title: A knowledge based approach for representing and reasoning about signaling networks.
abstract: BACKGROUND
  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.
  
  
  RESULTS
  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.
  
  
  BACKGROUND
  The system is available on the Web at http://www.public.asu.edu/~cbaral/biosignet
authors.affiliation: Department of Computer Science and Engineering, Ira A. Fulton School of Engineering, Arizona State University, Tempe, AZ 85281, USA. baral@asu.edu
authors.forename: C
authors.initials: C
authors.lastname: Baral
authors.forename: K
authors.initials: K
authors.lastname: Chancellor
authors.forename: N
authors.initials: N
authors.lastname: Tran
authors.forename: N L
authors.initials: NL
authors.lastname: Tran
authors.forename: A
authors.initials: A
authors.lastname: Joy
authors.forename: M
authors.initials: M
authors.lastname: Berens
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D015870:Gene Expression / Q000502:physiology*; D020869:Gene Expression Profiling / Q000379:methods*; D008954:Models, Biological; D020543:Proteome / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology*
chemical_list: D020543:Proteome