Network biology - Revision history

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← Older revision		Revision as of 15:15, 30 May 2007
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	We can best represent and analyze the essence of biology’s layers as computable networks. For example, a protein is a network or graph of amino acids with nodes and edges. The nodes can be amino acids and the edges can be chemical forces. We can represent the amino acids (about 20 of them) as the networks of atoms of carbon, nitrogen, oxygen, and so on. This can go down as far as the boundary of matter and nonmatter or go up as far as (or beyond) two humans having a conversation. We can regard the conversation as information processing with a relatively precise syntax and a highly context-dependent grammar. This is essentially the same process as two proteins interacting probabilistically to produce some biological functions. Humans are not much more than huge protein complexes with the same essentiall function of information processing. In this regard, our cities such as Seoul and London are just anoter layer of complex biological information processing units. As early as the 1980s, researchers started viewing DNA or genomes as the dynamic storage of a language system with precise computable finite states (Searls, 1993). Recent complex-systems research has also suggested some far-reaching commonality in the organization of information in problems from biology, computer science, and physics, such as the Bose–Einstein condensate (a special state of matter, Bianconi and A.L. Barabási, 2001). A grand theory explaining very small and large systems can come from the computational mechanics applied to biological networks that encompass atoms, giant organisms, and even larger objects in a coherent information-processing scheme. However, only in the last five years has bioinformatics truly shifted its focus from individual genes, proteins, structures, and search algorithms to large-scale networks often denoted as -omes such as biome, interactiome, genome and proteome. Suddenly, biologists are finding the links between the Internet and metabolic pathways, structural interactions of proteins via a network topology or scale-free network (Jeong et al., 2000, see Figure 1). We are becoming more certain that biology’s future lies in networks of biological entities. <br />		We can best represent and analyze the essence of biology’s layers as computable networks. For example, a protein is a network or graph of amino acids with nodes and edges. The nodes can be amino acids and the edges can be chemical forces. We can represent the amino acids (about 20 of them) as the networks of atoms of carbon, nitrogen, oxygen, and so on. This can go down as far as the boundary of matter and nonmatter or go up as far as (or beyond) two humans having a conversation. We can regard the conversation as information processing with a relatively precise syntax and a highly context-dependent grammar. This is essentially the same process as two proteins interacting probabilistically to produce some biological functions. Humans are not much more than huge protein complexes with the same essentiall function of information processing. In this regard, our cities such as Seoul and London are just anoter layer of complex biological information processing units. As early as the 1980s, researchers started viewing DNA or genomes as the dynamic storage of a language system with precise computable finite states (Searls, 1993). Recent complex-systems research has also suggested some far-reaching commonality in the organization of information in problems from biology, computer science, and physics, such as the Bose–Einstein condensate (a special state of matter, Bianconi and A.L. Barabási, 2001). A grand theory explaining very small and large systems can come from the computational mechanics applied to biological networks that encompass atoms, giant organisms, and even larger objects in a coherent information-processing scheme. However, only in the last five years has bioinformatics truly shifted its focus from individual genes, proteins, structures, and search algorithms to large-scale networks often denoted as -omes such as biome, interactiome, genome and proteome. Suddenly, biologists are finding the links between the Internet and metabolic pathways, structural interactions of proteins via a network topology or scale-free network (Jeong et al., 2000, see Figure 1). We are becoming more certain that biology’s future lies in networks of biological entities. <br />
	<br />		<br />
		+	<strong><font size="4">External Links</font></strong><br />
		+	[http://networkome.org Networkome.org]<br />
		+	<br />
		+	<div><font size="5"><strong>References: </strong></font><font size="2"> </font></div>
		+	<div><br />
		+	1. D.B. Searls, “The Computational Linguistics of Biological Sequences,” <em>Artificial Intelligence and Molecular Biology</em>, L. Hunter, ed., MIT Press, Cambridge, Mass., 1993, pp. 47–120.</div>
		+	<div>    2. G. Bianconiand A.L. Barabási,“Bose-Einstein Condensation in Complex Networks,” <em>Physical Rev. Letters</em>, vol. 86, no. 24, June 2001, pp. 5632–5635.</div>
		+	<div>    3. H. Jeong et al., “The Large-Scale Organization of Metabolic Networks,” <em>Nature</em>, vol. 407, no. 6,804, 5 Oct. 2000, pp. 651–654; "Lethality and centrality in protein networks," <em>Nature</em>, vol. 411, no. 6,833, 3 May 2001, pp. 41-42.</div>
		+	<div>    <a title="http://stat.kaist.ac.kr" rel="nofollow" href="http://stat.kaist.ac.kr/"><font color="#3366bb">http://stat.kaist.ac.kr</font></a></div>
		+	<div>    4. T.D. Thiery and R. Thomas, “Qualitative Analysis of Gene Networks,” <em>Proc. Pacific Symp Biocomputing</em>, World Scientific, Singapore, 1998, pp. 77–88. </div>
		+	<div>    5. S. Tsoka and C.A. Ouzounis, “Recent Developments and Future Directions in Computational Genomics,” <em>FEBS Letters</em>, <strong>vol. </strong>480, no. 1, 25 Aug. 2000, pp. 42–48</div>
		+	<div>    6. A.J. Walhout, S.J. Boulton, and M. Vidal, “Yeast Two-Hybrid Systems and Protein Interaction Mapping Projects for Yeast and Worm,” <em>Yeast</em>, vol. 17, no. 2, June 2000, pp. 88–94.</div>
		+	<div>    7. P. Uetz et al., “A Comprehensive Analysis of Protein-Protein Interactions in Saccharomyces Cerevisiae,” <em>Nature</em>, vol. 403, no. 6,770, Feb. 2000, pp. 623–627.</div>
		+	<div>    8. J. Park, M. Lappe, and S.A. Teichmann, “Mapping Protein Family Interactions: Intramolecular and Intermolecular Protein Family Interaction Repertoires in the PDB and Yeast,” <em>J. Molecular Biology</em>, vol. 307, no. 3, Mar. 2001, pp. 929–938.<br />
		+	</div>
		+	<div>    <a title="http://biointeraction.net/" rel="nofollow" href="http://biointeraction.net/"><font color="#3366bb">http://biointeraction.net/</font></a><br />
		+	<br />
		+	<font size="2">(Vol. 17,   No. 3, pp66-80). <strong>2002,</strong> IEEE, Inteligent Systems</font></div>
		+	<div><br />
		+	<br />
		+	</div>

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-We can best represent and analyze the essence of biology’s layers as computable networks. For example, a protein is a network or graph of amino acids with nodes and edges. The nodes can be amino acids and the edges can be chemical forces. We can represent the amino acids (about 20 of them) as the networks of atoms of carbon, nitrogen, oxygen, and so on. This can go down as far as the boundary of matter and nonmatter or go up as far as (or beyond) two humans having a conversation. We can regard the conversation as information processing with a relatively precise syntax and a highly context-dependent grammar. This is essentially the same process as two proteins interacting probabilistically to produce some biological functions. Humans are not much more than huge protein complexes with the same essentiall function of information processing. In this regard, our cities such as Seoul and London are just anoter layer of complex biological information processing units.
+<strong>Network biology</strong> is a branch of biology where everything is regarded as nodes of networks and their interactions are analyzed as complex systems.<br />
+<br />
-As early as the 1980s, researchers started viewing DNA or genomes as the dynamic storage of a language system with precise computable finite states (Searls, 1993). Recent complex-systems research has also suggested some far-reaching commonality in the organization of information in problems from biology, computer science, and physics, such as the Bose–Einstein condensate (a special state of matter, Bianconi and A.L. Barabási, 2001). A grand theory explaining very small and large systems can come from the computational mechanics applied to biological networks that encompass atoms, giant organisms, and even larger objects in a coherent information-processing scheme.
+We can best represent and analyze the essence of biology&rsquo;s layers as computable networks. For example, a protein is a network or graph of amino acids with nodes and edges. The nodes can be amino acids and the edges can be chemical forces. We can represent the amino acids (about 20 of them) as the networks of atoms of carbon, nitrogen, oxygen, and so on. This can go down as far as the boundary of matter and nonmatter or go up as far as (or beyond) two humans having a conversation. We can regard the conversation as information processing with a relatively precise syntax and a highly context-dependent grammar. This is essentially the same process as two proteins interacting probabilistically to produce some biological functions. Humans are not much more than huge protein complexes with the same essentiall function of information processing. In this regard, our cities such as Seoul and London are just anoter layer of complex biological information processing units. As early as the 1980s, researchers started viewing DNA or genomes as the dynamic storage of a language system with precise computable finite states (Searls, 1993). Recent complex-systems research has also suggested some far-reaching commonality in the organization of information in problems from biology, computer science, and physics, such as the Bose&ndash;Einstein condensate (a special state of matter, Bianconi and A.L. Barab&aacute;si, 2001). A grand theory explaining very small and large systems can come from the computational mechanics applied to biological networks that encompass atoms, giant organisms, and even larger objects in a coherent information-processing scheme. However, only in the last five years has bioinformatics truly shifted its focus from individual genes, proteins, structures, and search algorithms to large-scale networks often denoted as -omes such as biome, interactiome, genome and proteome. Suddenly, biologists are finding the links between the Internet and metabolic pathways, structural interactions of proteins via a network topology or scale-free network (Jeong et al., 2000, see Figure 1). We are becoming more certain that biology&rsquo;s future lies in networks of biological entities.&nbsp;<br />
+<br />

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