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Ghim, Cheol-Min
Physical Biology & Biological Physics
Research Interests
  • Control of complex networks, Stochastic fluctuations in a cell, Biochemical information processing, Human microbiome, Ecological inference, Nonequilibrium active system

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Systems biology of microbial communities

DC Field Value Language
dc.contributor.author Navid, A. ko
dc.contributor.author Ghim, Cheol-Min ko
dc.contributor.author Fenley, A.T. ko
dc.contributor.author Yoon, S. ko
dc.contributor.author Lee, S. ko
dc.contributor.author Almaas, E. ko
dc.date.available 2014-10-13T01:26:55Z -
dc.date.created 2014-10-10 ko
dc.date.issued 2009-01 ko
dc.identifier.citation METHODS IN MOLECULAR BIOLOGY, v.500, no.1, pp.469 - 494 ko
dc.identifier.issn 1064-3745 ko
dc.identifier.uri https://scholarworks.unist.ac.kr/handle/201301/7094 -
dc.description.abstract Microbes exist naturally in a wide range of environments in communities where their interactions are significant, spanning the extremes of high acidity and high temperature environments to soil and the ocean. We present a practical discussion of three different approaches for modeling microbial communities: rate equations, individual-based modeling, and population dynamics. We illustrate the approaches with detailed examples. Each approach is best fit to different levels of system representation, and they have different needs for detailed biological input. Thus, this set of approaches is able to address the operation and function of microbial communities on a wide range of organizational levels. ko
dc.description.statementofresponsibility open -
dc.language 영어 ko
dc.publisher Humana Press, Inc. ko
dc.title Systems biology of microbial communities ko
dc.type ARTICLE ko
dc.identifier.scopusid 2-s2.0-65649137349 ko
dc.type.rims ART ko
dc.description.scopustc 2 *
dc.date.scptcdate 2014-10-10 *
dc.identifier.doi 10.1007/978-1-59745-525-1_16 ko
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