Hypothesizing about signaling networks

Nam Tran, Chitta Baral

Research output: Contribution to journalArticlepeer-review

22 Scopus citations


The current knowledge about signaling networks is largely incomplete. Thus biologists constantly need to revise or extend existing knowledge. The revision and/or extension is first formulated as theoretical hypotheses, then verified experimentally. Many computer-aided systems have been developed to assist biologists in undertaking this challenge. The majority of the systems help in finding "patterns" in data and leave the reasoning to biologists. A few systems have tried to automate the reasoning process of hypothesis formation. These systems generate hypotheses from a knowledge base and given observations. A main drawback of these knowledge-based systems is the knowledge representation formalism they use. These formalisms are mostly monotonic and are now known to be not quite suitable for knowledge representation, especially in dealing with the inherently incomplete knowledge about signaling networks. We propose an action language based framework for hypothesis formation for signaling networks. We show that the hypothesis formation problem can be translated into an abduction problem. This translation facilitates the complexity analysis and an efficient implementation of our system. We illustrate the applicability of our system with an example of hypothesis formation in the signaling network of the p53 protein.

Original languageEnglish (US)
Pages (from-to)253-274
Number of pages22
JournalJournal of Applied Logic
Issue number3
StatePublished - Sep 2009


  • Abduction
  • Action languages
  • Hypothesis formation
  • Signaling networks

ASJC Scopus subject areas

  • Logic
  • Applied Mathematics


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