Biology – Quantitative Biology – Molecular Networks
Scientific paper
2005-02-13
Biology
Quantitative Biology
Molecular Networks
13 pages, 9 figures
Scientific paper
10.1103/PhysRevE.72.016123
Many growing networks possess accelerating statistics where the number of links added with each new node is an increasing function of network size so the total number of links increases faster than linearly with network size. In particular, biological networks can display a quadratic growth in regulator number with genome size even while remaining sparsely connected. These features are mutually incompatible in standard treatments of network theory which typically require that every new network node possesses at least one connection. To model sparsely connected networks, we generalize existing approaches and add each new node with a probabilistic number of links to generate either accelerating, hyper-accelerating, or even decelerating network statistics in different regimes. Under preferential attachment for example, slowly accelerating networks display stationary scale-free statistics relatively independent of network size while more rapidly accelerating networks display a transition from scale-free to exponential statistics with network growth. Such transitions explain, for instance, the evolutionary record of single-celled organisms which display strict size and complexity limits.
Gagen Michael J.
Mattick John S.
No associations
LandOfFree
Accelerating, hyper-accelerating, and decelerating probabilistic networks does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Accelerating, hyper-accelerating, and decelerating probabilistic networks, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Accelerating, hyper-accelerating, and decelerating probabilistic networks will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-674441