Generation of genetic networks from a small number of gene expression patterns under the Boolean network model.
There are lots of work for inferring genetic network architectures from state transition tables which correspond to time series of gene expression patterns, using the Boolean network model. Results of those computational experiments suggested that a small number of state transition (INPUT/OUTPUT) pairs are sufficient in order to infer the original Boolean network correctly. Tatsuya AKUTSU, Satoru MIYANO and Satoru KUHARA gave a mathematical proof for this. So there is possibility to devise an algorithm to generate all consistent genetic networks from a small number of gene expression patterns under the Boolean network model.
