3-D visualization of a gene regulatory network: stochastic search for layouts

In recent years, base sequences have been increasingly unscrambled through attempts represented by the human genome project. Accordingly, the estimation of the genetic network has been accelerated. However, no definitive method has become available for drawing a large effective graph. This paper proposes a method which allows for coping with an increase in the number of nodes by laying out genes on planes of several layers and then overlapping these planes. This layout involves an optimization problem which requires maximizing the fitness function. To demonstrate the effectiveness of our approach, we show some graphs using actual data on 82 genes, 552 genes, and artificial data modeled from a scale-free network of 1,000 genes. We also describe how to lay out nodes by means of stochastic searches, e.g., stochastic hill-climbing and simulating annealing methods. The experimental results show the superiority and usefulness of stochastic searches in comparison with the simple random search.