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.
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Seite 22
... coefficients of that organism. N represents all single-step catalytic capabilities of the organism. In the consensus reconstruction of. 1 PRINCIPLES BASED ON TOPOLOGY OF THE GENOME-WIDE METABOLIC NETWORK: LIMITED NUMBERS OF POSSIBLE FLUX ...
... coefficients of that organism. N represents all single-step catalytic capabilities of the organism. In the consensus reconstruction of. 1 PRINCIPLES BASED ON TOPOLOGY OF THE GENOME-WIDE METABOLIC NETWORK: LIMITED NUMBERS OF POSSIBLE FLUX ...
Seite 26
... coefficients defining the objective function Z in terms of a linear combination of the rates v. Depending on the specific information we want to retrieve through FBA, Z can also represent a non-biological criterion of optimality, as we ...
... coefficients defining the objective function Z in terms of a linear combination of the rates v. Depending on the specific information we want to retrieve through FBA, Z can also represent a non-biological criterion of optimality, as we ...
Seite 30
... coefficient (Burns et al. 1985), which is limited to the control of steady-state fluxes. Here we are more explicit about the fact that one may also define the flux control coefficient outside of steady state. This does require one to ...
... coefficient (Burns et al. 1985), which is limited to the control of steady-state fluxes. Here we are more explicit about the fact that one may also define the flux control coefficient outside of steady state. This does require one to ...
Seite 31
... coefficient of that rate will be virtually zero. As a consequence, the above law (Equation 3.8) predicts that all processes in the network together control the rate of phosphorylation of the target at a control coefficient of 1. However ...
... coefficient of that rate will be virtually zero. As a consequence, the above law (Equation 3.8) predicts that all processes in the network together control the rate of phosphorylation of the target at a control coefficient of 1. However ...
Seite 32
... coefficient is negative, implying that the sum of the control by all the kinases and the control by all the phosphatases must be (equally) negative. Since the phosphatases exercise negative control and the kinases positive control, this ...
... coefficient is negative, implying that the sum of the control by all the kinases and the control by all the phosphatases must be (equally) negative. Since the phosphatases exercise negative control and the kinases positive control, this ...
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 |
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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