Download e-book for iPad: Applied Statistics for Network Biology: Methods in Systems by Matthias Dehmer, Frank Emmert-Streib, Armin Graber, Armindo

By Matthias Dehmer, Frank Emmert-Streib, Armin Graber, Armindo Salvador

ISBN-10: 3527327509

ISBN-13: 9783527327508

This ebook introduces a couple of leading edge statistical equipment which might be used for the research of genomic, proteomic and metabolomic information units. particularly within the box of structures biology, researchers are attempting to research as much information as attainable in a given organic approach (such as a mobilephone or an organ). the best statistical evaluate of those huge scale facts is necessary for the proper interpretation and diversified experimental techniques require assorted ways for the statistical research of those facts. This ebook is written via biostatisticians and mathematicians yet aimed toward experimental researcher in addition to computational biologists who frequently lack a suitable heritage in statistical research.

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Additional info for Applied Statistics for Network Biology: Methods in Systems Biology

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A complete list of the possible binding configurations and the corresponding free energy of these configurations can be found in [85, 88]. Kinetic models with ODEs have been developed for describing the principle of the l right operator control system [81–85]: dx ¼ Sx Px ðx; yÞÀkdx x dt ð2:12Þ dy ¼ Sy Py ðx; yÞÀkdy y dt with: Px ðx; yÞ ¼ aðf31 þ f32 þ f33 Þ þ 40 X fm m¼34 Py ðx; yÞ ¼ f28 þ f29 þ f30 þ f40 ð2:13Þ j27 j 2 Stochastic Modeling of Gene Regulatory Networks 28 fs ¼ Ks ðKr x 2 ÞiðsÞ ðKc y2 ÞjðsÞ ½RNAPŠkðsÞ 40 P Km ðKr x2 ÞiðmÞ ðKc y2 ÞjðmÞ ½RNAPŠkðmÞ m¼1 where x and y are the concentrations of repressor and cro, respectively, Sx and Sy are the synthesis rates of repressor and cro, respectively, kdx and kdy are the degradation rates for repressor and cro, respectively, fs is defined by the sth configuration, Ks ¼ expðDGs =RTÞ is the equilibrium constant with the free energy DGs, and a ¼ 11 is the indication of positive feedback of the repressor.

4 Hierarchical Structure In signal transduction networks or transcriptional regulatory networks, genes can be divided into several layers and the signals flow from top to bottom (with feedback allowed). This kind of structure is called a hierarchical structure. Apart from the degree and network motifs, genes on different layers or having different offspring nodes (regulated by this gene) could provide information on understanding biological processes [16]. These network topology-based analyses have been widely used in identifying important genes in multiple studies of different species.

This technique is also consistent with other stochastic modeling approaches where Poisson random variables have been used for realizing the chance events in transcription and translation [68]. 1 Stochastic Modeling Method We first use a simple system to illustrate the relationship between a stochastic model, simulated by the Poisson t-leap method, with the corresponding deterministic ODE model simulated by the Euler method. This system includes two reactions: 1 R1 : S1 þ S2 Àc! S3 2 R2 : S3 Àc! 6).

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Applied Statistics for Network Biology: Methods in Systems Biology by Matthias Dehmer, Frank Emmert-Streib, Armin Graber, Armindo Salvador


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