To give relative network efficiency, this quantity NE is divided by the initial network efficiency.
Nodes that are deregulated by network damage may not have a critical impact on other nodes (or, notably, on the output nodes of signal transduction networks, such as cell death), or may only regulate other nodes that are also immediately deregulated by the initial network damage.
The final design can be obtained in a few iterations by adjusting the initial network.
Under the above model, we conducted computational experiments by DPLSQ-TV in which the initial network G was modified by randomly adding k0 edges and deleting h0 edges per node, resulting in G1, G2, and G3; additionally, we also conducted DPLSQ-HS experiments in which the initial network G was modified by randomly adding k0 edges per node, using the default values of k0 = h0 = 1.
Hence, these results suggest that DPLSQ-TV and DPLSQ-HS can correctly identify the change time points if the error levels are not very large and that it can complete the initial network by modifying the edges with relatively good accuracy if the observation error is small.
Starting from a randomly initialized PNFL model our reconstruction approach (Fig. 6) proceeds via four steps: (1) The topology and parametrization of the initial network are modified by the application of moves.
Then, the problem is defined as follows: given an initial network G V, E) consisting of n genes, N time series datasets, each of which consists of m time points for n genes and the positive integers h, k, and B, infer B change points (i.e., c1, c2,…, c B ) and complete the initial network G V, E) by adding k edges and deleting h edges in total such that the total least-squares error is minimized.
The Boolean functions of the initial network were improved iteratively by the comparison of the simulation runs to the experimental data and by exploitation of expert knowledge.
We have expanded the initial network model through "guilt by association" based on co-location (operons), co-regulation and co-evolution information that we can calculate through comparative genomic analyses.
The initial network of 32 conformational states, generated by the SwarmDock server, is depicted in Figure 2.
Then, an initial sink node X will be followed directly by all users that had a directed path towards X in the initial network.
