Topology of biological networks and reliability of information processing Export

@article{citeulike:453334, abstract = {10.1073/pnas.0509132102 Survival of living cells and organisms is largely based on highly reliable function of their regulatory networks. However, the elements of biological networks, e.g., regulatory genes in genetic networks or neurons in the nervous system, are far from being reliable dynamical elements. How can networks of unreliable elements perform reliably? We here address this question in networks of autonomous noisy elements with fluctuating timing and study the conditions for an overall system behavior being reproducible in the presence of such noise. We find a clear distinction between reliable and unreliable dynamical attractors. In the reliable case, synchrony is sustained in the network, whereas in the unreliable scenario, fluctuating timing of single elements can gradually desynchronize the system, leading to nonreproducible behavior. The likelihood of reliable dynamical attractors strongly depends on the underlying topology of a network. Comparing with the observed architectures of gene regulation networks, we find that those 3-node subgraphs that allow for reliable dynamics are also those that are more abundant in nature, suggesting that specific topologies of regulatory networks may provide a selective advantage in evolution through their resistance against noise.}, author = {Klemm, Konstantin and Bornholdt, Stefan}, citeulike-article-id = {453334}, citeulike-linkout-0 = {http://dx.doi.org/10.1073/pnas.0509132102}, citeulike-linkout-1 = {http://www.pnas.org/content/102/51/18414.abstract}, citeulike-linkout-2 = {http://www.pnas.org/content/102/51/18414.full.pdf}, citeulike-linkout-3 = {http://www.pnas.org/cgi/content/abstract/102/51/18414}, citeulike-linkout-4 = {http://view.ncbi.nlm.nih.gov/pubmed/16339314}, citeulike-linkout-5 = {http://www.hubmed.org/display.cgi?uids=16339314}, day = {20}, doi = {10.1073/pnas.0509132102}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, keywords = {attractor, booleannets, dynamics, networks, robustness, sysbio}, month = {December}, number = {51}, pages = {18414--18419}, posted-at = {2005-12-30 15:20:08}, priority = {2}, title = {Topology of biological networks and reliability of information processing}, url = {http://dx.doi.org/10.1073/pnas.0509132102}, volume = {102}, year = {2005} }

TY - JOUR ID - citeulike:453334 L3 - citeulike-article-id:453334 N2 - 10.1073/pnas.0509132102 Survival of living cells and organisms is largely based on highly reliable function of their regulatory networks. However, the elements of biological networks, e.g., regulatory genes in genetic networks or neurons in the nervous system, are far from being reliable dynamical elements. How can networks of unreliable elements perform reliably? We here address this question in networks of autonomous noisy elements with fluctuating timing and study the conditions for an overall system behavior being reproducible in the presence of such noise. We find a clear distinction between reliable and unreliable dynamical attractors. In the reliable case, synchrony is sustained in the network, whereas in the unreliable scenario, fluctuating timing of single elements can gradually desynchronize the system, leading to nonreproducible behavior. The likelihood of reliable dynamical attractors strongly depends on the underlying topology of a network. Comparing with the observed architectures of gene regulation networks, we find that those 3-node subgraphs that allow for reliable dynamics are also those that are more abundant in nature, suggesting that specific topologies of regulatory networks may provide a selective advantage in evolution through their resistance against noise. IS - 51 JF - Proceedings of the National Academy of Sciences of the United States of America EP - 18419 TI - Topology of biological networks and reliability of information processing VL - 102 SP - 18414 KW - attractor KW - booleannets KW - dynamics KW - networks KW - robustness KW - sysbio AU - Klemm, Konstantin AU - Bornholdt, Stefan PY - 2005/12/20/ UR - http://dx.doi.org/10.1073/pnas.0509132102 ER -