Computational Systems Biology: From Molecular Mechanisms to DiseaseThis 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 30
The definition of the corresponding flux control coefficient reads as: (3.7) C ej v i (t
) def= ∂lnvi ∂lnej = ( ∂lnvi(t) ∂pi ) in the system ( ∂lnvj(t=0)∂pj ) in a constant
molecular environment This definition differs somewhat from the standard ...
The definition of the corresponding flux control coefficient reads as: (3.7) C ej v i (t
) def= ∂lnvi ∂lnej = ( ∂lnvi(t) ∂pi ) in the system ( ∂lnvj(t=0)∂pj ) in a constant
molecular environment This definition differs somewhat from the standard ...
Seite 31
For a network with n processes, Westerhoff (2008) has proven the general
property or “law”: (3.8) Cviej(t) = 1 + Cv i t (t) n∑ j=1 The right-hand side is the
flux control coefficient of time defined by: (3.9) Cvit(t)def= ( ∂lnvi∂lnt ) in the
system It ...
For a network with n processes, Westerhoff (2008) has proven the general
property or “law”: (3.8) Cviej(t) = 1 + Cv i t (t) n∑ j=1 The right-hand side is the
flux control coefficient of time defined by: (3.9) Cvit(t)def= ( ∂lnvi∂lnt ) in the
system It ...
Seite 34
Here the hierarchical regulation coefficient ρih comprises both gene-expression
and signaltransduction regulation: def= (3.16) ρih ρig + ρis The proof is as follows
: Consider a regulation that results in a change in flux through the enzyme, dlnvi.
Here the hierarchical regulation coefficient ρih comprises both gene-expression
and signaltransduction regulation: def= (3.16) ρih ρig + ρis The proof is as follows
: Consider a regulation that results in a change in flux through the enzyme, dlnvi.
Seite 35
This makes the hierarchical regulation coefficient of that enzyme equal 3 (
Equation (15)), i.e. the cell will have to increase the ... In this example, the
metabolic regulation coefficients of enzymes 2 and 3 are 1, while their
hierarchical regulation ...
This makes the hierarchical regulation coefficient of that enzyme equal 3 (
Equation (15)), i.e. the cell will have to increase the ... In this example, the
metabolic regulation coefficients of enzymes 2 and 3 are 1, while their
hierarchical regulation ...
Seite 37
We computed the time-control coefficient Tc at those times, and this amounted to
0.87 and −4.57, respectively. We also computed the robustness R (defined as
the inverse of the time-control coefficients) of the amplitude of Clb5/6 at the two ...
We computed the time-control coefficient Tc at those times, and this amounted to
0.87 and −4.57, respectively. We also computed the robustness R (defined as
the inverse of the time-control coefficients) of the amplitude of Clb5/6 at the two ...
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Inhalt
| 1 | |
| 9 | |
| 21 | |
| 45 | |
5 Complexities in Quantitative Systems Analysis of Signaling Networks | 65 |
Estimation Modeling and Simulation | 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
Bibliografische Informationen
| Titel | Computational Systems Biology: From Molecular Mechanisms to Disease |
| Herausgeber | Andres Kriete, Roland Eils |
| Ausgabe | 2, überarbeitet |
| Verlag | Academic Press, 2013 |
| ISBN | 0124059384, 9780124059382 |
| Länge | 548 Seiten |
| Zitat exportieren | BiBTeX EndNote RefMan |