Applied Statistics for Network Biology by Dehmer M., et al. (eds.)

By Dehmer M., et al. (eds.)

The ebook introduces to the reader a couple of innovative statistical tools which could e used for the research of genomic, proteomic and metabolomic information units. particularly within the box of structures biology, researchers are attempting to research as many information as attainable in a given organic method (such as a mobile or an organ). the suitable statistical review of those huge scale information is important for the proper interpretation and diversified experimental techniques require varied techniques for the statistical research of those information. This booklet is written via biostatisticians and mathematicians yet aimed as a worthwhile consultant for the experimental researcher besides computational biologists who frequently lack a suitable heritage in statistical research.

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XN ÞÀgi ð x 1 ; . . ; xN Þ; ¼ fi ð dt i ¼ 1; . . ; N where fi ð x 1 ; . . ; xN Þ and gi ð x 1 ; . . ; xN Þ represent the increase and decrease processes in the value xi of species Si , respectively. Here, xi normally represents the concentration of species Si , whereas in stochastic models we use xi to represent the molecular number of species Si . It is assumed that the increase and decrease of the molecular number xi in a time interval ½t; t þ tÞ are samples of the Poisson random variables with mean fi ðx1 ; .

2 Toggle Switch with the SOS Pathway The genetic toggle switch, which is the first engineered switching network implemented on plasmids in Escherichia coli [26, 69] and in mammalian cells [70], is a robust bistable system comprised of two genes and regulated by a double-negative feedback loop. 1, this system consists of two genes, lacI and l cI, that encode the transcriptional regulator proteins, LacR and l CI, respectively [26, 69]. When E. coli cells are exposed to various concentrations of mitomycin C (MMC), the application of MMC causes DNA damage that leads to the activation of protein RecA.

1. 1 Direct Method [39] Step 1: Calculate the values of propensity functions aj ðxÞ based on the system state P x at time t and a0 ðxÞ ¼ M j¼1 aj ðxÞ. Step 2: Generate a sample r1 of the uniformly distributed random variable Uð0; 1Þ and determine the time of the next reaction: m¼   1 1 ln a0 ðxÞ r1 Step 3: Generate an independent sample r2 of Uð0; 1Þ to determine the index k of the next reaction occurring in ½t; t þ mÞ: kÀ1 X aj ðxÞ < r2 a0 ðxÞ j¼1 k X aj ðxÞ j¼1 Step 4: Update the state of the system by: xðt þ mÞ ¼ xðtÞ þ nk Step 5: Go to Step (1) if t þ m system state xðTÞ ¼ xðtÞ.

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