Biological networks/Bibliography: Difference between revisions
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==Books== | |||
* Ross J, Schreiber I, Vlad MO; with contributions from Arkin A, Oefner PJ, Zamboni N. (2006) [http://tinyurl.com/yeme67m ''Determination of Complex Reaction Mechanisms: Analysis of Chemical, Biological, and Genetic Networks.''] Oxford University Press: New York. ISBN 978-0-19-517868-5. | |||
**'''Excerpt:''' We have seen that computations can be achieved by chemical and biochemical reaction mechanisms, and have located computational functions in biological reaction systems. This identification helps in understanding functions and control in such systems. It also helps in suggesting new approaches to the determination of causal connectivities of reacting species, of reaction pathways, and reaction mechanisms, by exploring analogs of investigations in electronics, system analysis [citations here], multivariate statistics [citations here0], and other related disciplines. (From Chapter 4: Computations by Means of Macroscopic Chemical Kinetics.) | |||
==Book Chapters== | |||
==Journal articles== | |||
* Nitschke JR. (2009) [http://dx.doi.org/10.1038/462736a Systems chemistry: Molecular networks come of age.] ''Nature'' 462:736-738. | * Nitschke JR. (2009) [http://dx.doi.org/10.1038/462736a Systems chemistry: Molecular networks come of age.] ''Nature'' 462:736-738. | ||
**'''Excerpt:''' There are two main questions [systems chemists are asking]. The first is how the complex networks of molecules found on the prebiotic Earth might have crossed the threshold of life...The second question is how collections of molecules self-assemble into complex structures, and how secondary interactions between molecules and competition for molecular building blocks lead to complex behaviour within self-assembling systems. | **'''Excerpt:''' There are two main questions [systems chemists are asking]. The first is how the complex networks of molecules found on the prebiotic Earth might have crossed the threshold of life...The second question is how collections of molecules self-assemble into complex structures, and how secondary interactions between molecules and competition for molecular building blocks lead to complex behaviour within self-assembling systems. | ||
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* Alon U. (2003) [http://dx.doi.org/10.1126/science.1089072 Biological Networks: The Tinkerer as an Engineer.] Science'' 301:1866-1867. | * Alon U. (2003) [http://dx.doi.org/10.1126/science.1089072 Biological Networks: The Tinkerer as an Engineer.] Science'' 301:1866-1867. | ||
**'''Excerpt:''' This viewpoint [article] comments on recent advances in understanding the design principles of biological networks. It highlights the surprising discovery of "good-engineering" principles in biochemical circuitry that evolved by random tinkering. | **'''Excerpt:''' This viewpoint [article] comments on recent advances in understanding the design principles of biological networks. It highlights the surprising discovery of "good-engineering" principles in biochemical circuitry that evolved by random tinkering. | ||
* Barabási A-L, Albert R. (1999) [http://dx.doi.org/10.1126/science.286.5439.509 Emergence of Scaling in Random Networks.] ''Science'' 286:509-511. | * Barabási A-L, Albert R. (1999) [http://dx.doi.org/10.1126/science.286.5439.509 Emergence of Scaling in Random Networks.] ''Science'' 286:509-511. | ||
**'''Excerpt:''' Here we report on the existence of a high degree of self-organization characterizing the large-scale properties of complex networks. Exploring several large databases describing the topology of large networks that span fields as diverse as the WWW or citation patterns in science, we show that, independent of the system and the identity of its constituents, the probability P(k) that a vertex in the network interacts with k other vertices decays as a power law, following P(k) ~k-gamma<sup>- </sup>. This result indicates that large networks self-organize into a scale-free state, a feature unpredicted by all existing random network models. | **'''Excerpt:''' Here we report on the existence of a high degree of self-organization characterizing the large-scale properties of complex networks. Exploring several large databases describing the topology of large networks that span fields as diverse as the WWW or citation patterns in science, we show that, independent of the system and the identity of its constituents, the probability P(k) that a vertex in the network interacts with k other vertices decays as a power law, following P(k) ~k-gamma<sup>- </sup>. This result indicates that large networks self-organize into a scale-free state, a feature unpredicted by all existing random network models. | ||
Revision as of 20:41, 15 January 2010
- Please sort and annotate in a user-friendly manner. For formatting, consider using automated reference wikification.
Books
- Ross J, Schreiber I, Vlad MO; with contributions from Arkin A, Oefner PJ, Zamboni N. (2006) Determination of Complex Reaction Mechanisms: Analysis of Chemical, Biological, and Genetic Networks. Oxford University Press: New York. ISBN 978-0-19-517868-5.
- Excerpt: We have seen that computations can be achieved by chemical and biochemical reaction mechanisms, and have located computational functions in biological reaction systems. This identification helps in understanding functions and control in such systems. It also helps in suggesting new approaches to the determination of causal connectivities of reacting species, of reaction pathways, and reaction mechanisms, by exploring analogs of investigations in electronics, system analysis [citations here], multivariate statistics [citations here0], and other related disciplines. (From Chapter 4: Computations by Means of Macroscopic Chemical Kinetics.)
Book Chapters
Journal articles
- Nitschke JR. (2009) Systems chemistry: Molecular networks come of age. Nature 462:736-738.
- Excerpt: There are two main questions [systems chemists are asking]. The first is how the complex networks of molecules found on the prebiotic Earth might have crossed the threshold of life...The second question is how collections of molecules self-assemble into complex structures, and how secondary interactions between molecules and competition for molecular building blocks lead to complex behaviour within self-assembling systems.
- Bray D. (2003) Molecular Networks: The Top-Down View. Science 301:1864-1865.
- Excerpt: Everything in a living cell is, of course, connected to everything else, and interactions between macromolecules through multiple noncovalent bonds are the very fabric of life. It is therefore an attractive notion that, by taking a top-down view of protein-protein interactions, enzymatic pathways, signaling pathways, and gene regulatory pathways, we will gain a better perspective of how they work.
- Alon U. (2003) Biological Networks: The Tinkerer as an Engineer. Science 301:1866-1867.
- Excerpt: This viewpoint [article] comments on recent advances in understanding the design principles of biological networks. It highlights the surprising discovery of "good-engineering" principles in biochemical circuitry that evolved by random tinkering.
- Barabási A-L, Albert R. (1999) Emergence of Scaling in Random Networks. Science 286:509-511.
- Excerpt: Here we report on the existence of a high degree of self-organization characterizing the large-scale properties of complex networks. Exploring several large databases describing the topology of large networks that span fields as diverse as the WWW or citation patterns in science, we show that, independent of the system and the identity of its constituents, the probability P(k) that a vertex in the network interacts with k other vertices decays as a power law, following P(k) ~k-gamma- . This result indicates that large networks self-organize into a scale-free state, a feature unpredicted by all existing random network models.