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

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

The publication introduces to the reader a couple of leading edge statistical tools which could e used for the research of genomic, proteomic and metabolomic information units. specifically within the box of platforms biology, researchers are attempting to investigate as many information as attainable in a given organic procedure (such as a cellphone or an organ). the best statistical assessment of those huge scale info is necessary for the proper interpretation and varied experimental techniques require various techniques for the statistical research of those facts. This booklet is written via biostatisticians and mathematicians yet aimed as a precious consultant for the experimental researcher to boot computational biologists who frequently lack a suitable heritage in statistical research.

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Salvador. Ó 2011 Wiley-VCH Verlag GmbH & Co. KGaA. Published 2011 by Wiley-VCH Verlag GmbH & Co. KGaA. j 2 Stochastic Modeling of Gene Regulatory Networks 14 One of the major challenges in systems biology is the development of quantitative mathematical models for studying regulatory mechanisms in complex biological systems [27]. Although deterministic models have been widely used for analyzing gene regulatory networks, cell signaling pathways, and metabolic systems [28, 29], a deterministic model can only describe the averaged behavior of a system based on large populations, but cannot realize fluctuations of the system behavior in different cells.

Although these methods have many advantages, network biology still faces many challenges. Most of the methods rely on the quality of datasets, which determine the false-positives and limited coverage. Most edges in network maps are still lacking detailed attributes and directions. Post-transcriptional References modifications are hardly monitored at a large scale. Tissue- and cell-type specificities are hard to consider. However, with the development of new technologies, such as high-throughput and single-cell dynamic measurement techniques, and with increasing accuracy and coverage of high-throughput technologies, the ever-accelerating data acquisition will raise further need for data integration and modeling at the network level.

In the absence of MMC, all cells exhibited little or no Green Fluorescent Protein (GFP) expression (low LacR expression). Nearly all of the cells expressed GFP (high LacR expression) after treatment with 500 ng/ml MMC. 1 Genetic toggle switch interfaced with the SOS signaling pathway. 3 Discrete Stochastic Modeling intermediate MMC concentrations for the cell numbers with different GFP expression levels [69]. A deterministic model was proposed for studying the bistability properties of the genetic toggle switch [26, 69].

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