Computational Systems Biology: From Molecular Mechanisms to DiseaseAndres Kriete, Roland Eils Academic Press, 26.11.2013 - 548 Seiten This comprehensively revised second edition of Computational Systems Biology discusses the experimental and theoretical foundations of the function of biological systems at the molecular, cellular or organismal level over temporal and spatial scales, as systems biology advances to provide clinical solutions to complex medical problems. In particular the work focuses on the engineering of biological systems and network modeling.
|
Im Buch
Ergebnisse 1-5 von 52
Seite 91
... graph (DAG), denoted by G, as a relationship among random variables. In the gene network estimation based on Bayesian networks, a gene is regarded as a random variable and shown as a node. Let Xi(i = 1,...,p) be a discrete random ...
... graph (DAG), denoted by G, as a relationship among random variables. In the gene network estimation based on Bayesian networks, a gene is regarded as a random variable and shown as a node. Let Xi(i = 1,...,p) be a discrete random ...
Seite 92
... graph selection criterion in Section 2.1.3. Since gene expression data take continuous variables, some discretization methods are required for using the Bayesian networks based on the discrete random variables described above. However ...
... graph selection criterion in Section 2.1.3. Since gene expression data take continuous variables, some discretization methods are required for using the Bayesian networks based on the discrete random variables described above. However ...
Seite 93
... graph, which gives the best approximation of the system underlying the data. We construct a criterion for evaluating a graph based on our model from Bayes approach, that is the maximization of the posterior probability of the graph. The ...
... graph, which gives the best approximation of the system underlying the data. We construct a criterion for evaluating a graph based on our model from Bayes approach, that is the maximization of the posterior probability of the graph. The ...
Seite 94
... graph is the computation of the high-dimensional integration in Eq. (6.5). For log p(θ|λ,G) = O(n), the Laplace ... graph: (6.7) BNRC(G) = −2 log P(G) − rlog(2π/n) + log |Jλ (ˆθ|Xn)| − 2nlλ (ˆθ|Xn) The optimal graph ˆGis chosen such ...
... graph is the computation of the high-dimensional integration in Eq. (6.5). For log p(θ|λ,G) = O(n), the Laplace ... graph: (6.7) BNRC(G) = −2 log P(G) − rlog(2π/n) + log |Jλ (ˆθ|Xn)| − 2nlλ (ˆθ|Xn) The optimal graph ˆGis chosen such ...
Seite 95
... graph, and the marginal likelihood of the data p(Xn|G). The marginal likelihood shows the fitness of the model to the gene expression data. The biological knowledge can then be used as the prior probability of the graph. Suppose that ...
... graph, and the marginal likelihood of the data p(Xn|G). The marginal likelihood shows the fitness of the model to the gene expression data. The biological knowledge can then be used as the prior probability of the graph. Suppose that ...
Inhalt
1 | |
9 | |
21 | |
45 | |
65 | |
89 | |
7 Reconstruction of Metabolic Network from Genome Information and its Structural and Functional Analysis | 113 |
8 Standards Platforms and Applications | 133 |
From Network Structure to Attractor Landscapes Landscape | 241 |
From Single Cells to Colonies | 277 |
14 Advances in Machine Learning for Processing and Comparison of Metagenomic Data | 295 |
15 Systems Biology of Infectious Diseases and Vaccines | 331 |
16 Computational Modeling and Simulation of Animal Early Embryogenesis with the MecaGen Platform | 359 |
17 Developing a Systems Biology of Aging | 407 |
18 Molecular Correlates of Morphometric Subtypes in Glioblastoma Multiforme | 423 |
Mathematical Models of Apoptosis | 455 |
9 Databases Standards and Modeling Platforms for Systems Biology | 169 |
Deterministic versus Stochastic Approaches | 183 |
11 TopDown Dynamical Modeling of Molecular Regulatory Networks | 223 |
Author Index | 483 |
Subject Index | 525 |
Andere Ausgaben - Alle anzeigen
Computational Systems Biology: From Molecular Mechanisms to Disease Andres Kriete,Roland Eils Keine Leseprobe verfügbar - 2013 |
Häufige Begriffe und Wortgruppen
activation algorithm analysis annotation apoptosis approach attractor Bayesian networks behavior binding biochemical Bioinformatics Biol Boolean network cancer caspase caspase-8 cell types CellML cellular circadian clock circadian oscillations circadian rhythms coefficient complex concentration corresponding cycle database described deterministic differentiation discrete domain Drosophila dynamics embryo enzyme equations experimental feedback Figure flux gene expression gene networks gene regulatory networks genetic genome Goldbeter graph identify immune integration interactions intracellular k-mers KEGG kinase kinetic Leloup ligand mammalian mathematical models mechanisms membrane metabolic network metabolites metagenomic methods microarray model for circadian modules molecular molecules mRNA network model nodes optimal organisms parameter perturbation phosphorylation predict protein proteomics quantitative reactions receptor regulation represent response robustness samples SBML sequences signal transduction signaling networks signaling pathways simulation specific stochastic structure tion tissue transcription transition vaccine variables XIAP