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Biological control systems are typically very complex, involving multiple, interacted control nodes that function at different time scales.
As shown in the figure below, the sink node's neighbors are all relay nodes that function in alternate clock cycles.
The proposed algorithm divides these nodes into two groups of relay nodes that function on alternate clock cycles, ensuring that network connectivity continues while network lifetime is also enhanced.
We identified and quantified a significant number of phosphorylated nodes that function in this pathway, including E1, E2, E3 ligases, proteasome components, and deubiquitinases (DUBs).
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The node with a large BC value is another important node that functions as a bottleneck in the network, even when the degree of node is low.
The network starts from pathway maps mostly in the "carbohydrate" (orange nodes) and "energy" (brown nodes) categories that function in catabolism.
The final density of the infected nodes that changes as functions of different model parameters.
Reasoning that topological features (e.g., clusters of highly inter-connected proteins) might help approaching structured and non-redundant understanding of molecular function, an algorithm was developed that prioritizes inclusion of proteins into the function nodes that best overlap protein clusters.
A further problem relates to the volunteer machines, or nodes, that allow Bitcoin to function.
Nodes, that have the same function as, for example, Wee1/Mik1 and SK (Cdc2/Cig1, Cdc2/Cig2, Cdc2/Puc1) are joined together in a single node (see Figure 1), as it does not make a difference in the specific mathematical model dynamics considered here.
Module detection in an eQTL network can be formulated as an optimization problem: searching for a set of m+n nodes that maximizes the objective function Q m, n, k) = k/(m×n), where m is the number of eQTLs, n is the number of target genes and k is the number of edges between them.
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