stephen Muggleton
• Keywords: Metabolic network; Inductive logic programming; Disjoint sets; Metabolic pathway; Representation

In this paper we use a logic-based representation and a combination of Abduc- tion and Induction to model inhibition in metabolic networks. In general, the integration of abduction and induction is required when the following two conditions hold. Firstly, the given background knowledge is incomplete. Secondly, the problem must require the learning of general rules in the circumstance in which the hypothesis language is disjoint from the observation language. Both these conditions hold in the application considered in this paper. Inhibition is very important from the therapeutic point of view since many substances de- signed to be used as drugs can have an inhibitory effect on other enzymes. Any system able to predict the inhibitory effect of substances on the metabolic network would therefore be very useful in assessing the potential harmful side-effects of drugs. In modelling the phenomenon of inhibition in metabolic networks, background knowledge is used which describes the net- work topology and functional classes of inhibitors and enzymes. This background knowledge, which represents the present state of understanding, is incomplete. In order to overcome this incompleteness hypotheses are considered which consist of a mixture of specific inhibitions of enzymes (ground facts) together with general (non-ground) rules which predict classes of enzymes likely to be inhibited by the toxin. The foreground examples are derived from in vivo experiments involving NMR analysis of time-varying metabolite concentrations in