Robustness as a measure of plausibility in models of biochemical networks.
about
Towards a unifying, systems biology understanding of large-scale cellular death and destruction caused by poorly liganded iron: Parkinson's, Huntington's, Alzheimer's, prions, bactericides, chemical toxicology and others as examplesA calculus of purposeQuantifying robustness of biochemical network models.Transition to quorum sensing in an Agrobacterium population: A stochastic modelModeling intracellular signaling underlying striatal function in health and diseaseIron behaving badly: inappropriate iron chelation as a major contributor to the aetiology of vascular and other progressive inflammatory and degenerative diseasesSegregation and crosstalk of D1 receptor-mediated activation of ERK in striatal medium spiny neurons upon acute administration of psychostimulantsIn vivo robustness analysis of cell division cycle genes in Saccharomyces cerevisiaeRobustness can evolve gradually in complex regulatory gene networks with varying topologyQuantifying global tolerance of biochemical systems: design implications for moiety-transfer cyclesExperimental assessment of the sensitiveness of an electrochemical oscillator towards chemical perturbationsWhat can we learn from global sensitivity analysis of biochemical systems?Systematically studying kinase inhibitor induced signaling network signatures by integrating both therapeutic and side effectsBiological robustness: paradigms, mechanisms, and systems principlesOverexpression limits of fission yeast cell-cycle regulators in vivo and in silico.Stochastic noise and synchronisation during dictyostelium aggregation make cAMP oscillations robust.Negative autoregulation by FAS mediates robust fetal erythropoiesisInput-output behavior of ErbB signaling pathways as revealed by a mass action model trained against dynamic dataCell lineages and the logic of proliferative control'Glocal' robustness analysis and model discrimination for circadian oscillators.Efficient estimation of the robustness region of biological models with oscillatory behavior.On the stability of metabolic cycles.The contributions of interlocking loops and extensive nonlinearity to the properties of circadian clock models.Consistent robustness analysis (CRA) identifies biologically relevant properties of regulatory network modelsCircuit topology and the evolution of robustness in two-gene circadian oscillators.Convergent transcription in the butyrolactone regulon in Streptomyces coelicolor confers a bistable genetic switch for antibiotic biosynthesis.Mathematical modeling of polyamine metabolism in mammals.Efficient characterization of high-dimensional parameter spaces for systems biology.Modeling cortisol dynamics in the neuro-endocrine axis distinguishes normal, depression, and post-traumatic stress disorder (PTSD) in humansGlobal analysis of dynamical decision-making models through local computation around the hidden saddle.Evolving sensitivity balances Boolean Networks.Understanding regulation of metabolism through feasibility analysis.Robustness and the cycle of phosphorylation and dephosphorylation in a two-component regulatory system.Finding novel pharmaceuticals in the systems biology era using multiple effective drug targets, phenotypic screening and knowledge of transporters: where drug discovery went wrong and how to fix it.Agent Based Modeling of Human Gut Microbiome Interactions and Perturbations.Structural kinetic modeling of metabolic networksAnalysis of the robustness of network-based disease-gene prioritization methods reveals redundancy in the human interactome and functional diversity of disease-genes.Formation of the BMP activity gradient in the Drosophila embryo.Conditional robustness analysis for fragility discovery and target identification in biochemical networks and in cancer systems biology.A challenge for 21st century molecular biology and biochemistry: what are the causes of obligate autotrophy and methanotrophy?
P2860
Q24289511-EA254CE1-0693-444B-8BBA-A3B42C19FA1BQ24793312-470164D6-A5B7-40DE-B8C4-B4E3C48CB92BQ24794241-220A45C3-1C64-4147-91BB-1D44A28E96DCQ24814326-1C3B28E2-44F2-4CAC-A5A7-98CADB870584Q26851389-B1F5AE50-66B9-4FAD-B05C-3CA70E51F0B2Q28388335-1B9F297A-5CBE-4BD2-B76B-F8D90E39C8D5Q28395155-5B48349C-AA60-4B91-8A7A-30F7F7F3B09AQ28469021-B2A4172C-BED4-440B-92E6-93ABD5628549Q28469133-E96E1C88-A4CF-45EB-84B4-E62BF06FFAAAQ28474957-C9649AC4-76F4-490A-9C0A-09961FD928FEQ28485297-24E8B1AD-E061-41EA-B42C-B28502568F7EQ28535142-A6D64C93-3A31-46DB-8722-51374B83F962Q28536170-EC820FB7-FA10-4E75-99B5-A01BA8ED9153Q28730028-3F9755C7-786C-4CAA-8AA6-76242D1F9F27Q30542457-6F5D83D0-509C-4721-B7B0-66CA3AEBAEBEQ30837906-E807E4A2-4532-455F-87FF-E2BAE053F830Q33300418-2C34CACC-14D4-4FF8-962E-D1335C5B82D6Q33401214-18231828-AE24-4B08-99E1-3CCBE1B5C86AQ33402595-C5F7681F-1453-439A-93E3-D2052B03038AQ33510924-AA06CB9C-39EA-4391-B062-75A6FB87F402Q33549681-34C2B9B7-985A-4E6E-B7D9-6772C2EAF580Q33685840-D180C594-AFB4-4281-9C85-5E586DEF88C3Q33770240-4372278F-49E7-4E44-83EE-7078B8B27892Q33778726-9AE571B1-3356-4FEA-9506-30F8B5B78DDBQ33913632-A114DA89-7726-4C8E-B6B1-C57B69534957Q33963878-C15DB3F4-334B-4AA9-ADCD-296319913B96Q33995976-FEA9BEA2-0322-41D1-9AB3-08622030215DQ34020133-86A6FDEC-87DD-41A5-B160-5B81FD7328A7Q34167844-62199904-7189-4FBE-80D9-A31A433BBA21Q34206126-A4DAAB2A-038A-4F08-9D12-F8B7A0653324Q34269054-F2673244-313B-49C3-ACA0-B92DCDBC293CQ34341551-F53BEE0F-4874-45FA-8D62-46CC8B70CD35Q34470540-9A59EE37-E47B-4A17-9E90-95F42A4367B1Q34646772-87309C54-0DB9-47B2-99DA-EA459341A99AQ34675803-CF9C2275-154F-490E-A823-3D03C0BF2416Q34882641-83025C00-454C-49B5-B772-46FEAB0FFAD3Q35147851-29CA4934-7F00-4CC5-8842-47E407C6247EQ35690799-C3C02504-8F63-4D5F-A65C-25DBE4088DE1Q35813685-0E58F1C0-824B-433F-BDFB-00D2E5CD93B3Q35902251-A4DBDB56-023C-4E32-BEE4-DF230859BA36
P2860
Robustness as a measure of plausibility in models of biochemical networks.
description
2002 nî lūn-bûn
@nan
2002年の論文
@ja
2002年学术文章
@wuu
2002年学术文章
@zh-cn
2002年学术文章
@zh-hans
2002年学术文章
@zh-my
2002年学术文章
@zh-sg
2002年學術文章
@yue
2002年學術文章
@zh
2002年學術文章
@zh-hant
name
Robustness as a measure of plausibility in models of biochemical networks.
@en
Robustness as a measure of plausibility in models of biochemical networks.
@nl
type
label
Robustness as a measure of plausibility in models of biochemical networks.
@en
Robustness as a measure of plausibility in models of biochemical networks.
@nl
prefLabel
Robustness as a measure of plausibility in models of biochemical networks.
@en
Robustness as a measure of plausibility in models of biochemical networks.
@nl
P2093
P356
P1476
Robustness as a measure of plausibility in models of biochemical networks.
@en
P2093
Amanda E Winn
Hamid Bolouri
Hiroaki Kitano
John Doyle
Mark T Borisuk
Mineo Morohashi
P356
10.1006/JTBI.2002.2537
P407
P577
2002-05-01T00:00:00Z