Comparative visualization of IG of <i>A. mellifera</i> genome with intergenic regions of <i>D. melanogaster</i> genome.

<p>Octamer frequencies are depicted. The background frequency is determined by <i>D. melanogaster</i> genome.</p

Address of length 2 (a) and generator (b).

<p>Note that (<b>b</b>) is a mirror image of the generator because this is a stamped image.</p

Procedure for formation of trimer code from dimer code.

<p>(<b>a</b>) The dimer code is assumed. (<b>b</b>) The generator is stamped on a cell. (<b>c</b>) The generator is rotated around an edge and stamped again. (<b>d</b>) Repeating the rotation and stamping for all cells yields the trimer code.</p

Relationship between addresses and cells (a) and affine transformations (b).

<p>Points A, B, and C are vertexes of a regular triangle and positioned at , , and , respectively. Points D, E, and F are the midpoints of the three boundaries, respectively. Each affine transformation moves triangle ABC into an inner triangle indicated in (<b>b</b>).</p

Exhibition of TGCs in a science outreach event.

<p>The mobile sculpture is composed along the tree of life.</p

Estimating optimal sparseness of developmental gene networks using a semi-quantitative model

<div><p>To estimate gene regulatory networks, it is important that we know the number of connections, or sparseness of the networks. It can be expected that the robustness to perturbations is one of the factors determining the sparseness. We reconstruct a semi-quantitative model of gene networks from gene expression data in embryonic development and detect the optimal sparseness against perturbations. The dense networks are robust to connection-removal perturbation, whereas the sparse networks are robust to misexpression perturbation. We show that there is an optimal sparseness that serves as a trade-off between these perturbations, in agreement with the optimal result of validation for testing data. These results suggest that the robustness to the two types of perturbations determines the sparseness of gene networks.</p></div