Reverse Engineering for a Dynamic Model of the Drosophila Circadia Oscillator

In the field of bioinformatics, biochemical networks have been reconstructed in computer to understand their dynamic features. A problem is how to estimate the values of many kinetic parameters that are hard to measure experimentally. In this study, we have proposed a novel parameter estimation method based on genetic algorithms (GAs) and applied it to the Drosophila circadian oscillator. We defined two evaluation functions that represent two features of the dynamic model. To optimize the two functions simultaneously, we proposed the survival ratio GA, which has a gene’s lifetime as a new concept, that is, the population holds previous search histories. By alternating between two evaluation functions every generation, the population holds both previously evaluated genes and children inherit both properties. In the two-evaluation problem of the Drosophila circadian oscillation system, the survival ratio GA exhibited a considerably better optimizing performance than traditional GA methods.