Hypothesizing about Causal Networks with Positive and Negative Effects by Meta-level Abduction

Meta-level abduction discovers missing links and unknown nodes from incomplete networks to complete paths for observations. In this work, we extend applicability of meta-level abduction to deal with networks containing both positive and negative causal effects. Such networks appear in many domains including biology, in which inhibitory effects are important in signaling and metabolic pathways. Reasoning in networks with inhibition is inevitably nonmonotonic, and involves default assumptions in abduction. We show that meta-level abduction can consistently produce both positive and negative causal relations as well as invented nodes. Case studies of meta-level abduction are presented in p53 signaling networks, in which causal rules are abduced to suppress a tumor with a new protein and to stop DNA synthesis when damage is occurred.