By Matthias Dehmer, Frank Emmert-Streib, Armin Graber, Armindo Salvador
This ebook introduces a few leading edge statistical tools which are used for the research of genomic, proteomic and metabolomic facts units. particularly within the box of platforms biology, researchers try to investigate as much info as attainable in a given organic process (such as a mobilephone or an organ). the ideal statistical evaluate of those huge scale info is important for the proper interpretation and varied experimental techniques require diverse ways for the statistical research of those information. This ebook is written by way of biostatisticians and mathematicians yet aimed toward experimental researcher in addition to computational biologists who frequently lack a suitable historical past in statistical research.
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Extra resources for Applied Statistics for Network Biology: Methods in Systems Biology
2009) Bridging highthroughput genetic and transcriptional data reveals cellular responses to alphasynuclein toxicity. Nat. , 41, 316–323. D. (2010) Evaluating diabetes and hypertension disease causality using mouse phenotypes. BMC Syst. , 4, 97. 1 Introduction Recent studies through biological experiments have indicated that noise plays a very important role in determining the dynamic behavior of biological systems. Since the research work on stochastic modeling of the regulatory network of l phage [1, 2], there have been an increasing number of studies in the last decade investigating the origins of noise in biological networks and its crucial role in determining the key properties of biological networks [3–5].
Different partitioning techniques and different simulation methods have led to a number of effective methods and software for simulating chemical reaction systems [64–66]. 3 Discrete Stochastic Modeling Due to the lack of detailed knowledge of biochemical reactions, kinetic rates, and molecular numbers, stochastic models based on elementary chemical reactions may not always be the practical method to study chemical reaction systems. This section discusses a general approach to develop stochastic models based on widely used deterministic ODE models .
Sci. USA, 101, 4781–4786. Z. (2006) The role of microRNA-1 and microRNA-133 in skeletal muscle proliferation and differentiation. Nat. , 38, 228–233. E. (2005) Cell fates as highdimensional attractor states of a complex gene regulatory network. Phys. Rev. , 94, 128701. D. (2008) Understanding biological functions through molecular networks. , 18, 224–237. N. (2001) Lethality and centrality in protein networks. Nature, 411, 41–42. H. (2007) Functional centrality: detecting lethality of 10 11 12 13 14 15 16 proteins in protein interaction networks.