A knowledge based approach for representing and reasoning about signaling networks
Chitta Baral, Karen Chancellor, Nam Tran, Mike Berens and Nhan Tran
Abstract
In this paper we propose to use recent developments in knowledge
representation languages and reasoning methodologies for representing and
reasoning about signaling networks. Our approach is different from most other
qualitative systems biology approaches in that it is based on reasoning (or
inferencing) rather than simulation. Some of the advantages of our approach
are: we can use recent advances in reasoning with incomplete and partial
information to deal with gaps in signal network knowledge; and can perform
various kinds of reasoning such as planning, hypothetical reasoning and
explaining observations. Using our approach we have developed the system
BioSigNet-RR for representation and reasoning about signaling networks. We use
a NFkappaB related signaling pathway to illustrate the kinds of reasoning and
representation that our system can currently do.