The computational complexity of propositional STRIPS planning
Artificial Intelligence
The small-world phenomenon: an algorithmic perspective
STOC '00 Proceedings of the thirty-second annual ACM symposium on Theory of computing
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
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Recent small-world studies of the global structure of metabolic networks have been based on the shortest-path distance. In this paper, we propose new distance measures that are based on the structure of feasible metabolic pathways between metabolites. We argue that these distances capture the complexity of the underlying biochemical processes more accurately than the shortest-path distance. To test our approach in practice, we calculated our distances and shortest-path distances in two microbial organisms, S. cerevisiae and E. coli. The results show that metabolite interconversion is significantly more complex than was suggested in previous small-world studies. We also studied the effect of reaction removals (gene knock-outs) on the connectivity of the S. cerevisiae network and found out that the network is not particularly robust against such mutations.